esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Modeling
### Dynamic Modeling
Thus, it is advisable to divide the model parameters into two parts: the normal value part and the bias part. The normal value is denoted as \((\cdot)^{*}\), which can be obtained through using strip theory, pool experiment analysis or CFD computation. The bias term is denoted as \(\Delta(\cdot)\), which describes the difference between the real value and the nominal value. Then, (4) can be obtained. For control design, the normal values are available, while the bias parts are considered as model parameter uncertainties. \[\mathbf{M}=\mathbf{M}^{*}+\Delta\mathbf{M},\ \ \mathbf{C}=\mathbf{C}^{*}+\Delta\mathbf{C},\ \ \mathbf{D}=\mathbf{D}^{*}+\Delta\mathbf{D} \tag{4}\]  
Considering that the vehicle is driven by thrusters and the manipulator is driven by motors, the generalized force vector \(\mathbf{\tau}\) is related to the vector of thruster forces and actuator torques \(\mathbf{F}_{td}\) through (5). \[\mathbf{\tau}= \begin{bmatrix}\mathbf{B}_{\
u}&\mathbf{0}_{6\times n}\\ \mathbf{0}_{n\times p_{\
u}}&\mathbf{I}_{n}\end{bmatrix} \mathbf{F}_{td}=\mathbf{B}\mathbf{F}_{td} \tag{5}\]  
where \(\mathbf{F}_{td}=[\mathbf{T}^{T},\mathbf{\tau}_{m}^{T}]^{T}\in\mathbb{R}^{p_{\
u}+n}\). \(\mathbf{T}\in\mathbb{R}^{p_{\
u}}\) represents the vector of thruster forces, and \(\mathbf{\tau}_{m}\in\mathbb{R}^{n}\) represents the vector of actuator torques. \(\mathbf{B}_{\
u}\in\mathbb{R}^{6\times p_{\
u}}\) is the thruster configuration matrix, and \(\mathbf{B}\in\mathbb{R}^{(6+n)\times(6+n)}\) is the thruster-actuator configuration matrix. It is known that for an under-actuated underwater vehicle, \(p_{\
u}<6\). Generally, for a manipulator, \(n\) joint motors are all available.
## 3 Materials and Methods  
### Manufacturing Kefir  
Both types of kefir were manufactured in a small dairy plant, using cow milk from Polish Holstein Friesian and Siementhal breeds of high microbiological and cytological quality derived from local farms located in the Mazovian region in Poland. Two types of kefir were manufactured from the same three independent parts of milk, standardized to 1.50% fat with a cream separator, and homogenized, one part of milk being subjected to the enzymatic process to decompose lactose by \(\beta\)-galactosidase activity from _Kluyveromyces lactis_ of the commercial enzymatic preparation GODO-YNL2 Lactase (DuPontTM Danisco A/S, Brabrand, Denmark); the reaction lasted 24 h at 36 \({}^{\circ}\)C. Next, milk was pasteurized at 92 \({}^{\circ}\)C for 7 min, cooled rapidly down to 23 \({}^{\circ}\)C, and a lyophilized starter culture containing _Lactobacillus_ spp., _Lactococcus_ spp., _Lactococcus_ spp., _Streprococcus thermophilus_, _Lactobacillus rhamnosus_, and kefir yeast (DuPontTM Danisco A/S, Brabrand, Denmark). Kefir starter culture was added to milk at the level 0.025 g/L. The inoculated milk was distributed into polyethylene bottles and incubated in a thermostatically controlled chamber at 22-23 \({}^{\circ}\)C, the fermentation lasting 18 h. Samples were stored at 4 \({}^{\circ}\)C. From the second part of pasteurized milk, traditional kefir was manufactured using the same technological parameters and the same starter culture as in the case of lactose-free kefir. The kefirs were manufactured in three independent trials, 80 L each batch. Samples of both types of kefir were stored for 7 days and taken for analysis.  
All chemical and microbial analyses were performed in triplicate for each batch. Sensory assessment by an experienced panel was conducted in three sessions, while consumer evaluations by elderly subjects were in two independent ones.  
Figure 2: Distribution of individual preferences of lactose-free kefir (n = 256). 9—Like extremely; 8—Like very much; 7—Like moderately; 6—Like a little; 5—Neither like nor dislike; 4—Dislike a little; 3—Dislike moderately; 2—Dislike a lot; 1—Dislike extremely.
# Correlation between Soil Moisture Change and Geological Disasters in E'bian Area (Sichuan, China)
## 3 Results
Although vegetation and rainfall have a positive impact on soil moisture, studies indicate that the surface water content has not surpassed the critical threshold of negative correlation. This implies that after a certain point, the increase in moisture may no longer have a positive effect on vegetation growth and could even trigger waterlogging disasters and soil erosion. Another innovative aspect of this paper is the proposal of the correlation between the negative correlation threshold of surface water content and the development of geological disasters, which is also a key factor in the subsequent geological disaster assessment. The negative correlation threshold of surface water content is not a fixed value but is influenced by multiple factors such as soil type, vegetation cover, and climatic conditions. When the surface water content exceeds a specific negative correlation threshold, human engineering activities may accelerate the expansion of surface cracks, further promoting the deeper infiltration of surface water. However, this infiltration phenomenon may have the opposite effect, leading to a decrease in the moisture content of the soil surface layer, directly threatening the healthy growth environment of vegetation. Once the surface water content surpasses this critical threshold, excessive rainfall makes it difficult for the soil to rapidly absorb or effectively discharge into the water system, leading to a rapid rise in water levels and surface water accumulation, which could ultimately trigger flood disasters. Simultaneously, the stability of the soil structure decreases, creating a hidden danger for the occurrence of geological disasters such as debris flows. Under specific conditions, rainfall or other geological activities can easily trigger large-scale soil and rock slides, forming highly destructive debris flows that pose severe threats to surrounding areas. Additionally, the weakening of soil shear strength increases the risk of landslides and ground collapses, further complicating the prediction and accuracy of geological disasters. Given these potential risks, continuous monitoring of surface vegetation water content becomes particularly important. It not only allows for the assessment of vegetation growth and soil moisture levels but also serves as a critical basis for predicting geological disasters and formulating geological disaster prevention and control strategies.
# Improvement of Position Repeatability of a Linear Stage with Yaw Minimization
## 3 Factors Affecting the Yaw Motion
### Uncontrollable Parameters: Given Conditions
#### 3.1.2 Force Offset  
The force offset (\(FO\)) may be defined in terms of the distance between the ball screw linear actuator, illustrated in Figure 1, and the geometric center of the moving platform. In its normalized form, the force offset is defined as  
\[FO(\textit{ForceOffset})=\frac{d}{w/2}\times 100\ (\%) \tag{13}\]  
where \(d\) is the offset distance and \(w\) is the width of the platform. Figure 9 illustrates the parameters for \(FO\) and examples of different \(FO\) configurations.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No.** & \begin{tabular}{c} **Length, \(l\)** \\ **(m)** \\ \end{tabular} & \begin{tabular}{c} **Width, \(w\)** \\ **(m)** \\ \end{tabular} & \
\begin{tabular}{c} **Weight** \\ **(kg)** \\ \end{tabular} &
\begin{tabular}{c} _Iw_ \\ _Iw_ \\ \end{tabular} \\ \hline
1 & 0.5 & 0.3 & 29.3 & 1.7 \\
2 & 0.5 & 0.5 & 48.8 & 1.0 \\
3 & 0.5 & 0.8 & 78.0 & 0.6 \\
4 & 0.5 & 1.0 & 97.5 & 0.5 \\ \hline No.1 & No.2 & No.3 & No.4 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Dimensions of moving platform.  
Figure 8: Comparison of moving platforms.  
Figure 10 shows the yaw vibration of the No. 4 platform (Figure 8) driven with four different values of \(FO\). The vibration response of the moving platform in Figure 10 is typical for the trapezoidal motion profile [23]. The yaw angle is observed to converge after a certain period of time, indicating that the higher the \(FO\), the higher the convergence value becomes.  
Figure 11 presents the convergence value of the yaw angle vs. \(FO\) value for the four platform types, which demonstrates that the larger the \(l\)/\(w\) value, the smaller the \(FO\) effect on the yaw angle.  
Figure 10: Yaw angle vs. time for various force offsets (No. 4).  
Figure 9: (**a**) Schematic diagram of \(FO\) definition and (**b**) comparison of \(FO\) 20% and \(FO\) 80%.  
Figure 12 is an enlarged graph of a \(FO\) of 80% in Figure 10. The settling time (\(\lambda\)) is when the vibration value becomes less than the error band, \(0.1\times 10^{-6}\) rad, about 33 ms.
# A Novel Geo-Localization Method for UAV and Satellite Images Using Cross-View Consistent Attention
## 2 Related Works
### UAV to Satellite Geo-Localization  
Geo-localization from drones and satellites has also gained attention in recent years. The methods based on CNNs are first studied to match the images between drones and satellites. [PERSON] et al. [13] proposed a VGG-16 [36] baseline and the most commonly used benchmark, University-1652. [PERSON] et al. [37] used a heat map to find the similarity of cross-view images. [PERSON] [38] simplified the retrieval problem into a classification problem and considered the influence of the feature vector size on the matching accuracy. However, these methods indiscriminately extract all the information in the image, which causes noise information interference.  
LPN [9] introduced a network architecture based on ResNet [39], in which the square-ring partition approach was designed for rotation variations. Based on the LPN network, many methods were proposed to improve geo-localization accuracy. [PERSON] et al. [12] proposed the MSBA model based on LPN, applying self-attention to promote feature extraction performance. [PERSON] et al. [11] introduced the Unit Subtraction Attention Module (USAAM) to the LPN network to focus on salient regions of images. PCL [10], based on the LPN network, utilized perspective projection transformation and CGAN [40] to increase geometric consistency and color consistency. However, these LPN-based methods rely on the manual square-ring partition ratio and lack the universality of practical applications.  
Since drone and satellite images have more geometric invariant information than ground and satellite images, Vision Transformers perform better for drone and satellite geo-localization due to their ability to extract contextual information. [PERSON] et al. [15] introduced a Transformer-based structure FSRA and divided regions based on another Transformer-based classifier. This network needed two Transformer models--the image dividing Transformer model and the image retrieving Transformer model--increasing the complexity of training.
## 2 Methodology
### Definition of the Objective and Scope of the Study  
The objective of the present work has been to provide information on the environmental consequences of the brick production system from cradle to gate by comparing bricks produced in the traditional way with bricks in which certain amounts of clay have been substituted by olive pomace (3, 7, and 10% by weight), for a reference time period of 100 years, measuring their impact using the IPCC and CML methodologies. The cradle-to-gate approach analyzes the impact from the extraction of raw materials, the production of materials and product parts, and until the final product leaves the factory. The useful life and end-of-life stages are not the subject of study in this work. In this Life Cycle Analysis, the industrial manufacturing of 1 kg of traditional brick was compared to 1 kg of brick incorporating 3, 7, and 10% by weight of olive pomace for a period of 100 years. Approximate dimensions of the samples were 117 \(\times\) 28 \(\times\) 17 mm. The firing temperature defined in this analysis is 850 \({}^{\circ}\)C because the bricks fired at these temperatures presented the best technological and thermal properties in previously developed works [23]. \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Elementary Flow** & **Units** & **0\%** & **3\%** & **7\%** & **10\%** & **LCIA Dataset** \\ \hline \multicolumn{6}{c}{_Raw materials_} \\ Clay & kg & 1.11 & 107.48 & 103.04 & 0.9972 & Clay [GLO] market for [Alloc Def, U \\ Olive pomace & kg & - & 0.03324 & 0.07756 & 0.1108 & \\ Water & m\({}^{3}\) & 0.000736 & 0.000736 & 0.000736 & 0.000736 & Water, well, in ground, BS \\  & kg & 0.0072 & 0.0272 & 0.0272 & 0.0272 & Tap water [GLO] market group for [Alloc Def, U \\ Limestone, crushed, for mill & kg & 0.000396 & 0.000396 & 0.000396 & 0.000396 & Limestone, crushed, for mill [GLO] market for [Alloc Def, U \\  & & & & & & \\  & & & & & & \\  & & & & & & \\  & & & & & & \\  & & & & & & \\  & & & & & & \\  & & & & & & \\ \end{tabular}
\end{table} 
Table 1: Inventory data adjusted to \(1\) kg of bricks for the different phases analyzed: Raw materials; Extraction and transport; Manufacturing (energy and material inputs at bricks manufacturing plant) and Manufacturing (direct emissions associated with upstream processing).
# Temperature Load Mode of Bridge in Permafrost Region of Qinghai-Tibet Railway
## 3 Test Result
### Monitoring Results and Analysis of Beam Temperature  
Temperature monitoring was carried out in the Tuotuohe and Za'gya Zangbo regions for a period of one year, and the sampling interval of the beam temperature monitoring system was set at 2 h. According to the measured beam surface temperature at different times, the daily beam temperature difference was calculated as shown in Figures 9 and 10.  
As can be seen from Figure 11, the beam end displacement changes periodically with the daily air temperature, but lags behind the air temperature for about 2 h, which is related to the poor thermal conductivity of the concrete. The displacement of the beam end changes greatly in spring, and the maximum daily displacement is 2.89 mm (16 March). During the observation period, the cumulative relative displacement of the beam and rail is 1.18 mm, and the maximum daily variation is 1.43 mm (25 January). The cumulative change in beam gap width is 7.00 mm, and the displacement continues to increase with time. This monitoring result provides strong evidence for the frequent occurrence of direct contact of adjacent beams, bridge bearing failure, and other damage in the permafrost region of the Qinghai-Tibet railway. The reason for the damage to these bridges is that the daily temperature difference in this area is large, resulting in frequent telescopic displacement of the beam, and the performance of the bearing cannot meet the requirements of frequent displacement of the beam.
## 4 Diagnosis Result and Discussion
### Diagnosis Result  
In the case of Test 1, the confusion matrix results of the inner test set and the external test set of the feature family extracted from the four GIS vibration information are compared, as shown in Figure 8, wherein the horizontal axis represents the real category number and the vertical axis represents the predicted category number. It can be seen from Figure 8 that the internal test set of the four feature families has good diagnostic results, and the diagnostic accuracy is above 88%. The feature-extraction method based on the MFER (characteristic groups 3 and 4) is obviously superior to the method of extracting amplitude of the vibration spectrum (features 1 and 2). In the diagnosis results of the external test set in Figure 8, the diagnostic accuracy of feature sets 1-4 are 62.92%, 40.12%, 65.58%, and 81.42%, respectively, and the diagnostic accuracy of Class 1 is 89.25%, 98.25%, 100%, and 49%. Class 2 has a diagnostic accuracy of 0%, 9.25%, 45.75%, and 95.25%. Class 3 has a diagnostic accuracy of 99.5%, 13.75%, 50%, and 100%. It can be seen from the results of Figure 8 that the feature-extraction method based on the octave energy ratio (feature sets 3 and 4) can effectively reduce the influence of different current levels compared to the conventional feature description method based on the GIS vibration spectrum amplitude, and improve the applicability of the diagnostic model. At the same time, the MFER method (feature set 4) proposed in this paper to remove the influence of excitation frequency can further improve the diagnostic performance, and the diagnostic accuracy for each category is more balanced, reducing the possibility of over-fitting.  
In order to further compare the performance of the proposed method, the diagnostic results of Tests 1-6 were analyzed. In each test, the set An is randomly divided into 10 equal parts, each of which is used as an inner test set, and the union of the remaining 9 subsets is used as a training diagnostic  
Figure 8: Prediction results for the internal and external test sets with the diagnosis model based on 500 A current data set: (**a**) internal testing; (**b**) external testing.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Test Number** & **Training Set/Internal Testing Set** & **External Testing Set** \\ \hline Test 1 & An = [500 A] & Bm = [1000 A, 1500 A] \\ Test 2 & An = [1000 A] & Bm = [500 A, 1500 A] \\ Test 3 & An = [1500 A] & Bm = [500 A, 1000 A] \\ Test 4 & An = [500 A, 1000 A] & Bm = [1500 A] \\ Test 5 & An = [500 A, 1500 A] & Bm = [1000 A] \\ Test 6 & An = [1000 A, 1500 A] & Bm = [500 A] \\ \hline \hline \end{tabular}
\end{table}
Table 3: Training and testing data set.
###### Abstract  
The Sentinel-2 constellation has been providing high spatial, spectral and temporal resolution optical imagery of the continental surfaces since 2015. The spatial and temporal resolution improvements that Sentinel-2 brings with respect to previous systems have been demonstrated in both the literature and operational applications. On the other hand, the spectral capabilities of Sentinel-2 appear to have been exploited to a limited extent only. At the moment of definition of the new generation of Sentinel-2 satellites, an assessment of the usefulness of the current available spectral bands seems appropriate. In this work, we investigate the unique information contained by each 20 m resolution Sentinel-2 band. A statistical quantitative approach is adopted in order to yield conclusions that are application agnostic: multivariate regression is used to reconstruct some bands, using the others as predictors. We conclude that, for most observed surfaces, it is possible to reconstruct the reflectances of most red edge or NIR bands from the rest of the observed bands with an accuracy within the radiometric requirements of Sentinel-2. Removing two of those bands could be possible at the cost of slightly higher reconstruction errors. We also identify mission scenarios for which several of the current Sentinel-2 bands could be removed for the next generation of sensors.  
spectral bands; Sentinel-2; regression; spectral band reconstruction; spectral band selection Article Assessment of the Usefulness of Spectral Bands for the Next Generation of Sentinel-2 Satellites by Reconstruction of Missing Bands  
[PERSON]; [PERSON]; [PERSON] Assessment of the Usefulness of Spectral Bands for the Next Generation of Sentinel-2 Satellites by Reconstruction of Missing Bands. _Remote Sens._**2022**, _14_, 2503. [[https://doi.org/10.3390/rs14102503](https://doi.org/10.3390/rs14102503)]([https://doi.org/10.3390/rs14102503](https://doi.org/10.3390/rs14102503))  
Ascepted: 19 May 2022  
Published: 23 May 2022
# Gold Nanoparticles and Nanorods in Nuclear Medicine: A Mini Review
## 4 Gold Nanoparticle and Nanorods: Clinical Aspects
### Monoclonal Antibody-Labeled AuNPs
#### 4.5.1 Radionuclide \({}^{89}\)Zr  
\(\beta\)+ emitter. Cetuximab was radiolabeled with \({}^{89}\)Zr and conjugated to AuNPs with a coupling reaction yield \(>\)75%. Agents were more stable in vitro than in plasma. Evaluation in nude mice bearing human epithelial carcinoma xenografts (A431) revealed no significant difference in terms of tumor uptake for cetuximab conjugated to AuNPs up to 72 hours after the administration, compared to the non-conjugated antibody. Immuno-PET imaging showed a high tumor-to-background _ratio_[88].
# A Review of Transformer-Based Approaches for Image Captioning
## 6 Discussion and Analysis
### Major Contributions  
Table 3 gives a summary of the major contributions of the various transformer-based captioning methods that do not use vision-language pre-training. The Conceptual Captions transformer [59] is one of the earlier models that employed a transformer for captioning, shortly after the transformer was pioneered [36]. It had an associated dataset released (dubbed Conceptual Captions) which contains an order of magnitude more images than the MS-COCO dataset.  
Table 4 shows the major contributions of the methods that employ vision-language pre-training. It is interesting to note that the inherent design of the VLP transformers facilitated their application to multiple downstream vision-language tasks, resulting in very versatile models. VLP generally requires large amounts of data so a number of large-scale datasets associated with some of the models were created.  
\begin{table}
\begin{tabular}{l p{142.3 pt}} \hline \hline \multicolumn{1}{c}{**Model**} & \multicolumn{1}{c}{**Major Contributions**} \\ \hline Conceptual Captions transformer [59] & A novel dataset (Conceptual Captions) which contains an order of magnitude more images than the MS-COCO dataset. \\ \hline Stacked attention transformer [77] & Multi-level supervision that utilizes the outputs of multiple transformer layers to generate output tokens and calculate the loss. \\ \hline Object relation transformer [79] & Object relation transformer (using geometric attention) that incorporates the spatial relationships between various objects. \\ \hline Attention on Attention [84] & Attention on Attention module, which doubly applies attention to determine object relationships and filter out irrelevant attention results. \\ \hline Entangled Transformer [85] & Entangled attention, which exploits semantic and visual information; Gated Bilateral Controller to control visual and semantic interactions. \\ \hline Meshed-Memory Transformer [86] & Mesh-like multi-level structure to capture high-level and low-level image region relations. \\ \hline X-Linear Attention Networks [87] & Unified X-Linear attention block that models the second-order interactions with both spatial and channel-wise bilinear attention. \\ \hline Image Transformer [90] & A modified attention module suited to the complex natural structure of image regions. \\ \hline COS-Net [91] & A semantic comprehender that grasps and orders the rich semantics in images to generate linguistically coherent captions. They use the x-modaler codebase. \\ \hline \hline \end{tabular}
\end{table}
Table 3: Major contributions of the non-VLP transformer-based models.