esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Analysis  
### LAI Retrieval in Virtual Scenes  
The VFP and LAI were first retrieved in the homogeneous turbid scene (Figure 3a), which had LAI values of 4, 6, and 8, respectively. Here, we used the cumulative LAI profile to represent the VFP. The vertical resolution of the cumulative LAI profile from the waveform data simulated by DART model was 0.9 m, matching that of the canopy in the virtual scene. Cumulative LAI profiles from our method showed the same trend as true values given from the DART model for all levels of LAI distributions [i.e., in Figure 5a-c: LAI \(\approx\) 4, LAI \(\approx\) 6, LAI \(\approx\) 8]. Then, we applied our method in the discrete individual-tree scene (Figure 3b), and the result is shown in Figure 5d The cumulative LAI profile derived from our method still had strong consistency with the true value given by the DART model (i.e., LAI \(\approx\) 0.98).  
The above validation theoretically proves the reliability of our method because the scene constructed in the DART model was generated for an ideal situation, such as randomly distributed leaves and a spherical leaf inclination angle distribution. However, there are many uncertainties in the real environment. For example, the forest canopy is usually a mixture of branches and leaves, and it is difficult to separate them. Furthermore, the leaf inclination angle is sometimes not spherically distributed in an actual environment. Therefore, in addition to using virtual scenes for validation, ground-based measurements and other LAI products were used for cross-validation.  
Figure 4: Reflectance values of twelve non forest-covered GLAS footprints which are mentioned in the text with an average value of 0.21.  
Figure 5: Comparisons between true reference cumulative LAI and predicted cumulative LAI derived from the DART model simulated waveform data.
# River Discharge and Saltwater Intrusion Level Study of Yangtze River Estuary, China
## 3 Results
### Spatial Distribution of the Salinity in the YRE and Its Offshore Areas
As can be seen in Table 6, when the discharge was 27,856 m\({}^{3}\)/s, regardless of the type of tide present, the excess area percentage of the SB was less than 10%. However, when the discharge was reduced to 16,331 m\({}^{3}\)/s, the excess salinity areas rapidly increased, and the percentage reached over 50%. Therefore, the size of the discharge had a significant impact on the salt water intrusions. Figure 11 details the differences in the salinity levels between the different river discharges and the multi-year river discharge (27,856 m\({}^{3}\)/s) during the dry seasons. When the river discharge decreased, the mean salinity levels inside the estuary rapidly increased. The salinity in the NB had been in a high concentration state and was less affected by the runoff. The salinity levels remained relatively stable, and the amplification lines were unchanged. The salinity in the upper and lower reaches of the SB were observed to have increased significantly. The amplification line (0.2%) was extended from the upstream and downstream areas to the middle reach area. The amplification line of 0.45% near the entrance of the YRE extended upstream, oppressing the amplification line of 0.2%. The amplification line of 2% was mainly concentrated within a 40 km range of the entrance, and interactions between the river discharges and the tides were evident. The range of the salinity changes was found to be relatively stable. However, when moving away from the entrance to the estuary, the effects of the runoff were observed to weaken. For example, the amplification lines were gradually reduced. Figure 10: The space distribution of salinity in the neap tide: (**1**) Q = 27,856 m\({}^{3}\)/s; (**2**) Q = 18,112 m\({}^{3}\)/s; (**3**) Q = 16,331 m\({}^{3}\)/s; (**4**) Q = 14,832 m\({}^{3}\)/s. Table 7 details the lengths of the salinity amplification in the SB and offshore areas compared to the multi-year discharge of 27,856 m\({}^{3}\)/s. In Figure 10, A, B, C, D, and E represent the respective lengths of the different salinity variations. As the discharges decreased, the length of B (<0.2%) gradually narrowed. When the runoff was reduced to 14,832 m\({}^{3}\)/s, the length was shortened from 27.94 km to 6 km. As the river discharges decreased, the lengths of A, C, D, and E were observed to gradually rise, and the total length increased by approximately 3 km. These data show that not only the river discharge, but also the high salinity which spills over from the NB, influence the salinity change in the upper reaches of the SB. The impact of river discharge on saltwater intrusion is mainly reflected in the SB and offshore areas. As the distance from the mouth of the YRE is getting farther, the effect of the river discharge gradually weakens, and there is basically no effect at the end.
## 4 Data Collection  
To collect the best possible data quality, we chose to collect the data directly from national supervisory authorities (SA). A SA is an independent public authority that supervises the application of European data protection law, including GDPR. Each EU member state has to have a SA, which has investigative and corrective powers, provides expert advice on data protection issues, and handles any raised complaints. However, collecting responses from SAs is more difficult because there is only one per member state, and they might not be inclined to participate in unsolicited research. Even though they are the best entity to answer the prepared data protection questions, we expected to not get a response from every SA. To have the best possible feedback, we have repeatedly asked for their participation and have collected the data between April 2020 and June 2021.  
The information gathering was centred around processing different forms of (special) data (e.g., biometrics) and any additional legislation or policies upgrading the GDPR requirements. The survey collected data for the following topics:  
1. Any other legislation on the use of biometry (other than the GDPR).
2. Any other specific legislation on privacy, specifically with relation to: 1. Video surveillance, 2. hotography, 3. Anonymisation, 4. Pseudonymization and/or, 5. Audit trails.
3. Any additional legislation that extends specific sections of the GDPR, specifically with relation to: 1. Verification of parental consent, 2. Processing data of the deceased, 3. Processing of genetic data, 4. Use of biometric data for the purpose of identification,e. Processing of health data, f. Processing of data on the sex life of individuals, g. Processing of data on sexual orientation, h. Erasure of personal data, i. Data protection officer designation/appointment, and/or, j. Supervisory authority consultations.
* Presence of additional legislation on backing up of data.
* Whether or not the use of biometrics is allowed for the electronic acquisition of handwritten signatures.
* Whether or not the use of biometrics is allowed in a work environment (e.g., opening of server rooms with a fingerprint).
* Minimum age of persons that do not require consent from a holder of parental responsibility.
# Medical Devices for Tremor Suppression: Current Status and Future Directions
## 7 Tremor Suppression Devices: Place in Therapy  
The onset of ET can occur early in childhood due to familial factors, but the majority of cases of ET appeared after the age of 40 [140]. One study investigated the correlation between the age of onset and the progression of ET in 115 patients [141]. Patients with an age of onset later than 60 years experienced a more rapid progression when compared to patients with a younger age of onset (\(p<0.001\)) [141]. Since the onset of ET and PD tremors typically occurs in middle to late adulthood, aging-associated diseases such as dementia [142; 143] and mild cognitive impairment [144; 145; 146] intersect with both of these conditions. These neurological disorders may further preclude patients from adhering to pharmacotherapies. The medical devices described above offer alternative options for the suppression of tremors (Table 1), especially in patients who are not eligible for surgical interventions (i.e., DBS, SRS, and MRgFUS). However, the use of these devices is patient specific. For example, although Cala Trio has an aesthetic design that will likely not pose any social concerns, wearable orthoses may be a better option if the patient has any contraindication to the use of electrical stimulation systems. Depending on the patient's needs, assistive feeding devices may be a useful addition to the patient's daily living. Most of the devices that are available for use are subjected to the FDA's Class I general control for safety and efficacy assurance. In addition to the general control, Cala ONE requires Class II special control for its performance standards and special prescriber labeling.
## 5 Characterization Using the Time Domain Measurement Data
### Measurement Results
#### 5.1.3 PDD Results  
The PDD values of the noise are shown in Figure 8. PDD means the probability that the duration of the pulse exceeds a threshold value \(\tau_{d}\). It can be seen from the measurement results that the PDD value of the office area was always smaller than the values at the other two measurement positions in the three frequency bands. This means that the duration of noise in the office area was the shortest during the same measurement time. The PDD values measured near the welder robot and the manual spot welder were not exactly the same, which may be due to the fact that the contact time between the welding head and the object was not a fixed value during manual welding, so the duration of pulse noise varied greatly.
## 7 Future Directions
### Advanced Materials for THz Devices  
Further refinements of THz communication will depend on upcoming materials with better characteristics. For example, metamaterial and two-dimensional materials such as graphene are capable of enhancing the efficiency and tunability of THz transmittersand receivers. Such materials can also help develop compact as well as low-cost THz components, allowing this technology's application in different areas.
# Raman Spectroscopy of Individual Cervical Exfoliated Cells in Premalignant and Malignant Lesions
## 2 Materials and Methods
### Sample Preparation  
We gently centrifuged an aliquot of the cervical cytology sample (at 600 g) to avoid lysis of the cells. In this process, we obtained a pellet. Excess blood in the sample may interfere with the Raman spectrum; thus, the samples were first inspected and scored for blood in samples using a visual pathology/cytology scale: grade 0 no blood presence, grade 1 corresponding to low blood, grade 2, heavy blood, and grade 3, excessive blood [26]. Then, we remove blood from the samples. We treated Grade 0 and 1 samples with H\({}_{2}\)O\({}_{2}\) for 30 s, neutralizing the effect with PreservCyt (a methanol-based fixative solution, consisting of a mixture of water and methanol, 40-70% and 30-60%, respectively) [26]. Grade 2 and 3 samples were treated with red blood cell lysis buffer for 15 min, followed by two-time cycles of saline wash and centrifugation; then, pellets were treated with H\({}_{2}\)O\({}_{2}\). According to this evaluation, 22 samples in the premalignant/malignant group (CIN2 and CIN3 samples) received treatment with erythrocyte (RBC) lysis buffer and H\({}_{2}\)O\({}_{2}\) (30%) to eliminate any residual blood that could interfere with the measurement. The samples corresponding to normal cells and CIN1 29 samples (healthy group) and 30 samples (CIN1 group) were treated only with H\({}_{2}\)O\({}_{2}\).  
We spread the cell pellet on glass slides. We obtained the Raman spectrum using the XploRA plus Raman system (Jobin Yvon Technology) with an Olympus BX41 built-in microscope (model BX41 TF). Measurement conditions in the Raman spectrophotometer were like those described by [PERSON] et al. [27]. The objective used for the detection of the Raman spectrum was 100\(\times\). We used a 532 nm laser to excite the Raman scattering response; the acquisition time was 30 s for 2 accumulations. Full laser beams were applied to the sample, focusing on a field of \(\sim\)1 \(\upmu\)m; the spectrometer gating was 1200 groves/mm. The Raman signals were calibrated and recorded using a LO optical phonon spectrum (with silicon mode at 520.7 cm\({}^{-1}\)). For each sample, 3 measurements of both the cytoplasm and the nucleus were obtained in different regions of interest from 8 to 10 different cells depending on the sample quality (in some samples, the number of cells present was low, in which case we obtained measurements of at least 3 cells). The spectral region selected for the study was 800-1800 cm\({}^{-1}\) since it constitutes the fingerprint region for most biological samples [28]. We used LabSpec 6 software (Horiba Scientific, Ltd., Kyoto, Japan) to control the spectrophotometer and visualize the Raman spectra.
# Damage Identification Method Using Additional Virtual Mass Based on Damage Sparsity
## 3 Improved OMP Damage Identification Method Based on Sparsity
### Residual Vector Variance Criterion  
According to Equation (17), the residual vector corresponding to the variation of each damage-factor element is calculated to obtain the residual matrix \(\mathbf{\varepsilon}=\left(\mathbf{\varepsilon}_{1},\mathbf{\varepsilon}_{2},\ldots,\mathbf{ \varepsilon}_{n}\right)\) and its variance matrix \(\mathbf{\sigma}^{2}\).  
\[\mathbf{\sigma}^{2}=diag\left(\mathbf{\varepsilon}^{T}\mathbf{\varepsilon}\right) \tag{19}\]  
The sparse degree of the damage-factor is determined to be \(N\) by sorting each element of \(\mathbf{\sigma}^{2}\) from large to small and setting the threshold value \(p_{0}\). The \(n\)-\(N\) column vectorscorresponding to the smaller variance in the sensitivity matrix \(R\) are eliminated to obtain \(\widetilde{\mathbf{R}}_{0,1}\). The residual vector \(\varepsilon_{0,1}\) corresponding to \(\widetilde{\mathbf{R}}_{0,1}\) is calculated using Equation (17).  
\[p_{0}\;\leq\;\frac{\sum_{i=1}^{N}\sigma_{i}^{2}}{\sum_{i=1}^{n}\sigma_{i}^{2}} \tag{20}\]  
Let the residual vector corresponding to the remaining \(N\) damage factors form the residual matrix \(\widetilde{\varepsilon}_{0,1}\). The variance matrix \(\widetilde{\sigma}_{0,1}^{2}\) is then calculated and sorted to obtain the sensitivity matrix \(\widetilde{\mathbf{R}}_{0,2}\) and its residual vector \(\varepsilon_{0,2}\) by removing the column vector \(\mathbf{r}_{s}\) corresponding to the minimum variance \(\sigma_{j}^{2}\) from matrix \(\widetilde{\mathbf{R}}_{0,1}\). The final residual matrix \(\varepsilon_{0}=(\varepsilon_{0,1},\varepsilon_{0,2},\ldots,\varepsilon_{0,N})\) is obtained by repeating the above step to determine the number and location of damage substructures using the principal component analysis method and compute the specific values of the possible structural damage factors using the objective function (5).
# A New Data Processing Approach for the SHPB Test Based on PSO-TWER
## 2 Related Work
### Denoising of Waveform Signals
In Figure 3b-e, from the statistical results for the fractal dimensions, the fractal dimension value tends to decrease as the span increases. When the span is 0.01, the fractal dimensions are 1.0308 and 1.0671, which shows that the noise signal is effectively removed. The results at 0.05 are 1.0314 and 1.0571, which are slightly different from the results at 0.01. This shows that the impact of different spans on the results is mainly on the trend. Based on the above explanation, given the premiseof ensuring the operation speed and smoothing effect, the appropriate span value of the robust loess filter should be 0.01-0.1.
# A Review and Future Directions on the Deterioration Evaluation of Concrete Utility Poles
## 3 The Current Status and Issues of Concrete Utility Pole
### Deterioration Evaluation  
Regarding research on non-destructive testing methods, [PERSON] et al. [30] conducted a study on damage evaluation of concrete utility poles, focusing on the propagation speed of ultrasonic waves. The study used Hume pipes with hollow interiors similar to Japanese concrete poles for ultrasonic measurements. The effects of intentionally induced cracks on measurement results were analyzed, and it was confirmed that ultrasonic waves' propagation speed decreases as the cracks' width and length increase. Similarly, [PERSON] et al. [31]investigated the evaluation of internal voids and the degree of corrosion of internal steel reinforcement using the impact elastic wave method. Their study revealed that as damage and rebar corrosion progress, concrete poles exhibit delays in wave propagation speed, along with increased kurtosis and skewness in the data distribution.  
Elective inspection methods have also been explored in the context of rebar deterioration evaluation in concrete utility poles [32; 33; 34]. For example, [PERSON] et al. [33] studied a non-destructive inspection method for the health assessment of internal rebar in utility poles by measuring magnetic field signals in actual poles. Based on the measurement data, they developed an algorithm to evaluate deterioration through pattern recognition of healthy and damaged states. [PERSON] et al. [35] proposed a non-destructive inspection method using an endoscope probe inserted into drainage holes attached to utility poles to observe inner surface cracks. Their research included model analysis of internal crack formation, enabling the evaluation of the internal cracking state and overall damage of the poles.  
Although there are examples of non-destructive testing methods for concrete utility poles [30; 31; 32; 33; 34; 35; 36], the number of studies remains limited. Among the literature reviewed in this study on non-destructive testing, no studies were found addressing iron utility poles or FRP utility poles. The studies were scarce even for concrete utility poles, with most focusing on wooden utility poles. Consequently, it was decided to investigate the existing research on wooden utility poles, which constitutes the bulk of prior research on utility poles, to identify insights that could be applied to the deterioration evaluation of concrete utility poles.
###### Abstract  
Monitoring soil moisture content (SMC) remains challenging due to its spatial and temporal variability. Accurate SMC prediction is essential for optimizing irrigation and enhancing water use efficiency. In this research, a Gradient Boosting Regressor (GBR) model was developed and validated to predict SMC in two soil textures, loam and silt loam, using meteorological data from Internet of Things (IoT) sensors and gravimetric SMC field measurements collected from five different depths. The statistical analysis revealed significant variation in SMC across depths in loam soil (\(p<0.05\)), while silt loam exhibited more stable moisture distribution. The GBR model demonstrated high performance in both soil textures, achieving R\({}^{2}\) values of 0.98 and 0.94 for silt loam and loam soils, respectively, with low prediction errors (RMSE 0.85 and 0.97, respectively). Feature importance analysis showed that precipitation and humidity were the most influential features in loam soil, while solar radiation had the highest impact on prediction in silt loam soil. Soil depth also showed a significant contribution to SMC prediction in both soils. These results highlight the necessity for soil-specific modeling to enhance SMC prediction accuracy, optimize irrigation systems, and support water resources management approaches aligning with SDG6 objectives.  
Keywords:soil moisture content; gradient boosting regressor (GBR); IoT sensors; soil specific modeling; crop water management Article
## 2 Pedigree Methods
### Applications in Selection  
Currently, the management of autochthonous pig breeds is mostly focused on the conservation of genetic diversity, while genetic evaluation for economically important traits is rarely carried out, with a few exceptions such as the Iberian pig breed [54]. In autochthonous breeds, there are few studies of application of optimal contribution selection (OCS), a breeding strategy designed to maximize genetic gain while simultaneously controlling the rate of inbreeding. This is achieved by optimizing the selection and mating of individuals based on their genetic performance and their relatedness to others in the population. One of the recent studies was conducted by [PERSON] et al. [55], in which the authors used pedigree data to apply OCS to the Black Slavonian pig, a Croatian autochthonous pig breed. The study showed that it is possible to obtain genetic gain and maintain the genetic diversity of the analyzed population. However, the study emphasized the need to keep consistent pedigree records to obtain accurate estimates of relationships, which are the basis for creating mating plans. Pedigree information was used in combination with molecular information by [PERSON] et al. [1] to develop different mating strategies for small pig conservation populations. The OCS based on pedigree (POCS) was successful in reducing the inbreeding rate when truncation election was applied. A similar effect of using pedigree in controlling inbreeding in optimum contribution selection was found by [PERSON] et al. [56], where POCS achieved higher true genetic gain than OCS based on genomic information because it managed expected genetic drift without restricting selection at Quantitative Trait Loci (QTL). [PERSON] et al. [35] analyzed the pedigrees of commercial pig populations with the aim of assessing the source of the selective advantage provided by OCS and concluded that the source of selective advantage from OCS comes from Mendelian sampling terms. However, practical problems arising from the low pedigree completeness and data accuracy had to be overcome by incorporating criteria for selecting candidates with sufficient genealogical information, as the optimization process is highly dependent on the quality of pedigrees [57]. Therefore, the role of genealogical information in a strategy to maintain genetic variability in populations with low production levels remains crucial, as it allows for accurate estimation of relatedness between selection candidates and inbreeding coefficients of individuals. By using pedigree data, breeders can implement optimal contribution selection effectively, ensuring that the selection of breeding animals maximizes genetic gain while minimizing inbreeding. This approach supports the long-term sustainability and adaptability of autochthonous pig breeds and preserves their unique genetic resources and cultural value.
# Fast Converging Gauss-Seidel Iterative Algorithm for Massive MIMO Systems
## 2 Massive MIMO System Model
### Channel Model  
In practical large-scale MIMO communications, channels are generally correlated. The detection becomes complicated when channels are correlated. Considering the correlation between the transceiver and receiver antennas in massive MIMO communications, [PERSON]'s channel model [22] is described by  
\[H=R_{r}^{\frac{1}{2}}HR_{t}^{\frac{1}{2}} \tag{3}\]  
where \(H\) denotes the independent identically distributed complex channel fading matrix, and \(R_{r}\) and \(R_{t}\) denote the degree of channel correlation between the receiving and transmitting antennas, respectively.  
\[R_{r}(n,m)=\big{\{}\begin{array}{l}\big{(}\zeta_{r}e^{i\phi_{m,n}}\big{)}^{m-n },n\leq m\\ R_{r}^{*}(m,n),n>m\end{array} \tag{4}\]  
\[R_{t}(n,m)=\big{\{}\begin{array}{l}\big{(}\zeta_{r}e^{i\phi_{m,n}}\big{)}^{m- n},n\leq m\\ R_{t}^{*}(m,n),n>m\end{array} \tag{5}\]  
where \(\theta\) and \(\phi\) represent the random phases of the transmit and receive antennas that are uniformly distributed, respectively. Assuming that \(\zeta_{r}\) and \(\zeta_{t}\) are known at the base station side, the channel correlation scenarios are classified into four types according to the different values, which are as follows [23].  
(1) \(\zeta_{r}=0\) and \(\zeta_{t}=0\), the channel is independent and identically distributed.  
(2) \(\zeta_{r}=0\) and \(\zeta_{t}\
eq 0\), the channel is user-side relevant.  
(3) \(\zeta_{r}\
eq 0\) and \(\zeta_{t}=0\), the channel is base-station-side related.  
(4) \(\zeta_{r}\
eq 0\) and \(\zeta_{t}\
eq 0\), the channel is fully correlated.