esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experiments and Analysis
### Imaging a Loaded Power-Line Cable  
In this experiment, we measured the magnetic field induced by a current in a loaded 50 Hz power-line cable. We used a single-wire cable that was bent into an L-shape and placed around 3 cm above the large sensor array. The flux density around the cable was proportional to the current \(I\) and approximately inversely proportional to the distance between the wire and the sensor. Figure 6a shows the experiment setup. Since the array was planar, the field was stronger at points close to the cable and weaker at the top-left and bottom-right of the area.  
The sampling rate of the magnetometer was set to 150 Sa/s for all field components. Figure 6b shows the spectrum of 1 s of measured \(B_{z}\) data given by the sensor located below the cable's bend point. The spectrum, estimated using a FFT (fast Fourier transform),  
Figure 5: Experiments with a large array showed that the system could be used to roughly image more complex objects. Top: The experiment result with an iron clamp placed 3 cm from the sensors. Bottom: Experiment with an X-shaped stainless steel lug wrench hidden behind a 5 cm-thick stack of papers.  
showed a peak at 50 Hz. The frequency resolution was 1 Hz, which was due to the 1 s observation time. If necessary, one can increase the frequency resolution by using longer data measurements. It can be seen from the spectrum in Figure 6b that the measured peak frequency was slightly less than 50 Hz (which is common in 50 Hz power lines). In fact, if we used a 15 s measurement, the peak would be located at 49.917 Hz.  
Figure 6c-f shows the norm of the three-directional magnitude of 50 Hz components of magnetic flux density at each sensor for four values of current; i.e., \(I_{\rm rms}=12.6\) mA, \(I_{\rm rms}=68.3\) mA, \(I_{\rm rms}=355.5\) mA and \(I_{\rm rms}=1.037\) A. The figures show that the measured field increased with the increasing current and that it was stronger near the cable and weaker at the two corners as expected.
## 2 System Models and Definitions  
### Sporadic Task Systems  
Let \(\tau_{i}\) denote sporadic real-time tasks. The execution time of \(\tau_{i}\) on the slowest processor is denoted as \(E_{i}\). The relative deadline of \(\tau_{i}\) is denoted as \(D_{i}\). The minimum interval time between two task requests is denoted as \(T_{i}\). Let the set of \(n\) tasks be denoted as \(\Lambda=(\tau_{1},\tau_{2},\tau_{3},\ldots,\tau_{n})\). If the task satisfies the conditions of \(E_{i}\leq D_{i}\leq T_{i}\), then \(\Lambda\) is considered a restricted set. The \(j\)_th_ job of task \(\tau_{i}\) is denoted as \(\tau_{i,j}\), and its arrival time is denoted as \(A_{i,j}\).  
Note that an understanding of the relationship between the execution time and processor speed is critical. The worst execution time \(E_{i}\) is the execution time required by \(\tau_{i}\) on the processor with speed \(s_{1}\), which is a fixed value. Since we conduct research on performance asymmetric multiprocessor platforms, different processors do not have the same speed. Processors with different speeds perform differently at the same time. Therefore, the actual execution time required by \(\tau_{i}\) on different processors is not the same. In the following, we need to take into account the effect of processor speed when calculating the execution time.  
The processor utilization of task \(\tau_{i}\) is defined as \(U_{i}=E_{i}/T_{i}\). \(U_{\max}(\Lambda)\) represents the maximum utilization among all tasks:  
\[U_{\max}(\Lambda){=}\max_{\tau_{i}\in\Lambda}(U_{i}). \tag{1}\]  
The density of task \(\tau_{i}\) is defined as \(\delta_{i}=E_{i}/D_{i}\). \(\delta_{\max}(\Lambda)\) represents the maximum density among all tasks:  
\[\delta_{\max}(\Lambda){=}\max_{\tau_{i}\in\Lambda}(\delta_{i}). \tag{2}\]  
\(\mathrm{DBF}(\tau_{i},\Delta t)\) is used to represent the upper limit of the maximum cumulative processing time required for task \(\tau_{i}\)'s job to arrive at and within a time interval of length \(\Delta t\). Among them, the size of \(\Delta t\) can be arbitrary. This has been proven by [PERSON] [11]:  
\[\mathrm{DBF}(\tau_{i},\Delta t)\stackrel{{\mathrm{def}}}{{=}} \max\left(0,\left(\left\lfloor\frac{\Delta t-D_{i}}{T_{i}}\right\rfloor+1 \right)E_{i}\right). \tag{3}\]  
The load parameters based on the DBF function are defined as follows [12]:  
\[\mathrm{LOAD}(\Lambda)\stackrel{{\mathrm{def}}}{{=}}\max_{ \Delta t>0}\left(\frac{\sum_{\tau_{i}\in\Lambda}\mathrm{DBF}(\tau_{i},\Delta t )}{\Delta t}\right). \tag{4}\]
## 2 Methodology
### A Concrete Temperature Prediction Model Based on SSA-ISVR  
#### 2.2.1 Feature Selection for Model Inputs  
The basis of machine learning predictive models is to construct a system that does not require explicit human-provided features and rules but instead predicts unknown data by learning hidden patterns and rules within the data [29]. Unlike statistical predictive models, machine learning models focus on the data input into the model and the model's hyperparameters, requiring little to no feature engineering to uncover the relationships between features and targets. Although these relationships often cannot be quantitatively characterized, their accuracy is well recognized.  
According to Section 2, the temperature evolution of the blocks is the result of heat generation and transfer. Factors such as the heat of cement hydration, ambient air temperature, water pipe cooling, heat dissipation conditions, and construction practices all influence the block temperature. In summary, this study models the temperature variation process in the casting blocks of a concrete arch dam using a machine learning algorithm (SSA-ISVR),  
Figure 2: Temperature collection within pouring blocks.  
taking into account the multiple influencing factors. Many factors have been validated to exhibit strong correlations with concrete temperature according to long-term research results in the field of concrete temperature control by the authors' team [2; 5; 10; 12; 17; 18; 19; 20; 30], primarily including  
\(T=\{\)Concrete age (\(A_{t}\), \(t\)), initial temperature (\(T_{0}\), \(t\)), ambient temperature (\(T_{a}\), \(t\)), cooling inlet water temperature (\(T_{w}\), \(t\)), cooling water flow rate (\(F_{w}\), \(t\)), heat dissipation area of the block (\(C_{a}\), \(t\)), concrete grade (\(L_{c}\), \(t\)), height of the block (\(H_{c}\), \(t\)), and intermittent time of the block (\(R_{c}\), \(t\)).  
This section may be divided by subheadings. It should provide a concise and precise description of the experimental results, their interpretation, as well as the experimental conclusions that can be drawn.
# Sensitivity of Cell Cultures on Time-Varying Low-Frequency Magnetic Field Changes
## 2 Materials and Methodology
### Experiment Conditions
#### 2.4.1 Resonance Point Verification, Series 1 of the Experiments  
As mentioned, experimental studies based on IPR theory assume a narrow width of the peak of the resonant response characteristic of the biosystem. Therefore, this assumption was verified in this series of experiments. The estimated resonance frequency of calcium ions \({}^{40}\)Ca\({}^{2+}\) is 29.89 Hz; increasing or decreasing the stated frequency value shows the decreasing response of the biological system. The range of \(\pm\)5 Hz is chosen as the interval, and then this interval is divided in half and again in half--in this way, the ten values of the frequencies at which the experiments are performed are determined: 24.89; 27.39; 28.64; 29.26; 29.57; 29.89; 30.20; 30.51; 31.14; 32.39; 34.89 Hz. The \(B_{\mathrm{AC}}/B_{\mathrm{DC}}\) ratio is kept at the level of the expected maximum biological effect at 1.8. This value corresponds to the \(B_{\mathrm{AC}}\) value of 70.2 \(\upmu\)T and a current of 79.5 mA. The response of the biological system is determined based on the parameter \(S\) according to the Formula (2).  
The reasoning behind using such a narrow frequency range (29.89 \(\pm\) 5 Hz) with the described step is in studying the resonant nature via increased sensitivity towards the assumed bioactive frequency. This would provide comparable results with the study [18] and enhance the understanding regarding the behavior of biological samples as a response to even small changes in the frequency of applied time-varying ELF MF.
## 2 System Model  
In this section, the measurement model of the GNSS positioning signal is described, followed by a measurement model of the 5G positioning signal, a clock model of the positioning terminal, and finally, a measurement model of the GNSS and 5G fusion positioning.
###### Abstract  
This paper focuses on the topic of joint communication and radar sensing (JCRS) and its applications in the scope of upcoming sixth-generation (6G) technology. While the fifth-generation applications have reached the consumer market in the last few years, JCRS has been identified as one of the key technologies for next generation networks. The role of JCRS will vary, ranging from tasks such as radar coordination, context awareness for communication, enhanced security, increased availability, and improving the resilience and trustworthiness of future networks. In this work, the niche of JCRS technology in the future 6G ecosystem, as well as several potential applications, are discussed with a focus on RF hardware. The use of centimeter (cmWave) and millimeter (mmWave) frequency spectrums in the context of JCRS system implementation have been further elaborated. After presenting the near-term application scenarios, the circuit implementation perspectives are investigated in terms of radio frequency (RF) front-end architectures, antenna implementation, and phased arrays. Different communication and radar antenna options are compared, and the best candidates are identified. The packaging options are also presented. From circuit and system perspectives, link budget and self-interference cancellation (SIC) are highlighted. Furthermore, future directions including the next steps on the path to enabling JCRS technology are presented throughout this article. Prior works focused more on physical layers and network capabilities of JCRS systems, with less focus on hardware possibilities; to fill this gap, this article aims to contribute to this exciting research topic with a holistic review of RF hardware, highlighting the diversity of applications and the available technologies to tackle the near- and long-term needs of consumer applications.  
communication; radar; joint communication and radar sensing; RF; antenna; 6G; front-end; mmWave; hardware
# Named Entity Recognition Using Conditional Random Fields
## 4 Urdu NER Using Condition Random Fields
### Conditional Random Field  
A CRF is a probabilistic model that is widely acknowledged as a useful technique for the segmentation and labeling of several natural language processing tasks. As the name indicates, a CRF supports a conditional approach and it structurally follows the characteristics of an undirected graphical model, where the nodes of a CRF graph represent class label sequences, which in our case are named entity types, such as a person, organization, and location. A CRF classifier deduces tag sequences \(T\) of tags, where \(T=t_{1}\), \(t_{2}\),... \(t_{n}\), which effectively encapsulates the conceptual nature of an observed pattern \(x\), where \(x\) denotes a random observation unit taken from a sentence. The CRF model involves picking the y that gives the maximum dependent likelihood \(P(y|x)\). Formally, it is defined in Equation (1).  
\[P(y|x)=\frac{1}{Z(x)}\times exp(\sum_{t}\sum_{k}\lambda_{k}.f_{k}(y_{t-1},y_{t},x)) \tag{1}\]  
In this equation, \(\lambda_{k}\) symbolizes the weight values attributed to the various features during the learning phase. \(f_{k}(y_{t-1},y_{t},x)\) is a feature function whose value could be either 0 or 1 in the form of binary-valued features. \(Z(x)\) is a normalization element that is defined in Equation (2).  
\[Z(x)=\sum_{y\in Y}exp\sum_{t}\sum_{k}\lambda_{k}.f_{k}(y_{t-1},y_{t},x)) \tag{2}\]  
When using a CRF for Urdu NER tasks, \(x\) denotes the sequence of tokens in a line of text and \(y\) denotes the phrase's associated tag sequence.
# Feature Selection and Transfer Learning for Alzheimer's Disease Clinical Diagnosis
## 4 Experiments
### Feature Selection Results
\begin{table}
\begin{tabular}{c c c} \hline \hline
**AD vs. NC** & \multicolumn{2}{c}{**MCI vs. NC**} \\ \hline
**Amygdala\_L, Amygdala** & 0.259 & **Hippocampus\_R, Hippocamp** & 0.045 \\
**Inferior temporal gyrus** & 0.237 & Heschl\_L, Heschl gyrus & 0.042 \\ Temporal\_Inf\_L, Inferior temporal gyrus & 0.220 & Caudate\_R, Caudate nucleus & 0.039 \\
**Amygdala\_R, Amygdala** & 0.217 & Frontal\_Inf\_Oper\_R, Inferior frontal gyrus & 0.038 \\ Temporal\_Pole\_Mid\_L, middle temporal gyrus & 0.215 & SupraMarginal\_R, Supramarginal gyrus & 0.034 \\ Frontal\_Mid\_Or\_L, Middle frontal gyrus & 0.214 & Amygdala\_L, Amygdala & 0.033 \\ Temporal\_Mid\_L, Middle temporal gyrus & 0.213 & **Temporal\_Inf\_R, Inferior temporal gyrus** & 0.031 \\ Frontal\_Mid\_L, Middle frontal gyrus & 0.208 & **Angular\_L, Angular gyrus** & 0.030 \\ Frontal\_Inf\_Oper\_L, Inferior frontal gyrus & 0.180 & Angular\_R, Angular gyrus & 0.030 \\
**Hippocampus\_L, Hippocamp** & 0.180 & **ParaHippocampal\_R, Parahippocampal gyrus** & 0.029 \\
**Hippocampus\_R, Hippocamp** & 0.176 & Amygdala\_R, Amygdala & 0.029 \\ Frontal\_Sup\_Or\_L, Superior frontal gyrus & 0.172 & Supramarginal gyrus & 0.029 \\
**ParaHippocampal\_R, Parahippocampal gyrus** & 0.171 & **Hippocampus\_L, Hippocamp** & 0.029 \\
**Angular\_L, Angular gyrus** & 0.171 & Temporal\_Mid\_R, Middle temporal gyrus & 0.027 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The top 15 Information gains of anatomical region. Based on the evidences of the anatomical regions with the highest 15 information gains being shown on Table 3, a total of seven regions, including left and right hippocampus, left and right amygdala, right parahippocampal gyrus, left angular gyrus, and Inferior temporal gyrus, were presented in both compared groups. These regions have also been reported in other studies, suggesting brain volume loss in these regions in both the AD and MCI group [33]. For example, both AD and MCI patients appeared to have various degrees of volume loss in left and right hippocampus, left and right amygdala, and right parahippocampal gyrus. In subsequent experiments, we also discussed the accuracy of classification using the AVOI of seven common regions as feature.
## 4 Discussion
Figure 6: The overall sum of alive nodes. Figure 7: Packets received by the BS. It is based on multi-hop transmission and an energy utilization model that reduces the significant distance transmission and immediate utilization of restricted energy assets. Accordingly, the PECR algorithm covers extra transmission rounds with a comparative measure of starting energy. Figure 8 shows that the energy exhaustion pace of the PECR algorithm is 57% lesser than LEACH, 43% lesser than EC, 35% lesser than EECRP, and 29% lesser than EECA. The energy conservation of the sensor nodes and the elongated lifetime of the network is the main objective of WSNs. As discussed above, existing algorithms have a lot of limitations. The PECR algorithm focuses on the crucial points and solves these problems competitively. It advances the state of the art by overpowering the limits of the existing work with the combination of an energy consumption model, suitable path selection, and CH and MCH functionalities. The designed scheme ultimately elongates the network's lifetime by achieving the goal in a significant way and gives an overall 44% improved performance over existing algorithms. Due to the improved performance, the PECR algorithm is optimum for real applications like the Internet of Things, smart applications, and numerous self-designed, independent applications.
## 2 HSI Procedure
### Preprocessing
#### 2.2.7 Moving Average (MA)  
MA-based techniques play a very important role in HSI, especially in estimating global statistics from partial data. They provide a very efficient way of computing mean and covariance values, which can then be utilized by other applications such as anomaly detection and classification. On remotely sensed hyperspectral or multispectral imagery, MA sometimes has an application to denoising by smoothing the pixel values to achieve the purpose of noise suppression with retention of the significant features [65].  
In the food industry, MA has been widely applied to enhance the accuracy and robustness of the analytical models. For example, in the case of spinach juice droplets located on the surface of stainless steel, MA filters were used for noise smoothing and enhancing the clarity of spectral features, thus giving rise to a very high classification accuracy with SVM models [66]. Similarly, when MA was applied with other preprocessing methods to improve the quality of spectral data in the prediction of protein content in potato flour noodles, the performance of both PLSR and PCR models significantly improved. For instance, the MA-CARS-PCR technique gave highly accurate predictions [59].  
Nevertheless, in some cases, other more powerful techniques, such as OSC or OSC combined with CARS, showed improved performances compared to MA-based methods; the latter's performances relied so greatly on the type of machines used during pre-processing [59]. These sometimes identified that MA preprocessing could reduce accuracy when labeled by certain ML models [66]. Therefore, great attention should be paid when selecting the appropriate ML model with MA as the preprocessor.
## 5 Discussion
Secondly, the results of the FEM for the calculation of resonance frequency for multi-neck Helmholtz resonators have very small deviations from the results of the analytical method. This outcome is significant as the [PERSON] et al. [24] formula has been verified using experimental data. This, in turn, shows that the result of the FEM modeling is most likely to correspond to experimental results. The ability of the FEM to estimate the resonance frequency of multi-neck Helmholtz resonators can be useful in many practical cases. An advantage of the FEM approach, similar to the analytical approach, is that it will be able to rapidly estimate the resonance frequency of the multi-neck Helmholtz resonators in contrast to the method by [PERSON] et al. [34]. The method is easily applicable and accessible using commercial software, in contrast to [PERSON] et al. 's [34] elaborate multi-dimensional boundary element model. Another area of many practical applications depends on the ability of the FEM to be applied in cases with many different geometries, of which it is not trivial to apply the analytical formula of [PERSON] et al. [33]. As presented in the introduction, there are many different variations of the resonator's neck form (e.g., spiral neck [26], tapered neck [27], extended neck [28], angled neck aperture [29]) or possibly a combination of those. Applying the FEM in these cases can provide results that are difficult to achieve in other ways. The same applies in cases where there may be various combinations between the neck and the body of the resonator (e.g., neck-cavity-neck-cavity [32]). In such cases, due to the complex geometry, the FEM is a realistic approach for the estimation of the resonance frequency. In addition, the same applies in cases where manufacturing tolerances makes it impossible to prevent leaks or gaps in Helmholtz resonators in many real-world applications [34], which are sometimes even necessary, e.g., to allow drainage of moisture accumulating inside the cavity [36]. To establish the method's efficiency over a wide range of cases, more research must be conducted. The present study has only investigated a limited number of cases of multi-neck Helmholtz resonators. However, this study has gone some way towards enhancing our understanding of the applicability of the FEM for the estimation of the resonance frequency of multi-neck Helmholtz resonators. Future work is already underway for the application of the FEM to a broader range of cases.
## 2 The Applications in Smart Grid Based on Time Synchronization  
Smart grid applications require efficient, real-time communication systems to support continuous monitoring, measurement, and control [3]. However, the dynamics of power system signals are accelerating, driven by the expansion of renewable energy generation and the variability in load demands [23]. This increased reliance on renewable sources and microgrids introduces new temporal challenges, making precise time synchronization essential to ensure stability and efficient control.
## 4 Extracting the Data for Area Coverage and Energy  
The aim is to use the amounts of area coverage and energy consumption as decision criteria in a multi-criteria decision-making (MCDM) process. On one hand, since all the blocks of hTetro are equipped with cleaning functions, tiling sets, as depicted in Figure 6, will represent area coverage values. On the other hand, since the effect of time is not considered as a decision-criteria in this study, the consumed energy can be reported in terms of the consumed current [31; 32]. Moreover, the consumed current by the robot can be estimated using a current estimation algorithm developed based on [PERSON]'s algorithm [33; 34] which is illustrated by the pseudo code in Figure 7.  
In each sub-area, the starting point of the reconfigurable robot is set as a fixed position for all the tiling patterns to avoid the difference in the starting voltage and current. When the robot begins to  
Figure 6: Five sets of tiling sets for all four theorems. navigate, the energy values can be obtained with uniformity by fixing the common starting point and utilizing the same starting current for all the individual tiling sets [33]. Using the pre-defined tiling set, given in Figure 6, the robot's energy consumption is calculated for individual gaits, while the amount of energy consumed by all the motors for a single-step movement containing all types of locomotion, shapeshifting, and angular movements, overall, is called the gait pattern [33]. Supported by the experimental results, and considering only gait patterns as input, the motor torque, no-load speed, current, and the rated DC voltages are obtained by allowing the robot to traverse within the given tiling set. In the experiments, the output voltage amplitude is taken as the same as the offset value of the DC motor and the duty cycle, while applying the PWM technique, is calculated for the full 12 V DC supply. The total current ratio is set as 7.4 A before starting the area coverage. Generally, based on the Newton-Raphson algorithm, we developed an energy estimation scheme that characterizes the energy cost in terms of the current consumed with respect to specific locomotion  
Figure 7: Pseudo code for the energy estimation scheme. gains across all valid morphologies. This approach was adopted in our work to compute the current consumed by our hTetro robot for covering a given area. Tables 1-5 summarize the area coverage and energy values (in both percentages and real values) for each sub-area applying different tiling sets given by the introduced theorems. In addition, it should be mentioned that the _obs-free_ in the Tables 1-5 stand for the area which is the result of subtracting the area occupied by obstacles from the entire sub-area.