esa-sceva/satcom-chunk-collection huggingface dataset:
# C2B: A Semantic Source Code Retrieval Model Using CodeT5 and Bi-LSTM
## 5 Experimental Results
### Results and Discussions
#### 5.4.2 Experiment 2
For every query, the original CodeT5 architecture runs through all the existing code in the dataset, concatenates the query tokens with the existing codes one by one, and produces a similarity score. Further, the codes in the dataset are ranked in descending order according to their similarity score. Since the architecture runs for n times (n represents total codes in the corpus) for each query it ends up consuming a lot of time. However, in our case, we run the fine-tuned proposed C2B model once for every query to extract its embeddings and then use cosine similarity for comparing the embeddings. * Accuracy: Figure 8 depicts the accuracy of both models in response to n queries. We define accuracy as a fraction of total queries containing the true codes in the top k retrieved code snippets by the model. We can observe from the figure that the performance of both models is comparable. There exists a drop in the accuracy of our proposed C2B model when compared to the CodeT5 model. This drop can be attributed to two factors: (i) We truncate the maximum code token length to 128, which is way below the 400-token limit used in the original CodeT5. (ii) In the original CodeT5 architecture, the model obtains both codes as inputs. This is beneficial as the model is able to capture the interrelation between the codes. However, in our case, the fine-tuned model generates embeddings by looking at the codes independently with respect to NL query embeddings. Note that for the inference phase, code snippets corresponding to the provided natural language query were selected from the dataset. As depicted in Figure 8, it is evident that the average accuracy of our proposed C2B model for NL query and code snippet retrieval is approximately 0.93, while the average accuracy of the original CodeT5-based retrieval model stands at around 0.96. From this, we can infer that our proposed C2B retrieval system exhibits comparable accuracy and can retrieve relevant code snippets efficiently, reducing retrieval time. Figure 7: Total time taken to answer n queries. (**a**) CodeT5 time for n queries; (**b**) C2B hybrid model time for n queries.
# Application of Image Fusion in Diagnosis and Treatment of Liver Cancer
## 4 Imaging Fusion Algorithms
### Deep Learning(DL) Methods
#### 4.10.1 Convolutional Neural Networks  
The popular deep learning model, convolutional neural networks, can provide some new way in image fusion. Convolutional Neural Networks (CNNs) are able to extract the most important features from a large number of samples. The CNN uses a system much like a multilayer perceptron that has been designed to reduce processing requirements. CNNs consist of an input layer, an output layer and a hidden layer that includes multiple convolutional layers, pooling layers, fully connected layers and normalization layers. A convolutional layer defines multiple filters as a window and subsequently scans the entire image through this window.  
It can output many feature mappings after training. The output can provide a multiscale and multiangle feature, it can also provide the location information of the feature. The advantages of feature extraction by CNN can be fully utilized in pixel or feature level image fusion. Another operation in CNNs is spatial pooling (max-pooling, min-pooling), which can bring some desirable invariances, including translation, rotation and scale into the model to a certain extent. Fully connected layers act as the role of a classifier. CNN overcomes the difficulty on manually designing complicated activity level measurement and fusion rules [144]. The activity level measurement and fusion rule can be jointly generated via training a CNN model. The feasibility of CNNs used for medical image fusion have already been proposed [145]. CNN can decompose the original images to high frequency and low frequency images [146], and select the rule of regional matching to fuse the two high frequency and low frequency images to get the final fusion images. [PERSON] et al. [147] developed a supervised CNN to learn to merge the data from PET-CT images of lung cancer. CNN has also been applied to fuse medical images MRI/CT, MRI/SPECT, multiparametric MR images [148] and PET/MRI [149]. CNN can also be combined with a wavelet transform for the fusion of CT and MR images [150]. In this method, the wavelet transform coefficients are first obtained by decomposing the input images. The next step is to use the trained CNN model to improve the high frequency coefficient's resolution. A similar procedure is applied to combine CNN with NSST to merge the CT and MR images [151].
###### Abstract
The proposal focuses on providing visualizations based on non-verbal communication metrics to support the observation of six collaboration constructs: cognitive contribution, assimilation, team coordination, self regulation, cultivation of environment, and integration. The platform collects audio and video data, and using machine learning techniques, extracts spoken interaction and body posture measurements that are then presented in a set of five visualizations. We performed a case study to assess whether the visualizations were valuable to provide insights about the six collaboration constructs. The perspective of this research is from the researchers' and teachers' point of view: the researchers and teachers would like to evaluate whether the provided visualizations correlate with the observed collaborative behavior. As we are focusing on face-to-face collaborative scenarios, the present research falls under the scope of the so-called collocated collaboration field [19; 20]. The results of this initial empirical evaluation show that the proposed platform allows observing most of the collaboration constructs, which would allow the teacher to identify if his or her intervention is needed in order to foster the collaboration of a team. The contribution of this article is twofold. On the one hand, we show a practical application on how to connect the theoretical and technical aspects of MMLA in collaboration. We use a theoretical collaboration framework [21] to derive the technical requirements of measurement, analysis, and data visualization for an MMLA tool to analyze nonverbal communication during co-located collaboration. On the other hand, we show the first empirical evidence on how the MMLA tool designed from the above requirements can help teachers observe collaboration constructs in practice. Despite the limitations of an initial exploratory study, the results confirm the feasibility of the MMLA approach to support teachers in assessing collaboration. The remainder of the article continues as follows. Section 2 discusses some relevant related work, structured in terms of types of non-verbal cues. Section 3 briefly introducesthe theoretical framework on which we base to elicit the requirements for the platform [17]. Section 4 presents the design and technical considerations of the proposed system. Then, Section 5 presents the case study along with the results. In Section 6 we discuss in the implications of the results in the observation of collaboration constructs. Finally, Section 7 presents our conclusions and discusses future work.
# ReDSHIFT: A Global Approach to Space Debris Mitigation
## 2 Dynamical Mapping
The passive and active solar sailing techniques were compared in [11] to determine the most efficient deorbiting strategy under a maximum deorbiting time constraint. An optimisation algorithm was implemented to find the best deorbiting parameter required (i.e., surface area and time to deorbit). Maps of the best deorbiting parameters for active/passive solar sailing were obtained. A third novel strategy for solar sailing has been proposed in the ReDSHIFT project, named as modulating solar sailing. The sail area is oriented perpendicular or at feather with respect to the Sun, with a period of about six months so that the eccentricity of the orbit is constantly increased until re-entry. It has been demonstrated that, with such a sail control, satellites up to the MEO region can be deorbited with a small sail area and a deorbiting time less than 25 years. As an example, Figure 9 shows the sail requirements for deorbiting in 25 years using the modulating solar radiation pressure strategy.
###### Abstract  
In this study, we investigated the effect of photobiomodulation (PBM) using near-infrared light on the dentin and periodontal ligament in a beagle model. We utilized a specific PBM device to irradiate NIR light with a wavelength of 810 nm and an energy density of 80.22 mJ/cm\({}^{2}\). The device's settings were optimized for a frequency of 300 Hz and a 30% duty cycle, allowing precise and controlled light exposure. Through a comprehensive analysis involving micro-computed tomography, scanning electron microscopy, and hematoxylin and eosin staining, we demonstrated increased odontoblast activity at the pulp-dentin interface in PBM-treated samples. This increased activity may be postulated to potentially contribute to alleviating dental hypersensitivity through the differentiation of dental pulp stem cells and the promotion of vascular development within the odontoblast layer. Moreover, our observations also indicated an improvement in the strength and integrity of fibrous connective tissue within the periodontal ligament. These findings highlight the potential of PBM with specific parameters applied using NIR as a valuable treatment method for tooth tissue regeneration. It shows particular promise in the treatment of dental diseases associated with dentin and periodontal ligament damage and offers a new perspective in the management of tooth hypersensitivity and other related dental diseases.  
regeneration; near-infrared; photobiomodulation; dentin; differentiation; periodontal ligament +
Footnote †: journal: Applied Sci.
## 4 Analysis of Attitude-Prediction Results
### Prediction Model Validation
SP52-SP56 and SP01-SP04 exhibited a weak correlation with FVD (\(P\) = 0.2-0.4). This shows that the shield propulsion system was an important factor that affected the attitude motion trajectory, and the deviation between the attitude and DTA can be adjusted by controlling the corresponding position of the propulsion cylinder pressure. In addition, the attitude motion trajectory of the shield-tunneling machine can be adjusted by controlling the deflection MY, the difference in up and down stroke, and the cut water pressure. The correlation between other driving parameters and output parameters was weak. In particular, the geometric and geological parameters of the shield were also strongly correlated with the attitude parameters. The shield attitude was mainly controlled by the field adjustment of the propulsion cylinder pressure and was considerably affected by its geometric and geological parameters.
## 4 Test Results and Mechanism Analysis
### Effect of Nano-SiO\({}_{2}\) on the FTIR Spectra of Soil Samples  
Figure 8 shows the FTIR spectra of the two samples. The peaks at 3620 and 3694 cm\({}^{-1}\) were associated with OH vibrations in crystal water or terminal hydroxyl group [30]. The peaks at 3363 cm\({}^{-1}\) were associated with OH vibrations in free water. The band with peaks at 987 and 911 cm\({}^{-1}\) were due to the antisymmetric stretching vibrations of Si-O-Si bonds [16]. The peaks at 794 and 777 cm\({}^{-1}\) were related to the symmetric stretching vibrations of Si-O-Si bonds, typical of quartz and silica [31]. As shown in Figure 8, the hydroxy terminal at 3620 cm\({}^{-1}\) was primarily consumed during the dehydration process of Si-O-Si. The consumption degree of the terminal OH in Si-OH can be analysed by calculating the peak strength ratio (I\({}_{3620}\)/I\({}_{3363}\)) between 3620 cm\({}^{-1}\) and free water at 3363 cm\({}^{-1}\). I\({}_{3620}\)/I\({}_{3363}\) was 1.77 in the unmodified soil sample and 1.02 in the modified soil sample. After adding nano-SiO\({}_{2}\), the relative content of I\({}_{3620}\)/I\({}_{3363}\) decreased significantly, indicating that the terminal hydroxyl in the soil was consumed owing to its participation in the reaction. By calculating the relative intensity of the absorption peaks, we can determine the degree of different absorption peaks involved in the reaction. The absorption peak at 911 cm\({}^{-1}\) corresponded with the newly generated Si-O-Si. The degree of newly generated Si-O-Si can be reflected by calculating the I\({}_{911}\)/I\({}_{987}\) of the peak strength of Si-O-Si at 911 and 987 cm\({}^{-1}\). I\({}_{911}\)/I\({}_{987}\) was 0.66 in the unmodified soil sample and 0.74 in the modified soil sample. The relative strength of I\({}_{911}\)/I\({}_{987}\) increased, indicating that Si-O-Si was newly formed after adding nano-SiO\({}_{2}\).  
Expansive soil contains a large amount of quartz. Infrared spectra showed a layer of free silanol group (Si-OH) on the fracture surface of quartz particles [15]. Nano-SiO\({}_{2}\) was considered to be an inorganic compound, containing a certain amount of surface silanols (Si-OH). In the modified soil, superhydrophobic nano-SiO\({}_{2}\) was uniformly dispersed and attached onto the surface of the soil particles. Silanols were easily dehydrated and condensed to form siloxanes. The reaction equation is shown in Equation (8).  
\[[\mathrm{Si}]-\mathrm{OH+[Si]-OH}\ \rightarrow[\mathrm{Si}]-\mathrm{O-[Si]} \tag{8}\]  
A simplified diagram of the reaction between soil and silica is shown in Figure 9. The figure shows the reaction between the silanols in silica and the silanols in soil.  
Figure 7: **(a)** The micropore radius of soil samples with different SiO\({}_{2}\) contents after hydraulic conductivity test, **(b)** the medium pore radius of soil samples with different SiO\({}_{2}\) contents after hydraulic conductivity test, **(c)** the macrpore radius of soil samples with different SiO\({}_{2}\) contents after hydraulic conductivity test.
###### Abstract  
The clumping index (CI) quantifies the foliage grouping within distinct canopies relative to randomly distributed canopies, which plays an important role in the vegetation radiative regime. Among the vegetation structure parameters, the global CI map can be retrieved by using multiangle remote sensing observations. The bidirectional reflectance distribution function (BRDF)/Albedo product (MCD43) of the Moderate-Resolution Imaging Spectroradiometer (MODIS) is the crucial input data of the global CI product, which provides validated spatiotemporal continuous directional reflectance data. To determine the impacts of updating the MCD43 product on the consistency and performance of global CI products, CIs retrieved from different MCD43 versions (Collection V005/V006, C5/6) were compared on a global scale and validated with field-measured CI data. The results showed that the global and seasonal comparisons of C5 and C6 CI data are generally consistent. Compared to C5 CI data, C6 CI data have improved quality with more main algorithm retrievals and fewer case of missing data. The comparisons over the field measurements indicate that both versions of CI data agree with field-measured CI data in terms of values and seasonal variations, while C6 CI data (R\({}^{2}\) = 0.89, RMSE = 0.05, bias = 0.02) are closer to field CIs than C5 CI data (R\({}^{2}\) = 0.80, RMSE = 0.07, bias = 0.03), indicating a higher accuracy for C6 CI data. The monthly CI is recommended for characterizing the overall seasonal patterns of surface CIs.  
MODIS; BRDF; collection 5; collection 6; clumping index Article
# Cramer-Rao Lower Bounds on 3D Position and Orientation Estimation in Distributed Ranging Systems
## 3 Bounds on Position Estimation
### Ranging Model  
Consider \(N\) ground antenna labeled \(g_{i}\), which estimates the ToF \(\tau_{i}\) between a target antenna \(h\), depicted in Figure 3. Assume that the ToF estimates \(\tau_{i}\) are unbiased estimators of the corresponding distance \(d_{i}\), such that  
\[\tau_{i}\sim\mathcal{N}(d_{i}/c,\sigma_{\tau_{i}}^{2}), \tag{7}\]  
where \(c\) is the speed of light, \(\mathcal{N}\) denotes the normal distribution, \(\sigma_{\tau_{i}}^{2}\) denotes the variance of the ToF estimator, and \(d_{i}\) is the Euclidean distance between \(g_{i}\) and \(h\). The CRLBs for time-of-flight (ToF) estimation are well studied [45] and primarily depend on the integrated signal-to-noise ratio (ISNR) and the signal bandwidth. ISNR is a function of numerous variables, but if the bulk distance between platforms \(G\) and \(H\) is large compared with the local distance between antennas \(g_{i}\), it is reasonable to assume that each link has comparable ISNR and, therefore, comparable variance, i.e., \(\sigma_{\tau_{i}}^{2}\approx\sigma_{\tau}^{2}\ \forall\ i\ \{1,2,\cdots,N\}\). This approximation is reasonable for most applications, but a notable counter-example is any  
Figure 3: Example system configuration with a 4-antenna user \(G\) and a 4-antenna user \(H\). The distances between each antenna pair are extracted from time-of-flight (ToF) estimates produced by a two-way ranging (TWR) system.  
near-field application where the antennas of one system surround the antennas of the other, for example, the Airbus DeckFinder system [48].  
The variance of an unbiased estimator \(\hat{\mathbf{\alpha}}=[\hat{\mathbf{\alpha}}_{1}\ \hat{\mathbf{\alpha}}_{2}\ \cdots\ \hat{\mathbf{\alpha}}_{q}]^{T}\) is bounded by \(\sigma_{\hat{\mathbf{\alpha}}_{i}}\geq[\mathbf{I}^{-1}(\alpha)]_{ii}\) where \(\mathbf{I}(\alpha)\) is the Fisher information matrix (FIM) of size \(q\times q\) and is defined [49] as  
\[[\mathbf{I}(\alpha)]_{ij}=-E\Bigg{[}\frac{\partial^{2}}{\partial\alpha_{i} \partial\alpha_{j}}\ln p(z;\alpha)\Bigg{]}. \tag{8}\]  
We use this definition of the CRLB for the remainder of this manuscript.
## 2 Materials and Methods
### Tensile Tests  
Since only a few strength parameters are available for the used additively manufactured material, especially at elevated temperatures, tensile samples were produced from the same material in order to determine the characteristic values by means of tensile tests. As already shown by [PERSON] and [PERSON] (2014), the mechanical properties of additively manufactured 1.4404 exhibit anisotropic behavior even at elevated temperatures. Therefore, the position of the tensile samples in the powder bed during SLM were divided into those manufactured horizontally, in a 45\({}^{\circ}\) angle and vertically. The same SLM parameters were selected as for the reactor body. The blanks were then machined to produce the tensile samples shown in Figure 2. The geometry was chosen in accordance with DIN 50125. Besides room temperature (RT), 300 \({}^{\circ}\)C and 425 \({}^{\circ}\)C were specified as testing temperatures (representing the measured maximum reactor material temperature according to [20] and arithmetic mean between the maximum reactor material temperature and maximum permissible catalyst temperature of 550 \({}^{\circ}\)C, respectively). The heating rate was 10 K/s and the target temperature was held for 5 s prior to testing to homogenize the temperature. A constant cross-head speed of 0.04 mm/s was applied. At RT, two tensile tests were carried out per spatial direction for averaging; at the elevated temperatures, three tests were carried out in each case.  
The tensile tests were conducted in a thermomechanical simulator Gleeble 3500-GTC (DSI Inc., Poestenkill, NY, USA). The samples were mounted in the testing rig using conical copper grips, as depicted in Figure 3. Conductive heating with an alternating current was applied to reach the desired testing temperature. The temperature was monitored with point-welded thermocouples of type K. During testing, a load cell measured the forces in the axial direction. A linear variable differential transducer, which exhibits improved accuracy compared to the sole measurement of the piston stroke, recorded the displacement.  
While the evaluation of the strength parameters, especially the ultimate tensile strength, is valid for all temperatures, strain parameters such as uniform elongation and elongation at break can only be analyzed at room temperature. Due to the conductive heating, a temperature gradient develops along the gauge length at elevated temperatures, resulting in immediate strain localization under tensile loading. The assumption of homogeneous strain over the entire gauge length up to the necking is therefore only valid to a limited extent.
## 4 Systematic Literature Review
### Main Findings
#### 4.1.1 Land Mapping  
Land-use mapping represents a critical research topic that addresses approaches for identifying crop areas in counties, regions and countries. Crop maps are the basis of accurate agricultural statistics for estimation, stratification purposes and food security studies.  
Machine learning, deep learning and statistical methods provide tools for automatically classifying zones dedicated to specific crop types. Most of the work on land-use mapping uses vegetation indices and spectral bands derived from satellite imagery. MODIS and Landsat image datasets were explored to perform cotton and winter wheat mapping in Central Asia [67]. By applying Random Forest and Support Vector Machines algorithms to these datasets, it was possible to reach a land cover accuracy of 91%.  
Maize mapping in Nigeria was studied in [68], using the same algorithms over multi-temporal spectral indices and bands retrieved from Sentinel-1A and Sentinel-2A datasets. Another study [2] followed a similar approach for delimiting land use and cover mapping in Brazil, using a CBERS (China-Brazil earth resources satellite) data cube technology and MODIS datasets. Wheat mapping is proposed in [70] using Chinese Landsat-8 and Sentinel-2 datasets. The authors of the work explored Principal Component Analysis (PCA) to increase the accuracy provided by vegetation indices and spectral bands in classifying Brazilian rainfed crops, irrigated crops, savannas/shrublands, grasslands, forestlands, pasturelands and perennial crops. The NDVI (normalized vegetation index) predictors showed the best performance in delimiting crops, while the NDBI (normalized difference built-up index) method proved valuable for excluding buildings. Soybean mapping and cropland mapping in Argentina, the West African Sahel and Northeast Asia are studied in [73; 74; 76]. Vegetation indices and spectral bands obtained using the Google Earth Engine were selected as predictors for an RF model. Research on field mapping in Ghana, using similar methods, is presented in [81] using CubeSats and PlanetScope datasets. RF models were also used for Quinoa abiotic stress prediction [77].  
Supervised learning models depend on labeled datasets for training and evaluation. Data labeling represents a manual, work-intensive process. Automatization of the data labeling process significantly reduces the cost of datasets and promotes their readiness. In [82], paddy rice mapping in South Korea resorts to the K-means clustering algorithm to create pseudo-labels for datasets and RF for classification, using Sentinel-1 and Sentinel-2 datasets. Another study on crop mapping in South Dakota [83] adopted a similar approach using high-resolution images (10 m crop-type maps).