esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Moisture Retention Tests
#### 4.1.2 Effect of Biopolymer Concentration  
_Medium-grained sand_. Increasing the biopolymer concentration had no significant effect on moisture retention (Figure 5 and Table 3). The published literature also reported mixed findings, with most studies concluding that higher biopolymer concentrations increase moisture retention [17; 28; 49], while one study could not confirm this observation [46]. For biopolymer applications that increased moisture retention (Figure 5), it is assumed that doubling the concentration amplified the effects of water absorption capacity, bioclogging [52; 56], and crust formation [47; 57] (as discussed in Section 4.1.1).  
For some biopolymers, doubling the concentration had little effect on moisture retention or even caused it to decrease. [PERSON] et al. [28] reported similar results when testing applications of lignocellulosic on red sand, with moisture retention only increasing noticeably up to a certain concentration. This suggests that each biopolymer has a specific threshold concentration, beyond which only minor changes or even a decrease in soil moisture retention occurs. This trend is likely related to an increase in biopolymer viscosity, which reduces infiltration depth and increases evaporation rate, resulting in the adverse effects of higher viscosity outweighing the positive effects of increased water absorption capacity, bio-clogging, and crust formation (see Section 4.1.1).  
_Fine-grained silica sand_. On fine-grained silica sand, doubling the concentration resulted in a slight increase in moisture retention for most tested biopolymers (Figure 6 and Table 3), which agrees with previous studies [17; 28; 49]. It is believed that the same factors assumed for medium-grained sand caused these trends on fine-grained silica sand. Samples treated with XG and TG exhibited decreased moisture retention when the concentration was doubled, likely because of an increase in viscosity that limited the infiltration depth and, thus, increased the evaporation rate. CMC-treated samples showed no significant change, assumably because of the balancing of positive effects (e.g., increased water absorption, bioclogging, and crust formation) and the negative effect of higher viscosity. Fine-grained silica sand samples, on average, exhibited a lower moisture-retention capacity than medium-grained sand samples (Figure 5) because of the higher hydraulic conductivity of medium-grained sand that enhanced the infiltration depth and decreased evaporation throughout the curing period.
## 2 Modeling and Degradation Simulation
### Model Validation  
Using real 24-h data [17] (Figure 3), simulations confirm power proportionality to irradiance (Figure 4) and inverse proportionality to temperature (Figure 5), aligning with [18; 19]. While our validation is based on synthetic data aligned with known physical behaviors, future improvements could involve hardware-in-the-loop setups for more realistic validation [20].
# Flexible Null Broadening Robust Beamforming Based on JADE
## 5 Conclusions  
This paper obtains the mixing matrix by blind source separation technology, and the DOA of interference is obtained by the correlation method. Then, we can further estimate  
Figure 5: Relationship between output SINR and the interference mismatch angle.  
Figure 6: The descending trend of eigenvalues of covariance matrix.  
the array gain-phase errors, so as to improve the robustness of beamforming in a high dynamic environment. Furthermore, a new CMT is proposed, which can flexibly control the broadening angle and null depth of each interference by trapping the covariance matrix of each interference, respectively. The simulation results show that when the array has the gain-phase errors, the difference between the algorithm output SINR and the ideal value is less than 1 dB; compared with the traditional null broadening beamforming, when there is a non-dynamic interference, it saves 2-3 DOFs. The disadvantage of this algorithm is that in the case of signal coherence, it is unable to separate signal steering vectors by blind source separation technology, resulting in a serious performance degradation. Therefore, the issue will be further studied in the future.  
Conceptualization, Y.L. and Y.Y.; methodology, R.L.; software, R.F. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Stable-Support Scientific Project of China Research Institute of Radiowave Propagation (Grant No. A131906W09).  
Not applicable.  
The authors declare no conflict of interest.  
The \(i\)-th interference rejection ratio \(\eta_{i}\) is defined as  
\[\eta_{i}=1-\frac{\hat{\sigma}_{io}^{2}}{\hat{\sigma}_{i}^{2}} \tag{1}\]  
The ratio  
* (14) [PERSON] Covariance matrix augmentation to produce adaptive array pattern troughs. _Electron. Lett._**1995**, _31_, 771-772. [CrossRef]
* (15) [PERSON] Production of adaptive array troughs by dispersion synthesis. _Electron. Lett._**1995**, _31_, 2141-2142. [CrossRef]
* (16) [PERSON] Theory and Application of Covariance Matrix Tapers for Robust Adaptive Beamforming. _IEEE Trans. Signal Process._**1999**, _47_, 985-997. [CrossRef]
* (17) [PERSON]; [PERSON]; [PERSON] Flexible robust adaptive beamforming method with multiple separately widened nulls. _Electron. Lett._**2020**, _56_, 957-959. [CrossRef]
* (18) [PERSON] Independent component analysis: A new concept. _Signal Process._**1994**, _36_, 287-314. [CrossRef]
* (19) [PERSON]; [PERSON] Blind beamforming for non-Gaussian signals. _IEE Proc. F (Radar Signal Process.)_**1993**, \(6\), 362-370. [CrossRef]
* (20) [PERSON]; [PERSON]; [PERSON] Gain-phase errors calibration for a linear array based on blind signal separation. _Sensors_**2020**, _20_, 4233. [CrossRef] [PubMed]
* (21) [PERSON]; [PERSON] Robust adaptive beamforming based on interference covariance matrix reconstruction and steering vector estimation. _IEEE Trans. Signal Process._**2012**, _60_, 3881-3885.
## 3 Results and Discussion
### Sorption Study
#### 3.1.5 Effect of Temperature  
The temperature effect on the adsorption of yttrium ions was conducted in a temperature varying in the range of 25-55 \({}^{\circ}\)C. All the experiments were executed at pH value 6.0, 50 mL of liquid solution with initial concentration 200 mg\(\cdot\)L\({}^{-1}\) of yttrium ions, and 50 mg amount of AHIBC for 60 min constant time. Figure 1e shows that the adsorption efficiency for AHIBC decreased from 85.66 to 82.70% when the temperature increased to 55 \({}^{\circ}\)C.  
The increasing temperature led to the decomposition of organic solvent; hence, the active sites were decreased. The result pointed out to exothermicity chemical reaction. Consequently, the room temperature was considered ideal for the yttrium ions extraction using the AHIBC adsorbent.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**adsorbent Materials**} & **Adsorption** & \multirow{2}{*}{**References**} \\  & **Capacity, mg\(\cdot\)g\({}^{-1}\)** & \\ \hline Macroalga (Turbinaria conoides) & 151.6 & [16] \\ \hline Polysulfone immobilized Turbinaria & & \\ conoides (PTIC) & 157.9 & [17] \\ \hline
2-amino-5-guanidinopentanoic acid modified & & \\ activated carbon (AGDPA@AC) & 219.9 & [19] \\ \hline Polyethylenimine (PEI)-scaffolded and & & \\ functionalized graphene aerogel (PEI-GO-AG) & 38.17 & [21] \\ \hline Carbonized Spent Bleaching Earth (CSBE) & 53.91 & [30] \\ \hline Nano-composite cation exchanger sodium dodecyl & & \\ sulfate acrylamide Zr(IV) selenite (SDS-AZS) & 21.01 & [37] \\ \hline AHIBC & 171.32 & This study \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of adsorption capacity of different sorbent materials.
# Cervical Cell Segmentation Method Based on Global Dependency and Local Attention
## 3 Proposed Method
### Global Dependency Module  
Both convolutional and recurrent operations are constructive blocks that deal with one local neighborhood at a time. To capture global dependency in image data, for example, the common practice is to stack convolutional layers, and as the layers deepen, the perceptual field becomes larger and larger. It is possible to incorporate the originally non-adjacent pixel points into an overall consideration, and the breadth of the acquired information distribution becomes higher and higher. However, as the network deepens, a series of problems come along with it, such as model degradation, gradient disappearance or explosion, and difficulty in network training. In order to obtain global context information while avoiding these problems, the global dependency module in this study refers to the simplified version of the Non-local Block (NL Block) mentioned in [38]. Denote \(X=\{X_{i}\}_{i=1}^{N_{p}}\) as the feature map of one input instance, where Np is the number of positions in the feature map. \(N_{p}=H\times W\). GD module can be expressed as:  
\[F_{G_{i}}(X_{i})=W_{p}\sum_{j=1}^{N_{p}}\frac{\exp{(W_{k}X_{j})}}{\sum_{n=1}^{N _{p}}\exp{(W_{k}X_{n})}}X_{j} \tag{2}\]  
where \(i\) is the index of the query location and j enumerates all possible locations. \(W_{p}\) and \(W_{k}\) represent the linear transformation matrices, and \(X\) and \(F_{G}\) represent the input and output of the GD module, respectively. As shown in Figure 3B, the input feature mapping utilizes the Embedded Gaussian [37] to construct global context information. Specifically, the feature weight map is obtained using \(1\times 1\) convolution \(W_{k}\) and the softmax function, and all query locations share an attention map to obtain global context features, which will be \(1/(H\times W)\) of the original computational complexity compared to computing the attention map [40] for each point on the feature map, enabling it to be used as the network base component to be embedded in different network layers. Subsequently, the global context features are feature transformed using \(1\times 1\) convolution \(W_{v}\), and finally the global context features are aggregated to each query position through element-wise addition operation.
# Height Control System for Wind Turbines Based on Critical Wind Speed Calculation
###### Abstract  
The increasing frequency of wind turbine failures due to extreme weather conditions necessitates the implementation of new solutions to enhance their operational reliability. This paper presents an automatic rotor drop system specifically designed for wind turbines equipped with the Onipko rotor. The system aims to protect turbines from damage caused by critical wind speeds, reducing maintenance costs and extending the equipment's lifespan. The unique design of the Onipko rotor allows it to operate at wind speeds as low as 0.1 m/s. However, its high drag coefficient and lack of aerodynamic optimization make it susceptible to mechanical stress and structural instability under strong gusts, requiring additional protective measures. The paper presents a calculation of the critical wind speed at which protective measures must be initiated. Through mathematical modeling, this study demonstrates the effectiveness of the rotor drop system in ensuring safe operation at wind speeds reaching 23.5 m/s. The optimization of the PI controller parameters provides a rapid response and stability, significantly enhancing the resilience of wind turbines to adverse weather conditions.  
operational reliability; failure frequency; critical wind speed; Onipko rotor; automatic system; PI controller optimization
## 5 Security Verification
### Informal Security Analysis
Resistance of Impersonation Attacks  
Suppose an attacker tries to imitate a legitimate UE, it must have the UE's group private key \(gsk_{i}\), batch private key \(buk_{i}\), or roaming parameters in order to generate a valid signature. However, these private keys are only held by the UE and NCC. It is difficult for the attacker to obtain both private keys. The LEO's private key is only held by the LEO and NCC. If the attacker uses the wrong private key signature, the UE will verify the signature and the validation will fail. In the roaming authentication phase, the attacker needs to obtain the \(psk\), modify the information in the token, and send the token to the UE through the established secure channel to complete the attack. This is also difficult for the attacker. Resistance to Man-in-the-Middle Attacks  
An attacker attempting a man-in-the-middle attack attempts to intercept the communication between the UE and LEO and imitate the other party in the conversation. However, due to the resistance of the protocol to impersonation attacks, it is difficult for the attacker to successfully achieve this goal, and they therefore cannot complete man-in-the-middle attacks. Resistance to Dos Attacks  
The traditional batch authentication protocol does not support a reauthentication algorithm. Therefore, when an attacker launches a DoS attack, the UE of the whole group cannot perform batch authentication. Thus, the LEO cannot know the specific UE who implemented the DoS attack. The batch authentication scenario of the proposed scheme supports the rebatch process. A legitimate UE can pass the authentication without re-authentication, and the LEO can also find the illegal UE that launched attacks. When the number of illegal operations performed by a UE reaches the threshold, the NCC can revoke them.
# The Story of an Egyptian Cat Mummy Through CT Examination +
## 3 Materials and Methods
### CT Scanning  
Given the mummy's fragility and cultural importance, invasive methods like unwrapping were ruled out to protect its structural integrity. Instead, a non-invasive approach using a Siemens SOMATOM go. Top CT scanner was chosen to examine the mummy's internal composition without causing any physical damage. This technique is particularly effective for mummy research, offering detailed insights into the internal morphology and condition of the specimen while preserving its conservation status.  
The scanning process started with the acquisition of a topogram to precisely determine the positioning of the specimen, serving as a guide for the subsequent detailed imaging. Then, a high-resolution helical scan was conducted with a 1 mm slice thickness, with a spiral pitch factor of 0.8, using a peak tube voltage of 70 kVp and a tube current of 58 mA, along with a Br64f/3 convolution kernel, which captured comprehensive data on the internal structures of the mummy.  
The CT scanner generated a DICOM-format dataset comprising 512 \(\times\) 512 pixel 2D slice images, providing a detailed visualization of both the skeletal and soft tissue components. These images facilitated an in-depth investigation of the mummy's internal structures. Parallel orthogonal slices resulting from the CT scanning were used to build a 3D model using AVIZO 3D Pro 2022 software (Thermo Fisher Scientific Inc., Waltham, MA, USA), enabling further elaborations. A semi-automatic threshold-based segmentation was used to separate some of the bones from the bandages and soft tissues [38; 39; 40]. Measurements were recorded using AVIZO software on 3D models of the bones following [PERSON] (1979) [41]. The sagittal reconstruction (Figure 2a) clearly outlines the skeletal features, offering a non-invasive approach to assess the preservation of the bones and detect any potential anomalies or pathological conditions. Additionally, the three-dimensional volume-rendered (VR) model (Figure 2b) offers a comprehensive reconstruction of the mummy, enabling the examination of the wrapping patterns and the identification of any artifacts placed inside during the mummification process. The age at death was established considering the epiphyseal fusion and teeth's eruption, following available data in the literature [42; 43; 44; 45].  
Figure 1: The cat mummy currently exhibited at the _Museo Entologico Missionario di San Francesco di Fisole_ (Florence, Italy), measuring 38 cm in length and 10.3 cm in width.  
Figure 2: (a) CT scan (MRP sagittal reconstruction) and (b) three-dimensional reconstructed model of the cat mummy.  
These high-resolution CT images prove invaluable for studying the preservation techniques utilized by ancient Egyptian embalmers. They not only provide insight into the materials and methods used but also allow comparisons with other animal mummies from similar time periods and regions. This enriches our understanding of the role animals played in ancient Egyptian religious and burial practices.
## 2 Materials and Methods
### Structural and Compositional Analysis of RH  
The surface features of RH were observed. SEM-EDX (FE-SEM; field-emission scanning electron microscope, HITACHI S-4800/Horiba EX-250, Japan) was used to study the distribution of silica in raw RH. The compositional constituents of the raw and Fr. RH were subjected to a two-stage acid hydrolysis process, which is an analytical procedure standardized by the National Renewable Energy Research Laboratory, USA, NREL/TP-510-42618 (structural carbohydrates and lignin) [23]. Glucose and hemicellulose sugars (xmg: xylose + mannose + galactose) in the Fr. hydrolysate were analyzed by high-performance liquid chromatography (HPLC; LC-10A, Shimadzu Inc., Kyoto, Japan) with an RI detector (RID-10A, Shimadzu Inc., Kyoto, Japan) according to the NREL/TP-510-42623 (sugars in the liquid fraction) [24]. The analytical column used was an Aminex HPX-87H (300 mm \(\times\) 7.8 mm) (Bio-Rad Lab. Inc., Hercules, CA, USA). The operating conditions for the HPLC column were 65 \({}^{\circ}\)C and a mobile-phase (sulfuric acid) flow rate of 0.6 mL/min. Hemicellulose is denoted by XMG (capitalized), which refers to the sum of three oligomeric sugars (xylan, mannan, and galactan), while fractionated hemicellulose sugars are denoted by xmg (uncapitalized), which represents the sum of the hydrolyzed sugars (xylose, mannose, and galactose). The extractives in raw RH were analyzed using a two-step extraction process to remove water soluble and ethanol soluble materials in biomass according to the NREL/TP-510-42619 (Determination of Extractives in Biomass) [25]. Structural ash is inorganic material that is bound in the physical structure of the biomass was also analyzed according to the NREL/TP-510-42622 (Determination of Ash in Biomass) [26].
# Trajectory Prediction of Marine Moving Target Using Deep Neural Networks with Trajectory Data
## 6 Conclusions  
In this paper, a trajectory prediction model based on multi-dimensional trajectory features and a CNN model structure is proposed aimed at the characteristics of space-based observation data, which can process sparse and long non-uniform time series trajectory data without an interpolation algorithm for uniform sampling and is adaptive to prediction requirements of hour-scale variable time intervals.  
Because of the correlation of ship movement state in the time domain, based on the local correlation perception and weight-sharing characteristics of CNN, this paper innovatively applies the CNN model to the trajectory prediction problem. In order to exploit the ship's motion characteristics to the greatest extent from the raw trajectory data, multi-dimensional features extraction is carried out to construct the multi-dimensional trajectory feature matrix as the input of the network model, and the time-space correlation characteristic of the track data is representant and mined. To the best of our knowledge, this is the first study in which local connection characteristics of the CNN have been applied to the time domain to solve ship trajectory prediction problems. By carrying out experiment and method comparison, it shows that the method can achieve better prediction accuracy and adaptability for the sparse non-uniform trajectory data.  
Research on the trajectory prediction of maritime moving targets for satellite resource scheduling is of great application value; one of the difficulties is the uncertainty of maritime moving targets. The trajectory prediction algorithm can decrease the uncertainty of maritime moving targets in order to reduce the blind search when making the satellite search plan, guide the satellite observation of the target, and greatly improve the efficiency of target search.
## 2 CLARA WebGIS: Data Sources
### CLARA Data
#### 2.3.2 Digital Surface Model from Satellite Data
The results of the overall DSM validation are shown in Table 2. The DSM shows completeness equal to 98.42%, evaluated as the ratio (percentage) of the filled DSM pixels over the number of pixels of the reference raster. It is worth noting that the errors in the semi-natural class can derive from the missing reconstruction of the vegetation in the reference DSM since it was generated from a DTM (Figure 9). Moreover, the 'Artificial Surfaces' and 'Buildings' classes are the most critical due to the high urban density, which makes the DSM production from satellite images very challenging because of occlusion issues (Figure 10). For these two classes, the RMSE reaches the highest values, being slightly higher than 3.5 m. However, in this case, the values of median and NMAD, which are significantly lower than the mean and standard deviation, denote the presence of outliers. As mentioned, most are probably related to occlusion issues; however, careful inspection showed that some outliers could be due to rather coarse simplifications inside the 'unita volumetrica' shapefile, which was used to generate the reference for the buildings. In fact, some complex buildings (one relevant example is the castle; Figure 9) were represented with just one unique height. Therefore, it is possible to improve the building heights in RSDI using the generated DSM where outliers were highlighted. Finally, Figure 10 shows a section extracted from the reference (in black) and the generated DSM (in red); as indicated in the lower left and lower right corners, the profile direction is SW-NE (Figure 8). Even if the resulting DSM is noisier than the reference, the multi-view approach allows for the accurate and dense reconstruction of the terrain and all the objects within it (buildings--visible in the reference--and vegetation--not included). \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Tile** & **Mean** & **Std. Dev.** & **RMSE** & **Median** & **NMAD** & **LE68** & **LE90** & **Number of** \\  & **[m]** & **[m]** & **[m]** & **[m]** & **[m]** & **[m]** & **Pixels** \\ \hline Overall & \(-1.1\) & 2.4 & 2.7 & \(-0.8\) & 1.1 & 1.2 & 3.0 & 119,865,099 \\ \hline Artificial surfaces & \(-1.3\) & 3.7 & 3.9 & \(-0.6\) & 1.5 & 2.0 & 5.9 & 34,110,194 \\ \hline Agricultural areas & \(-0.8\) & 1.6 & 1.8 & \(-0.8\) & 0.9 & 0.9 & 1.8 & 68,037,393 \\ \hline Semi-natural areas & \(-1.8\) & 1.9 & 2.6 & \(-1.4\) & 1.0 & 1.2 & 2.7 & 17,717,512 \\ \hline Buildings & 1.2 & 3.4 & 3.7 & 0.8 & 2.3 & 2.6 & 5.4 & 8,110,798 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Statistical indicators for DSM accuracy assessment.