esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Method
### Compensation Principle
\tag{21}\]  
So, we can obtain the change in three components of the geomagnetic field in the aircraft coordinate system due to aircraft action without fluxgate magnetometer data. The T-L compensation model based on fluxgate magnetometer information is expanded and we establish a conjunctive compensation model with 36 coefficients. The expression is as follows:  
\[H_{I}=H_{I_{FULX}}+H_{I_{IGRF}} \tag{22}\]  
\[H_{I_{FULX}}=\sum_{i=1}^{3}p_{i}c_{i}+H_{flux}\sum_{i=1}^{3}\sum_{j=1}^{3}a_ {ij}c_{i}c_{j}+H_{flux}\sum_{i=1}^{3}\sum_{j=1}^{3}b_{ij}\check{c}_{i}c_{j} \tag{23}\]  
\[H_{I_{IGRF}}=\sum_{i=1}^{3}k_{i}g_{i}+H_{igrf}\sum_{i=1}^{3}\sum_{j=1}^{3}m_{ ij}g_{i}g_{j}+H_{igrf}\sum_{i=1}^{3}\sum_{j=1}^{3}n_{ij}\check{g}_{i}\check{g}_{j} \tag{24}\]where \(p_{i}\),\(a_{ij}\), and \(b_{ij}\) are the compensation coefficients based on fluxgate magnetometer information, and \(c_{i}\) is the cosine of the angle between the geomagnetic field and the three axes of aircraft coordinate system and it can be calculated using the measured values \(H_{i}\), \(i\) = 1, 2, 3 of the three axes of the fluxgate magnetometer. The expression is (25)and \(\hat{c}_{i}\) is the time derivative of \(c_{i}\). \[c_{i}=\frac{H_{i}}{\sqrt{H_{1}^{2}+H_{2}^{2}+H_{3}^{2}}},i=1,2,3 \tag{25}\]  
\(H_{flux}\) can be calculated by Equation (26). \[H_{flux}=\sqrt{H_{1}^{2}+H_{2}^{2}+H_{3}^{2}} \tag{26}\]  
\(k_{i},\ m_{ij},\ \text{and}\ n_{ij}\) are the compensation coefficients based on INS/GPS information, and \(g_{i}\) is the cosine of the angle between the geomagnetic field and the three axes of the aircraft coordinate system calculated by the IGRF and the rotation matrix and it is calculated using the analog values \(Hg_{i}\), i = 1, 2, 3 by the IGRF and rotation matrix. The expression is (27) and \(\hat{g}_{i}\) is the time derivative of \(g_{i}\). \[g_{i}=\frac{Hg_{i}}{\sqrt{Hg_{1}^{2}+Hg_{2}^{2}+Hg_{3}^{2}}},i=1,2,3 \tag{27}\]  
\(H_{igrf}\) can be calculated by Equation (28). \[H_{igrf}=\sqrt{Hg_{1}^{2}+Hg_{2}^{2}+Hg_{3}^{2}} \tag{28}\]  
So, the matrix form of Equation (22) can be expressed as follows:  
\[H_{I}=\text{X}w \tag{29}\]  
where X is n \(\times\) 36 platform attitude matrix and \(w\) is 36 \(\times\) 1 coefficients matrix. Then, the least squares solution of the compensation coefficient \(w\) can be expressed as follows:  
\[w=\Big{(}\text{X}^{T}\text{X}\Big{)}^{-1}\text{X}^{T}H_{I}. \tag{30}\]
# Improved Anti-Collision Algorithm for the Application on Intelligent Warehouse
## 6 Application  
In the RFID system, the tag has the ID number of the unique global identification, which is attached to the surface of the identified item or embedded in the item [16]. The tags may store the name, price, date of production, manufacturer and other related information of the item. The reader identifies the tag on the item by transmitting the radio frequency signal and obtains the stored data information. Readers need to communicate with a large number of tags, and a single reader has only one communication channel. If there are multiple tags in the reader's reading range, each tag can only send information after the reader's command because the tag itself cannot perceive the existence of other tags. In the intelligent warehouse, there are a lot of tags waiting for identification during the inbound and outbound processes, and when two or more tags send their ID data to the reader at the same time, signal interference occurs, which results in the reader can not accurately distinguish information from these wireless signals, that is, the tag collision problem occurs, and the whole query process will be significantly delayed. Aiming at this problem, an improved anti-collision algorithm is proposed in this paper to reduce the occurrence of collision and idle time slots, thus improving the recognition efficiency.  
In addition to attaching RFID tags to various goods, use RFID technology on the shelves, shelves, forklift trucks in the warehouse to make them have the ability of information feedback. The RFID reader is installed at the entrance and exit of the warehouse. When the goods enter and leave the warehouse in batches, the reader can get the information of the objects in and out of the warehouse automatically by reading the RFID tags attached to the goods. The forklift is equipped with a vehicle-mounted intelligent terminal and a hand-held RFID reader with wireless communication function, and the data is uploaded and sent through the wireless network and the back-end computer management system, and combined with the computer network technology, the warehouse operation state can be monitored in real time.  
Figure 4: Communication complexity with different protocols.  
Throughout the process, all actions can be carried out in batches, which greatly improves the efficiency of the warehouse, because of the improved anti-collision algorithm. In addition, the layout of intelligent warehouse is shown in Figure 5.
## 2 Background and Motivation
[25] on SFC challenges covered frameworks that proposed to solve challenges such as path selection, orchestration, security, SFC path composition, QoS, and traffic steering. They concluded by acknowledging that more work needs to be done in developing related technologies (SDN and NFV). We also consider frameworks that focus on areas such as resilience, fault recovery, performance tuning, and resource allocation to extend their contributions. To the best of our knowledge, we have summarised the key contributions of notable related surveys and how our work adds to the NFV/SFC domain. Unlike most existing surveys, our research also presents the detailed requirements for chaining virtual network functions in service provider environments, including some important use cases. The taxonomy we created in our work presents the design choices and technologies used for the implementation of different equivalence classes of NFV/SFC frameworks. This focuses on the existing problems in the NFV/SFC domain, that is, we categorise the surveyed frameworks based on their proposed solutions and technological design choices. Most of the notable related surveys we have presented only discuss the technologies and open challenges in the NFV or SFC domain. Some exceptions to this are the surveys by [PERSON] et al., which present a taxonomy of NFV/SDN architectures by categorising them into NFV-side and SDN-side designs, while [PERSON] et al. presented a taxonomy based on the life cycle of NFV platforms. [PERSON] et al. presented a taxonomy based on the optimisation approaches used in SFC, such as availability, placement, and load balancing. In contrast to previous surveys, we categorise the surveyed frameworks based on their proposed solutions and technological design choices. We carefully describe what each framework has been designed to achieve, what technology and approach have been used, and what performance (or other quality) benchmarks have been performed. We have also highlighted the different methods and technologies used for implementing each framework. In addition to presenting an extensive list of open research challenges in NFV/SFC, we also discuss some notable early attempts in the literature aimed at addressing the research challenges identified (Section 5), providing the reader with knowledge of some existing efforts in this direction.
# Time of Flight Distance Sensor-Based Construction Equipment Activity Detection Method
## 2 Literature Review and Background
### Optimization with Adaptive Moment Estimation (Adam) Algorithm  
In this study, the Adam algorithm was used in the deep learning model's learning process. The algorithm is a combination of root mean square propagation (RMSProp) and momentum methods and is used to adjust the learning rate of each parameter [34]. This algorithm works particularly well even in non-uniform data or parameter spaces and shows excellent performance in a variety of nonlinear optimization problems. The algorithm proceeds through the following mathematical expressions:  
Time step update:  
\[\text{t}\leftarrow\text{t}+1 \tag{1}\]  
Increment the time step, denoted as t, to signify progression through the training iterations.  
Gradient computation:  
\[\text{g}_{\text{t}}=\
abla_{\theta}\times\text{f}_{\text{t}}\times(\theta_{ \text{t}-1}) \tag{2}\]  
Compute the gradient of the stochastic objective function with respect to the parameters \(\theta\) at time step t, represented as \(\text{g}_{\text{t}}\).  
First-moment update:  
\[\text{m}_{\text{t}}=\beta_{1}\times\text{m}_{\text{t}-1}+(1-\beta_{1})\times \text{g}_{\text{t}} \tag{3}\]  
Update the biased first-moment estimate, \(\text{m}_{\text{t}}\), by using the exponential moving average of past gradients. The gradient decay factor is represented as \(\beta_{1}\).  
Second-moment update:  
\[\text{v}_{\text{t}}=\beta_{2}\times\text{v}_{\text{t}-1}+(1-\beta_{2})\times \text{g}_{\text{t}}{}^{2} \tag{4}\]  
Update the biased second raw moment estimate, \(\text{v}_{\text{t}}\), by using the exponential moving average of the squares of past gradients. The squared gradient decay factor is represented as \(\beta_{2}\).  
Bias-corrected first-moment calculation:  
\[\text{m}_{\text{t}}{}^{\text{b}}=\text{v}_{\text{t}}\ /\ (1-\beta_{2}{}^{ \text{t}}) \tag{5}\]  
Compute the bias-corrected first-moment estimate, \(\text{m}_{\text{t}}{}^{\text{b}}\), to correct for initialization bias.  
Bias-corrected second-moment calculation:  
\[\text{v}_{\text{t}}{}^{\text{b}}=\text{v}_{\text{t}}\ /\ (1-\beta_{2}{}^{ \text{t}}) \tag{6}\]  
Compute the bias-corrected second raw moment estimate, \(\text{v}_{\text{t}}{}^{\text{b}}\), to correct for initialization bias.  
Parameter update:  
\[\theta_{\text{t}}=\theta_{\text{t}-1}-\alpha\times(\text{m}_{\text{t}}{}^{ \text{b}})\ /\ (\sqrt{\text{v}_{\text{t}}{}^{\text{b}}})+\epsilon) \tag{7}\]Update the parameters \(\theta_{t}\) using the bias-corrected estimates. The learning rate, \(\alpha\), the bias-corrected first-moment estimate, \(\text{m}_{t}^{b}\), and the bias-corrected second raw moment estimate, \(\
u_{t}^{b}\) are utilized to adjust the parameters in the direction of minimizing the loss. \(\epsilon\) is constantly added to prevent division by zero.  
Good default settings for the tested machine learning problems are \(\alpha=0.001\), \(\beta_{1}=0.9\), \(\beta_{2}=0.999\), and \(\epsilon=10^{-8}\)[34]. Thus, the same hyperparameter values are used in this study.
## 1 Introduction  
There has long been an effort to devise non-invasive diagnostic techniques in the fields of medical and forensic sciences. The radiographs introduced by [PERSON] in 1895 have opened the era of forensic radiology since the first postmortem radiographic examination in 1898 [1]. With the development of three-dimensional imaging techniques such as magnetic resonance imaging (MRI) and computed tomography (CT) in the late 20 th century, forensic imaging has attracted much attention as a new non-invasive diagnostic tool as well as a new subfield of forensic science [2,3]. Compared to the radiographs, forensic imaging using cross-sectional techniques has advantages in that it allows for three-dimensional examination and visualization of complex structures in an easy and interactive way [1,4]. Indeed, the past two decades have seen a drastic development of CT techniques and subsequently a rapid growth in the application of CT in the forensic context [5].  
The purpose of this study is to quantitatively assess the trend of CT-related forensic studies through bibliometric methods and thus present a holistic idea about the application of CT in the field of forensic science of the past and present. Bibliometric studies allow to track the frequencies and trends of scientific publications associated with a specific field/topic and the relationship of citationsbetween the works in a systematic way [6; 7]. Specific focus will be placed on the growth trend of this field in terms of the number of publications, most active journals with trend topics, and productive authors and countries. This research is expected to provide researchers using CT with not only an understanding about the past research trend but also an insight about future research topics and potential collaborative partners.
## 4 Experimental Results
### Implementation Details  
The environment used in this experiment was Python 3.10 and torch 2.4.0 + cu124. An NVIDIA A600 GPU (NVIDIA Corporation, Santa Clara, CA, USA) was used for training and testing the model. During the training process of the SWIMSEG, SWINSEG, TCDD, and HRC_WHU datasets, the initial learning rate was set to 0.0005, the optimizer used was AdamW, and cosine decay was set to gradually reduce the learning rate to 5 \(\times\) 10\({}^{-6}\). The batch size was 4, and the number of training epochs was 100. The input image size for the model was 224 \(\times\) 224, and the output image size was also 224 \(\times\) 224. Data augmentation operations added Gaussian noise with a probability of 0.5. The loss function for all models is set to binary cross-entropy loss to optimize model performance. Additionally, the model saves the best weights each time the validation set performance improves.
## 2. Stray Current Corrosion Mechanism and Interference Characteristics
### Corrosion Mechanism and Interference Characteristics of AC Stray Current
#### 2.2.4 Oscillation Model  
[PERSON] [30] proposed an oscillation model, pointing out that the AC voltage oscillates continuously between the outside of the pipeline and the soil environment in contact with it, causing the passive film to be damaged and corrosion to occur. The oscillation model is proposed based on the alkalinization theory combined with the electrochemical process. The model proposes that in acidic, neutral, and alkaline environments, high current interference will cause the pipeline potential to oscillate between the corrosion-free zone and the active dissolution zone, as well as the passivation zone and the corrosion-free zone. The reason for AC corrosion at this time is that the cyclic oscillation of the AC voltage destroys the passivation film on the pipeline surface and forms a porous, non-protective oxide film. This non-protective oxide film continues to thicken as the reaction proceeds, and the pipeline is not corroded during this period [31]. [PERSON] et al. [32] executed an experiment on the corrosion behavior of pipelines in alkaline environments and found that the AC current affects the polarization potential and the pH value of the environment, resulting in periodic destruction of the passivation layer of the pipeline steel. This periodic destruction is a typical manifestation of the oscillation model.
# Data-Driven Convolutional Model for Digital Color Image Demosaicing
## 3 Methods
### Image Model
Similarly, if the bandwidth of the filter allows the Luminance component to spill in the Chrominance signal, the false color artifact is visible. Finally, if the Chrominance filter bandwidth is too narrow, low frequency components in the Chrominance channel causes the color of the objects to spread beyond their edges thus producing a visible watercolor effect. Classical demosaicing algorithms tend to underestimate Luminance and overestimate Chrominance, therefore blurring and false color artifacts are particularly evident. Optimal selection of the bandwidth for the chrominance and Luminance filters is, in general, not possible as it depends on the original input signal. In this work we propose a data-driven solution to this problem, by learning the filter coefficients that minimize a cost function on the training dataset of patches. With this approach, we let the network act as a non-linear filter with learnable coefficients that can automatically select the \"best\" separation between regions in the frequency spectrum.
###### Abstract
Furthermore, outdoor experiments are conducted to validate its reliability. The remainder of this paper is structured as follows: Section 2 presents a CBS system model for ground deformation detection. Section 3 outlines the data processing flow for detecting ground deformations based on a CBS. In Section 4, the outdoor experimental scenarios and results are discussed, along with considerations related to the CBS-based ground deformation detection methodology. Finally, conclusions are drawn in Section 5.
###### Abstract  
One of the most promising and exciting research fields of the last decade is that of 3D-printed antennas, as proven by the increasing number of related scientific papers. More specifically, the most common and cost-effective 3D printing technologies, which have become more and more widespread in recent years, are particularly suitable for the development of dielectric resonator antennas (DRAs), which are very interesting types of antennas exhibiting good gain, excellent efficiency, and potentially very small size. After a brief survey on how additive manufacturing (AM) can be used in 3D printing of antennas and how much the manufacturing process of DRAs can benefit from those technologies, a specific example, consisting of a wideband antenna operating at 2.4 GHz and 3.8 GHz, was deeply analyzed, realized, and tested. The obtained prototype exhibited compact size (60 \(\times\) 60 \(\times\) 16 mm\({}^{3}\), considering the whole antenna) and a good agreement between measured and simulated S\({}_{11}\), with a fractional bandwidth of 46%. Simulated gain and efficiency were also quite good, with values of 5.45 dBi and 6.38 dBi for the gain and 91% and 90% for the efficiency, respectively, at 2.45 GHz and 3.6 GHz.  
DRA; additive manufacturing; 3D-printed antennas; wideband; IoT; 5G Article
## 2 Data
### CDAAC COSMIC-2 Neutral Atmospheric Profiles  
The CDAAC's inversion package to invert the fundamental observable (time delay) for a RO occultation event is summarized in [[http://cdaac-www.cosmic.ucar.edu/cdaac/doc/documents/[PERSON]_newroam.pdf](http://cdaac-www.cosmic.ucar.edu/cdaac/doc/documents/[PERSON]_newroam.pdf)]([http://cdaac-www.cosmic.ucar.edu/cdaac/doc/documents/[PERSON]_newroam.pdf](http://cdaac-www.cosmic.ucar.edu/cdaac/doc/documents/[PERSON]_newroam.pdf)). The general descriptions of CDAAC inversion procedures are detailed in [21; 22; 23]. Table 1 from [22] details methodologies used in each of the CDAAC inversion procedures.  
RO refractivity is a function for temperature and pressure in the stratosphere and temperature, moisture, and pressure in the troposphere [25; 26], which can be formulated as  
\[N=77.6\frac{P}{T}+3.73\times 10^{5}\frac{e}{T^{2}} \tag{1}\]  
Figure 2: (**a**) The average of COSMIC-2 occultations in 500 \(\times\) 500 km\({}^{2}\) box per day during October 2019. (**b**) The daily average of the COSMIC-2 (in blues line) and COSMIC (in red line) density distribution was constructed by 500 \(\times\) 500 km\({}^{2}\) cells in the same latitude band. COSMIC data are from October 2009.  
where \(P\) (in hPa) is pressure, \(T\) (in K) is temperature, and \(e\) (in hPa) is the water vapor pressure [27]. CDAAC used a one-dimensional variational approach (1D var, [[http://cdaac-www.cosmic.ucar.edu/cdaac/doc/documents/ldvar.pdf](http://cdaac-www.cosmic.ucar.edu/cdaac/doc/documents/ldvar.pdf)]([http://cdaac-www.cosmic.ucar.edu/cdaac/doc/documents/ldvar.pdf](http://cdaac-www.cosmic.ucar.edu/cdaac/doc/documents/ldvar.pdf))) to inverse refractivity to temperature, moisture, and pressure. The European Centre for Medium-Range Weather Forecasts interim (ERA-interim) reanalysis is used as a priori estimates [28]. We will use the 1D-var data products (wetPrf) for our comparisons.  
Note that to ensure the 1D var water vapor retrievals are using the information primarily from the refractivity, but from the a priori, CDAAC used a very tight retrieval constraint where the residual refractivity (defined as refractivity computed from the final temperature and moisture minus the observed refractivity) are within the known observation errors. Because CDAAC COSMIC-2 wetPrf is only available before October 2019, we limited our comparisons to the same period in this study. CDAAC also provided another set of COSMIC-2 water vapor retrievals (i.e., wetPrf2, see [29]). Ref. [29] stated that to reduce the refractivity bias impact to the retrievals due to the possible SR condition, the wetPrf2 water vapor profiles may substantially fit the background (for example, the ECMWF) below 2 km. Because wetProf2 retrievals may use more information from the background in the lower troposphere, which is hard to quantify, we do not use wetProf 2 in this study.
## 2 A Prototype Design of the Smart Photovoltaic System Blind
### Properties 2: Tracking System
#### 2.2.1 Tracker Types Applicable to the SPSB  
The tracker types should be considered to maximize electricity generation by increasing the solar radiation that reaches the PV panel. These tracker types can be categorized into the single-axis tracker and dual-axis tracker (refer to Figure 2).  
1. Single-axis tracker type: The single-axis tracker type is a technology that has one axis of rotation and can be divided into four tracker types: (1) the horizontal single-axis tracker (HSAT); the vertical single-axis tracker (VSAT); (2) the tilted single-axis tracker (TSAT); and (3) the polar aligned single-axis tracker (PASAT). The HSAT can track the daily north-south motion of the sun through the axis of rotation horizontal to the ground (refer to tracker type (A) in Figure 2). The VSAT can track the daily east-west motion of the sun through the axis of rotation vertical to the ground (Refer to tracker type (B) in Figure 2). The TSAT sets the axes of rotation between the horizontal and the vertical. Thus, the daily east-west motion of the sun is tracked by setting the axis of rotation to be parallel to the axis of the earth's rotation (refer to tracker type (C) in Figure 2). The PASAT is aligned to the polar star. Through this, the tilt angle becomes equal to the latitude of installation and allows it to align with the Earth's axis (refer to tracker type (D) in Figure 2) [23; 49; 50].
2. Dual-axis tracker type: The dual-axis tracker type is a technology that has two axes of rotation and can be divided into two tracker types: (1) the tip-tilt dual-axis tracker (TTDAT); and (2) the azimuth-altitude dual-axis tracker (AADAT). In general, the TTDAT tracks the daily east-west motion of the sun and north-south motion of the sun by rotating the PV panel fixed to the top of the pole (refer to tracker type (E) in Figure 2). The AADAT can track the daily east-west motion of the sun and the daily north-south motion of the sun using a large ring mounted on the ground with a series of rollers (refer to tracker type (F) in Figure 2) [23; 49; 50].
# Experimental Study of Influence of Plant Roots on Dynamic Characteristics of Clay
## 3 Experimental Results Analysis
### Influence of Plant Root Systems on Soil Shear Modulus
#### 3.1.3 Shear Modulus Fitting Results of the Hardin-Drnevich Hyperbolic Model  
The dynamic shear modulus fitting parameters of the Hardin-Drnevich hyperbolic model are shown in Table 2. As observed in the table, the maximum dynamic shear modulus for samples with added plant root systems consistently surpasses that of samples without plant roots. The maximum dynamic shear modulus can be enhanced by up to 27.6% (\(\rho_{g}=1.5\%\) g \(\cdot\) cm\({}^{-3}\), \(\sigma_{c}=300\) kPa), underscoring the capacity of plant root systems to elevate the soil's dynamic shear modulus. The correlation coefficients obtained are all greater than 0.97, indicating a robust fit of the regression curves to the observed values. The findings in this table offer valuable insights for seismic design involving root-reinforced soil.  
Figure 10: Conceptual diagram of root-soil interaction.