esa-sceva/satcom-chunk-collection huggingface dataset:
# Weakly Supervised Learning with Positive and Unlabeled Data for Automatic Brain Tumor Segmentation
## 3 Experiments and Results  
Two datasets have been used for the experiments. An explorative analysis was conducted using an in-house dataset of 19 patients with glioblastoma. For this in-house dataset, T\({}_{1}\) contrast-enhanced, T\({}_{2}\) Flair- and diffusion-tensor (DTI)-MRI images are available for each subject. More details on imaging parameters are provided in [12]. The images were rigidly registered to the Flair image and resampled to a common in-plane spacing of 1 mm \(\times\) 1 mm. A brainmask was extracted semi-automatically and T\({}_{1}\) and T\({}_{2}\) images were normalized to a standard deviation of 1 and a mode of 0. The image intensity at each voxel and different DTI-derived measures were used as features corresponding to the approach described in [12].  
Trained experts manually segmented the GTV and used images from earlier or later timepoints for the iterative improvement of segmentations. This careful labeling process of the ground truth took more than four hours per subject. In addition to the small and only positive (tumorous) regions, a trained radiologist also created small annotations of healthy tissue (negative regions) to allow training the benchmark method. The creation of the small samples for all classes took less than 5 min per patient. The sparse annotations covered 0.25% \(\pm\) 0.15% of the complete brain volume and 2.6% \(\pm\) 1.5% of tumorous tissues. Of the sparse annotated voxels, 55.1% \(\pm\) 16.5% belonged to negative samples.  
In addition to this, the BraTS 2013 dataset [32] was used for validation in an independent cohort. To avoid overeningering, only final experiments were conducted using the BraTS dataset. Thus, if not stated otherwise, the in-house dataset was used.  
The BraTS 2013 dataset was preprocessed accordingly to [17] as a strong baseline approach. The images were normalized using histogram matching based on a 3D-Slicer [33]. The contrast of an arbitary subject (HG0001) was used as the reference. All voxels with intensity below the mean image intensity were discarded as background images. Follwoing this step, all images were normalized to the mean intensity of the CSF. For this, CSF was semiautomatically segmented in all subjects. The original four modalities were extended to ten images per subject by subtracting each contrsat from the others. For each image in the stack, the Laplacian of the Gaussian (scale 1.0) of the structure's tensor eigenvalues (scale 1.6) and the Hessian of Gaussian eigenvalues (scale 1.6) were calculated and used together with the pure intensities as features.
# Application and Evaluation of Surrogate Models for Radiation Source Search
## 2 Physical Model
### Statistical Model  
To develop surrogate models, we must first construct an appropriate statistical model. We assume a constant mean background with nominal intensity \(B=300\) gamma counts per second and take \(q_{0}=[x_{0},y_{0},I_{0}]\) to be the true source location and intensity. Radioactive decay and detection are Poisson random processes so we take the detector response to be a Poisson-distributed random variable. For each of the considered detectors, we employ the statistical model  
\[Y_{i}=\text{Pois}(\hat{u}_{i}(q_{0})+B),\;\;i=1,...,d, \tag{4}\]  
for the total detector response. Here, \(\text{Pois}()\) denotes the Poisson distribution with mean  
\[u_{i}(q_{0})=\hat{u}_{i}(q_{0})+B, \tag{5}\]  
for \(\hat{u}_{i}(q_{0})\) given by Equation (3). The responses of \(d\) detectors are mutually independent. A Gaussian distribution approximates the Poisson distribution with a large expected value, which in this case is accurate for a large number of gamma observations. Therefore, we approximate Equation (4) by  
\[Y_{i}\sim\mathcal{N}(u_{i}(q_{0}),\sigma_{0_{i}}^{2}),\;\;i=1,...,d, \tag{6}\]  
Figure 1: Satellite image of domain with overlaid model geometry from [2].  
where \(\sigma_{0_{i}}^{2}=u_{i}(q_{0})=\hat{u}_{i}(q_{0})+B\). We denote the detector observations by \(y_{i},\ i=1,...,d\), which are realizations of the random variables  
\[Y_{i}=u_{i}(q_{0})+\epsilon_{i},\ i=1,...,d, \tag{7}\]  
where \(\epsilon_{i}\sim\mathcal{N}(0,\sigma_{0_{i}}^{2})\).
## 1 Introduction
Given the assumption that the soil moisture and humidity index functions are identical under different conditions (i.e., spatial resolution), then the spatial resolution of soil moisture can be improved through scaling factor calculations. The parameters in downscaling factor methods are independent of ground data, and are characterized by simple operation and high efficiency [20]. This method has been shown to be applicable to semi-arid regions [11], and does not need to consider the effect of land-cover type on interpolation results. Given these approaches, the problem to be solved involves the application of an appropriate downscaling method to SMAP data to satisfy downscaling data in watershed scale research. The research in this aspect has certain research foundations, but there are some imperfections that need further study. The objectives of this study are hence to (1) establish the Temperature Vegetation Drought Index (_TVDI_) based on the downscaling factor method to improve the spatial resolution of SMAP soil moisture products in the study area; and (2) assess the accuracy of downscaling associated with various land uses.
# A Novel Channel Coding Scheme for RFID Generation-2 Systems
## 5 Conclusions  
In this article, we have proposed a new encoding algorithm for tag-to-reader RFID communication channels. The low-complexity decoder is suitable for RFID applications that require both low-cost and energy-saving solutions, like in battery-powered and passive-based systems.  
Despite its simple hardware implementation, the proposed solution has the same performance as a Viterbi hard decoding of the Miller 4 modulation scheme, even better for lower values of the channel signal-to-noise ratio due to the soft decision on the analog output of the receiver correlator. Results obtained through simulations have shown the feasibility and the performance of the proposed solution under different SNR conditions. The evaluation focused on the Gen-2 standard, but we strongly believe that the proposed scheme will be of interest for future UWB RFID research [23], for example by including the new positional encoding concept in a novel line encoder.  
Furthermore, the low hardware cost of the decoder could be applied to wireless sensor networks (WSNs), where the energy saving is a crucial design issue, not only for tags but also for readers, as in space applications [24; 25].  
As an interesting perspective for future research developments, we are looking for more performant decoding schemes by adding a state-dependent coded output, in order to increase the codeword minimum distance. Obviously, the trade-off of getting a higher coding gain with respect to simple hardware realization should be adapted in order to respond to the requirements of different target applications. Further, since the proposed baseband solution may be regarded as an alternative scheme with respect to the FM0 and Miller baseband codes, it could be extended and included in a turbo coded architecture like the one presented in [17], where the FM0 and Miller coded waveforms are treated as inner codes of a turbo coded scheme.  
The MATLAB source code of the modified simulator implementing the proposed coding scheme for EPC Gen-2 C1 is available online at [[http://dx.doi.org/10.5281/zenodo.62075](http://dx.doi.org/10.5281/zenodo.62075)]([http://dx.doi.org/10.5281/zenodo.62075](http://dx.doi.org/10.5281/zenodo.62075)).  
[PERSON] and [PERSON] conceived and designed the coding algorithm. [PERSON] developed the MATLAB code and performed the simulation experiments. [PERSON] and [PERSON] analyzed the simulation results and wrote the paper. [PERSON] supervised the work and the manuscript.  
The authors declare no conflict of interest.
# Dynamic Analysis of Fuzzy Systems
## 4 Dynamic Analysis of Non-Linear Fuzzy Systems: Second-Order Model  
This section aims to provide a fuzzy dynamic system analysis. This work will be extended to the technique of fuzzy non-linear analysis. Theoretical analysis is often the most efficient way to explore the characteristics of a system. A continuous system can be represented by differential equations using the values of the input and output variables. For the second-order case of three input variables and one output variable, such an equation will be obtained as follows:  
\[f(x_{1},x_{2},u)=x_{3} \tag{18}\]  
System variables are defined as follows:  
\[x_{1}=x(t),\;x_{2}=\frac{dx}{dt},\;x_{3}=\frac{d^{2}x}{dt^{2}} \tag{19}\]  
Often, the input \(u\) is zero because the system is stationary or time-invariant.  
Reviewing the literature, one of the graphical methods used is phase-plane analysis. This plane is two-dimensional and is helpful for second-order systems.  
Although the simulation is important for non-linear control, it must be supported by theoretical analysis. Blind simulation of non-linear systems is likely to yield few or erroneous results. This is especially true because of the different behaviours that non-linear systems can exhibit.  
\[\dot{x}=f(x,\dot{x},u) \tag{20}\]  
\[a_{2}\frac{d^{2}x(t)}{dt^{2}}+a_{1}\frac{dx(t)}{dt}+a_{0}x(t)=b_{0}u(t), \tag{21}\]  
With zero initial conditions. As with first-order systems, a standard form has been established. Rewriting the differential equation as follows:  
\[\frac{a_{2}}{a_{0}}\frac{d^{2}x}{dt^{2}}+\frac{a_{1}}{a_{0}}\frac{dx}{dt}+x= \frac{b_{0}}{a_{0}}u(t) \tag{22}\]Two parameters can be used to describe the characteristics of the second-order transient response in the same way that the time constant of the first-order system is determined. The two quantities are known as the natural frequency and the relative damping factor. The resultant transfer function becomes:  
\[\frac{X_{(s)}}{U_{(s)}}=\frac{K}{\frac{s^{2}}{\omega_{n}^{2}}+\frac{2\zeta s}{ \omega_{n}}+1}=\frac{K\omega_{n}^{2}}{s^{2}+2\zeta\omega_{n}s+\omega_{n}^{2}} \tag{23}\]
# A Filter-Based and Parallel Unknown Tag Identification Protocol in Open RFID Systems
## 4 Filter-Based and Parallel Unknown Tag Identification Protocol
### Separation Phase
#### 4.2.2 Building Fingerprint Filter  
Although the RSQF-based filter can assign a slot to each known tag, it cannot solve the slot collision problem when multiple tags have the same _quotient_ and _remainder_. Furthermore, it cannot separate known tags from unknown tags with the same _quotient_ and _remainder_ in open RFID systems. Therefore, this paper added a \(fingerprints\) vector to solve the above two problems. The reader generates a unique \(l_{fp}\)-bit fingerprint via a hash function \(h_{2}(id,r_{2})\) for every tag in the same \(run\), where \(r_{2}\) is another random seed. The reader inserts the fingerprint of each tag into the corresponding position of the \(fingerprints\) vector according to the _remainders_ vector, as shown in Figure 2.  
The reader broadcasts a request with parameters \(\langle occupids,runends,fingerprints,r_{1},r_{2}\rangle\) to all tags. After receiving the request, the tag \(t_{i}\) firstly calculates its occupieds index by \(h_{0}(id_{i})\) and obtains its \(run\) index \(\lambda_{i}\). Then, it obtains the beginning position \(b_{\lambda_{i}}\) and the end position \(e_{\lambda_{i}}\) in the \(fingerprints\) vector. Subsequently, it calculates its fingerprint \(FP_{i}\) by \(h_{2}(id_{i},r_{2})\) and checks \(fingerprints[j]\) (\(b_{\lambda_{i}}\leq j\leq e_{\lambda_{i}}\)). If \(FP_{i}=fingerprints[j]\), this tag regards itself as a known tag and deactivates itself. The flowchart of separation phase is shown in Figure 3. After this phase, all known tags are deactivated without participating in the collection phase.  
Figure 2: A simple RSQF-based fingerprint filter.
# Measurement of Ultra-High Speed by Optical Multistage Cascade Frequency Reduction Technology
## 3 Discussion and Results
### Laser Wavelength Tuning Experiment  
At present, there is no standard speed source used in the measurement of high-speed test equipment, and a Doppler velocimeter is essentially a frequency meter, so the frequency measurement ability of a velocimeter in a wide speed domain can only  
Figure 7: (**a**) First-stage two-dimensional frequency conversion spectrum; (**b**) two-dimensional frequency conversion spectrum of the second stage.  
be evaluated by establishing an analog speed signal [16]. The laser wavelength tuning experiment is designed. The laser wavelength is changed by the long thermal tuning function of the resonator and the laser wavelength is interfered with using the reference light inside the velocimeter to form an optical beat signal, which simulates the optical Doppler frequency shift characteristics when the object moves at elevated speed. By examining the frequency response characteristics of the velocimeter under the optical beat signal at different frequency points, the velocity measurement ability of the velocimeter is evaluated.  
The output fiber of the tunable laser is directly connected with the optical output port of the velocimeter, and the RF output port of the velocimeter is connected with the spectrum analyzer. The initial wavelength setting of the tunable fiber laser is the same as that of the frequency-stabilized laser module in the velocimeter. By adjusting the wavelength of the tunable fiber laser to different wavelength points and measuring the RF signal frequency of the optical beat after photoelectric conversion with the spectrum analyzer, the speed measurement range can be evaluated. The wavelength tuning range of the tunable fiber laser is 0.1\(\sim\)227.1 pm (corresponding to the frequency of 12.5\(\sim\)28.4 GHz) when the measuring speed range is 10\(\sim\)22 km/s.  
The rate of the shift in the optical beat frequency generated by the two-stage optical path with wavelength is obtained, as shown in Figure 8. The optical beat rate of the primary channel is 124.73 GHz/nm and \(-\)124.63 GHz/nm, and that of the secondary channel is 126.44 GHz/nm and \(-\)126.88 GHz/nm. The optical beat frequency varies linearly with wavelength. In actual measurement, the rate of the shift in two-stage optical beats needs to be calibrated before the experiment, so as to obtain a more accurate velocity profile.  
This experiment can obviously measure the characteristics of laser frequency shift, which are similar to the characteristics of interface velocity shift in the typical impact loading process. It can verify the principle feasibility of multi-cascade laser interferometric velocimetry technology.  
Figure 8: Linear fitting results of optical beat frequency.
# Compression-Aware Aggregation and Energy-Aware Routing in IoT-Fog-Enabled Forest Environment
## 4 Aggregation-Based Compressed Sensing
### Data Aggregations  
Heterogeneous data aggregation acts as a vital role in wireless sensor routing [29] especially in forest monitoring systems [30]. Specific to the forest region, several sensor nodes are deployed randomly in the region, which may sense the same value of data for a limited time. In such cases, data redundancy and many data retransmissions occur, which would deplete the resources. Data aggregation will remove the data duplications and group the data based on its type, then, forward it to the root.
## 2 The Paradigm of Carbon-based Energy Production
### Biomass as an Alternative to Coal  
Fossil fuel use contributes to various environmental issues, including air and water pollution, climate change, and ecosystem degradation [85]. As a result, there is an urgent need to reduce dependence on nonrenewable resources and transition to more sustainable and ecofriendly energy sources [86]. The search for less-carbon-intensive alternatives to coal has led to a growing interest in biomass, a potentially renewable and sustainable energy source with lower carbon emissions when combusted [87]. Research is assessing the technical, economic, and environmental feasibility of using biomass as a coal substitute in energy production. However, there are significant challenges, such as technical, economic, and environmental factors. Biomass properties differ from coal, requiring adjustments to combustion technology for efficient energy production. Additionally, the costs of biomass  
Figure 3: Distribution of greenhouse gas emissions worldwide in 2021 by major emitter adapted from [[https://www.statista.com/](https://www.statista.com/)]([https://www.statista.com/](https://www.statista.com/)), accessed on 15 March 2023).  
production and transportation can be higher than coal, impacting its economic feasibility. Sustainable biomass resource availability and competing uses, such as food production, must also be considered for long-term biomass energy viability. Furthermore, potential environmental impacts from biomass production and transportation, including land use changes and supply chain emissions, need to be addressed [88]. Coal power plants with a combined capacity of 35.4 GW are located in countries planning to phase out coal by 2030 or sooner, leading to these plants' expected shutdown [89; 90; 91]. In the power industry, biomass and waste can be used in two ways: as the sole fuel source in smaller combined heat and power plants or couilized in existing coal-fired power plants to reduce net CO\({}_{2}\) emissions [73; 92].  
Cofiring biomass and coal has been demonstrated as a cost-effective technology for increasing biomass to energy processes in power generation. This can be done either by directly burning a biomass and coal mixture or by first gasifying the biomass to create clean fuel gas, which is then burned with coal. This method can significantly reduce greenhouse gas emissions. As some researchers, such as [PERSON] [73], suggest, cofiring biomass and coal provides technical, economic, and environmental benefits compared to other options. One primary advantage is that the plant always has coal as its primary fuel, ensuring 100% utilization even if the biomass supply is suddenly interrupted. This guarantees continuous operation and reduces the risk of power outages.
# Experimental Study on Mechanical Properties of Mask-Improved Calcareous Sand
## 2 Laboratory Testing Program  
### Tested Materials  
In this experiment, medical masks manufactured by Anhui Subolun Garment Co., Ltd. (Tongcheng, China) were used, as shown in Figure 1. The mask consisted of 71% non-woven fabric, and 29% melt-blown fabric. Given that the mask's dimensions exceeded the capacity of conventional testing methods, it was divided into uniform sections measuring 10 mm \(\times\) 10 mm. To evaluate the influence of mask size on the mask-calcareous sand specimens, a comparative test was conducted using mask sizes of 5 mm \(\times\) 5 mm and 10 mm \(\times\) 20 mm.  
The calcareous sand used in the experiment was sourced from an island and a reef in the South China Sea. The content of CaCO\({}_{3}\) in the calcareous sand was 96%. The material had a relative particle density of 2.80 g/cm\({}^{3}\), and its gradation curve is shown in Figure 2. The median particle size (d\({}_{50}\)) is 0.73 mm. The coefficient of uniformity (C\({}_{\rm u}\)) is 2.618, and the coefficient of curvature (Cc) is 0.973. Here, d\({}_{10}\), d\({}_{30}\), and d\({}_{60}\) represent the particle diameters at 10%, 30%, and 60% passing by weight, respectively. The coefficients are calculated as follows: C\({}_{\rm u}\) = d\({}_{60}\)/d\({}_{10}\) and Cc = (d\({}_{30}\))\({}^{2}\)/(d\({}_{10}\)\(\times\) d\({}_{60}\)).  
Numerous studies have demonstrated that the optimal fiber volume content in fiber-reinforced soil is approximately 1.0% (equivalent to 0.6% by mass) [20; 21]. Accordingly, this study investigates the mass content of four fiber contents, namely 0%, 0.3%, 0.6%, and 0.9%.  
The density of the mixture was determined in accordance with the ASTM D4254 [22] and is presented in Table 1. Due to the challenges in controlling the water absorption properties of the mask material, the mixture used in the test was maintained in a completely dry state. From the table, it can be observed that both the maximum and minimum dry density of the mixture decrease with increasing mask content. Furthermore, when the mask content is low, the mixture's density is lower than when all masks are considered as pores. This suggests that the mask can absorb compaction energy, thereby hindering the compaction of the surrounding particles.
## 5 Conclusions  
The present study proposed a novel method to estimate finite finger joint CoRs using 3D hand skeleton motions reconstructed from CT scans. After a hand skeleton was reconstructed from CT data for each of the ten postures, each bone segment in the template posture was registered to that in each of the nine remaining postures so that all the ten postures shared the same bone surfaces. Then, the proximal bone segment of a joint from each of the nine remaining postures was registered to that in the template posture. The centerline of the distal bone segment of the joint from each of the 10 postures was identified by the PCA method. The finite CoR of the joint between two different postures was lastly derived as the intersection point of the identified centerlines of the distal bone segments of the joint from the two postures.  
The present study proposed to use the same bone surfaces among different hand postures for accurate estimation of finite finger joint CoRs. In [PERSON] et al.'s method, different bone surfaces were used, which could cause errors in the estimated finite CoRs reported in their study. In contrast, no apparent errors in the estimated finite CoRs were observed in the proposed method after using the same bone surfaces.  
The proposed method reduced the variation of estimated finite joint CoRs by 16.0% to 67.0% among the finger joints compared to [PERSON]'s method. The variation of estimated finite joint CoRs decreased as the rotation angle of the joint increased for both the methods. Specifically, the proposed method significantly outperformed [PERSON]'s method when the rotation angle was within 5 degrees.  
The proposed method can be used to accurately assess human joint movement for various applications, such as biomechanical modeling, clinical applications, and ergonomic design. However, this research was limited by the use of a CT scan.  
**Author Contributions:** Conceptualization, X.Y. and H.Y.; methodology: [PERSON] and H.Y.; validation: [PERSON] and H.Y.; formal analysis: [PERSON], [PERSON], and M.Z.; investigation: [PERSON] and H.Y.; resources: [PERSON], [PERSON], and H.Y.; data curation: [PERSON], [PERSON], and H.Y.; writing--original draft preparation: [PERSON] and [PERSON]; writing--review and editing: [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON]; visualization: [PERSON] and [PERSON]; supervision: [PERSON] and [PERSON]; project administration: [PERSON], [PERSON], and [PERSON]; funding acquisition: [PERSON] and [PERSON]. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Fundamental Research Funds for the Central Universities (JUSRP12051) and the research programs of the National Research Foundation (NRF) of the Ministry of Education, Science, and Technology (2017M3C1B6070526; 2018R1A2A2A05023299; 2018K1A3A1A20026539) and the Ministry of Trade, Industry, and Energy (R0004840, 2020).  
**Conflicts of Interest:** The authors declare no conflict of interest.