esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments
### Ablation Experiments and Result Analysis
#### 4.4.3 Dpem  
The DPEM explicitly models spectral-structural differences by calculating bi-temporal feature disparities, effectively suppressing pseudo-change interferences caused by factors such as illumination variations and seasonal fluctuations. The data in Row 4 of Table 1 indicate that the model equipped with this module improves F1-scores by 2.17%, 2.21%, and 1.26% on the LEVIR-CD, WHU-CD, and CLCD datasets, respectively. The significant improvement on WHU-CD is closely related to the susceptibility of shadows and vegetation to be misclassified as changes in urban scenes. Conversely, the 1.08% decrease in IoU on CLCD reflects the necessity of enhancing features for low-contrast differences in farmland. The visualization results in Column d of Figure 6 show that the model without the DPEM generates numerous red noise points in the vegetation areas of CLCD, visually verifying the module's ability to filter out pseudo-changes. The above experimental results show that the absence of the DPEM significantly weakens the model's ability to capture complex difference patterns, confirming the key role of the differential feature enhancement mechanism in change detection tasks.
## 3 Experimental Phenomena and Their Analysis
### Main Experimental Phenomena
#### 3.1.2 Crack Development in Specim J2  
The joint of test piece J2 was made of Q345 steel with 24 mm diameter common bolts. The loading method was displacement-controlled. The first crack with a width of 0.1 mm appeared on the lower surface of the beam (32 cm from the edge of the column) when loaded to \(-\)30 mm. The precast beam also had the phenomenon of rotation with the contact surface between the bottom of the beam and the corbel as the point of rotation. Details of the phenomena are recorded in Table 8.  
\begin{table}
\begin{tabular}{c c} \hline
**Position** & **Test Phenomenon** \\ \hline \multirow{8}{*}{Side of beam} & \(-\)20 mm: 30 cm from the edge of the column, length of 30 cm, seam width of 0.04 mm \\ \cline{2-3}  & \(-\)35 mm: 18 cm and 35 cm from the edge of the column, lengths of 13 cm and 20 cm, \\  & respectively, both with a seam width of 0.1 mm \\ \cline{2-3}  & \(-\)40 mm: 12 cm and 60 cm from the edge of the column, lengths of 20 cm and 25 cm, \\  & respectively, both with a seam width of 0.1 mm \\ \cline{2-3}  & \(-\)50 mm: at 15 cm, 35 cm, and 45 cm from the edge of the column, lengths of 16 cm, 5 cm, and 24 cm, respectively, seam widths of 0.08 mm, 0.07 mm, and 0.07 mm, respectively \\ \cline{2-3}  & \(-\)60 mm: the side of the enlarged end of the beam runs parallel to the face of the bearing plate, length of 73 cm, seam width of 0.13 mm; 45 cm from the edge of the column based on the original crack branch, length of 5 cm, seam width of 0.11 mm \\ \hline \multirow{2}{*}{The top surface of the beam} & \(+\)50 mm: 3 cm, 12 cm, and 25 cm from the edge of the column, lengths of 40 cm, 42 cm, \\  & and 40 cm, seam widths of 0.2 mm, 0.11 mm, and 0.22 mm, respectively \\ \hline \multirow{8}{*}{The bottom surface of the beam} & \(-\)10 mm: 43 cm and 65 cm from the edge of the column, lengths of 40 cm and 20 cm, \\  & respectively, seam widths of 0.13 mm and 0.08 mm, respectively \\ \cline{1-1} \cline{2-3}  & \(-\)30 mm at 33 cm from the edge of the column, length of 40 cm, seam width of 0.13 mm \\ \cline{1-1} \cline{2-3}  & \(-\)45 mm: from the bearing plate to the free end of the beam parallel to the long side of the beam, length of 15 cm, seam width of 0.08 mm \\ \cline{1-1} \cline{2-3}  & \(-\)50 mm: at 20 cm, 65 cm, and 88 cm from the edge of the column, lengths of 40 cm, 10 cm, \\ \cline{1-1} \cline{2-3}  & and 18 cm, respectively, seam widths of 0.1 mm, 0.2 mm, and 0.12 mm, respectively \\ \hline \end{tabular}
\end{table}
Table 7: Specim J1 test phenomena.  
The final damage images for the joints are shown in Figures 12 and 13. The damage to both J1 and J2 was ultimately to the bolts and precast beams. The precast columns were not damaged, which followed the design concept of these new joints.
# Research and Application of Semi-Supervised Category Dictionary Model Based on Transfer Learning
## 3 Experiments and Results Analysis
### Results Analysis
#### 3.3.1 Results Analysis of the Vision Data Experiment
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Task** & **1-NN**[25] & **SRC**[26] & **LSDT**[27] & **TSC + LR**[4] & **SSDT** \\ \hline L\(\rightarrow\)V & 52.15 \(\pm\) 6.39 & 51.82 \(\pm\) 3.13 & 50.24 \(\pm\) 2.49 & 52.11 \(\pm\) 3.26 & **54.48 \(\pm\) 3.69** \\ \hline L\(\rightarrow\)S & 44.44 \(\pm\) 7.63 & 44.11 \(\pm\) 2.68 & 43.43 \(\pm\) 1.90 & 45.31 \(\pm\) 2.04 & **46.62 \(\pm\) 3.03** \\ \hline S\(\rightarrow\)L & 35.47 \(\pm\) 8.26 & 47.67 \(\pm\) 4.21 & 47.72 \(\pm\) 4.93 & 46.90 \(\pm\) 2.60 & **48.59 \(\pm\) 4.95** \\ \hline S\(\rightarrow\)V & 54.37 \(\pm\) 4.80 & 57.30 \(\pm\) 2.34 & 60.27 \(\pm\) 4.03 & **62.87 \(\pm\) 2.64** & 60.62 \(\pm\) 3.42 \\ \hline V\(\rightarrow\)L & 39.32 \(\pm\) 8.22 & 51.74 \(\pm\) 3.38 & **52.94 \(\pm\) 3.35** & 51.03 \(\pm\) 3.25 & 51.69 \(\pm\) 3.30 \\ \hline V\(\rightarrow\)S & 44.41 \(\pm\) 5.42 & 61.87 \(\pm\) 1.37 & 62.33 \(\pm\) 2.15 & 61.90 \(\pm\) 1.94 & **62.77 \(\pm\) 1.52** \\ \hline Average & 45.07 \(\pm\) 6.79 & 52.42 \(\pm\) 2.85 & 52.82 \(\pm\) 3.14 & 53.35 \(\pm\) 2.62 & **54.13 \(\pm\) 3.32** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Experimental output of each model, based on cross-domain dataset fc7 layer depth features (mean \(\pm\) standard deviation %). (The best results for different tasks are bolded). Figure 7: Comparison of the best performance of each model based on cross-domain dataset fc6 layer depth features.
## 5 Experiments and Results
### The Influence of Corpus Characteristics on the ADR Detection Accuracy
So, its saturation by ADR stays lower than in most of the existing corpora surveyed in Table 1.  
In order to study the effect of increasing saturation of the corpus by ADR mentions, we experiment with subsets of RDRS that have various sizes and various ADR mention shares per review (see Table 12). Increasing the proportion of ADR by balancing the corpus by the amount of documents with ADR (in the RDRS-1250, 50% of reviews have ADR in it) leads to a more significant increase in ADR precision of 21%. At the same time, it does not cause a significant change in the disease and medication detection accuracy (see Table 14). This may be explained by the higher saturation of the corpus by these entity types, which stays practically unchanged after balancing the corpus. Corpus RDRS-610 includes only sentences with ADR, and corpus RDRS-1136 includes sentences 50% of which has ADR \begin{table}
\begin{tabular}{c c c} \hline \hline
**Corpus** & **RDRS-1250** & **RDRS 2800** \\ \hline BNE-Pos & 51.2 & 50.3 \\ \hline Diseasename & 87.6 & 88.3 \\ \hline Indication & 58.8 & 62.2 \\ \hline Dosage & 59.6 & 63.2 \\ \hline DrugBrand & 81.5 & 83.8 \\ \hline Drugclass & 89.7 & 90.4 \\ \hline Drugform & 91.5 & 92.4 \\ \hline Drugname & 94.2 & 95.0 \\ \hline DrugName/MedFromDomestic & 61.7 & 76.2 \\ \hline DrugName/MedFromForeign & 63.5 & 74.4 \\ \hline Duration & 75.5 & 74.7 \\ \hline Frequency & 63.4 & 65.0 \\ \hline MedMaker & 92.5 & 93.8 \\ \hline MedMaker/Domestic & 65.1 & 87.1 \\ \hline MedMaker/Foreign & 74.4 & 85.0 \\ \hline Route & 58.4 & 61.2 \\ \hline SourceInfodrug & 66.0 & 67.3 \\ \hline Negative * & 52.2 & 52.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: F1-exact of detecting mentions with different tags for RDRS-1250 (the balanced version of our corpus) and RDRS-2800 (the full version). * Negative is the union of tags Worse, NegatedADE and ADE-Neg. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Number of Enities**} & \multicolumn{3}{c}{**F1-Exact**} \\ \hline
**RDRS Subset** & **ADR** & **Medication** & **Disease** & **ADR** & **Medication** & **Disease** \\ \hline RDRS-2800 & 1778 & 33,008 & 17,408 & 52.8 \(\pm\) 3.4 & 84.1 \(\pm\) 0.8 & 63.5 \(\pm\) 0.5 \\ \hline RDRS-1250 & 1752 & 13,750 & 6307 & 61.1 \(\pm\) 1.5 & 84.2 \(\pm\) 0.6 & 62.9 \(\pm\) 1.5 \\ \hline RDRS-1600 & 843 & 17,931 & 9840 & 40.1 \(\pm\) 2.7 & 79.6 \(\pm\) 1.3 & 56.9 \(\pm\) 0.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Accuracy of recognizing three entity types for three subsets of the corpus, different by size and balancing. in it and 50% does not. The experiments on these corpora, which has an ADR saturation closer to that of CADEC, show a further increase of ADR detection accuracy up to 71.3.
# A Survey on Wireless Body Area Networks for eHealthcare Systems in Residential Environments
## 2 Candidate Wireless Technologies
### Alternative Low-Power Wireless Technologies
#### 2.2.5. Zarlink  
Zarlink [70; 71] is a proprietary ultra-low power RF transceiver specifically designed for medical implantable applications. Zarlink utilizes Cyclic Redundancy Check (CRC) error detection along with Reed-Solomon error correction scheme to provide a highly reliable communication link. It operates in the MICS (402-405 MHz) and ISM (433-434 MHz) bands. Zarlink supports data rates of up to 800 kb/s. Zarlink is able to operate in both an implant and a base station. Depending on the selected system type, different requirement is needed especially in terms of power consumption. Therefore, Zarlink has specified two important operation modes: Implantable Medical Device (IMD) mode and base mode. When Zarlink is configured as an IMD mode, the radio is asleep most of the time which consumes only \(\upmu\)W of power compared to mW of power in other modes. However, communication between two nodes cannot occur if either node's radio is in sleep mode. Therefore, a mechanism is required to wake up the receiver's radio to ensure the sender's transmit and receiver's listen operations coincide. This can be done by either utilizing an ultra-low power 2.4 GHz radio or directly by using the IMD processor. Zarlink is considered as one of the leading ultra-low power wireless technology for low-data rate medical implantable applications (TRX = 5 mA, low-power mode = 1 mA and ultra-low power wakeup circuit = 250 nA).
###### Abstract  
Predicting the extreme loads in power production for the preliminary-design of large-scale wind turbine blade is both important and time consuming. In this paper, a simplified method, called Particle Swarm Optimization-Extreme Load Prediction Model (PSO-ELPM), is developed to quickly assess the extreme loads. This method considers the extreme loads solution as an optimal problem. The rotor speed, wind speed, pitch angle, yaw angle, and azimuth angle are selected as design variables. The constraint conditions are obtained by considering the influence of the aeroelastic property and control system of the wind turbine. An improved PSO algorithm is applied. A 1.5 MW and a 2.0 MW wind turbine are chosen to validate the method. The results show that the extreme root load errors between PSO-ELPM and FOCUS are less than 10%, while PSO-ELPM needs much less computational cost than FOCUS. The distribution of flapwise bending moments are close to the results of FOCUS. By analyzing the loads, we find that the extreme flapwise bending moment of the blade root in chord coordinate (CMF_ROOT) is largely reduced because of the control system, with the extreme edgewise bending moment of the blade root in chord coordinate (CME_ROOT) almost unchanged. Furthermore, higher rotor speed and smaller pitch angle will generate larger extreme bending moments at the blade root.  
Keywords:extreme load prediction; improved PSO algorithm; bending moment; pitch angle; rotor speed +
Footnote †: journal: Applied Sciences
# Methodology for CubeSat Debris Collision Avoidance Based on Its Active ADCS System
## 4 Results and Discussion  
### Distances Calculation  
The results of the simulations for the different maneuvers defined are presented in this section. HDD and VDD are computed for two days after the maneuver. Simulations are grouped by type of maneuver: AB, AC, and BC. Results are presented in figures where the axes have the same range, for the aim of visual comparison.
# An Adaptive Weighted Method for Remote Sensing Image Retrieval with Noisy Labels
## 3 Methodology  
In this section, we first introduce the APL and analyse its limitations. Then, we present the framework of our method, which consists of feature extraction, AWNet and image retrieval components. Next, we explain the meaning and role of the two metrics requiredfor the adaptive determination of the APL weights, namely the ratio of entropy to standard deviation (abbreviated as RES) and \(\delta\). Finally, we introduce our core method of the AWNet and describe the algorithm in detail, showing the connection between the AWNet, APL weights and classification.
###### Abstract  
An experimental study was undertaken to assess the heat-transfer coefficient (HTC) of graphene nanoplatelets-pentane nanofluid inside a gravity-assisted heat pipe (HP). Influence of various parameters comprising heat flux, mass fraction of the nanoparticles, installation angle and filling ratio (FR) of the working fluid on the HTC of the HP was investigated. Results showed that the HTC of the HP was strongly improved due to the presence of the graphene nanoplatelets. Also, by enhancing the heat flux, the HTC of the HP was improved. Two trade-off behaviors were identified. The first trade-off belonged to the available space in the evaporator and the heat-transfer coefficient of the system. Another trade-off was identified between the installation angle and the residence time of the working fluid inside the condenser unit. The installation angle and the FR of the HP were identified in which the HTC of the HP was the highest. The value of installation angle and filling ratio were 65\({}^{\circ}\) and 0.55, respectively. Likewise, the highest HTC was obtained at the largest mass fraction of the graphene nanoplatelets which was at wt. % = 0.3. The improvement in the HTC of the HP was ascribed to the Brownian motion and thermophoresis effects of the graphene nanoplatelets.  
graphene; n-pentane; thermosyphon; Thermal performance; tilt angle; filling ratio +
Footnote †: journal: Applied Soft. 2019, 9, 2101; doi:10.3390/app9102101
# Weed Detection in Wheat Crops Using Image Analysis and Artificial Intelligence (AI)
## 3 Materials and Methods
### Data Pre-Processing  
To feed the dataset to the neural networks, the dataset must be pre-processed according to the needs of neural networks such as YOLO-based architectures. All the blurred and noisy images are removed from dataset, and the images are resized to 224 \(\times\) 224 dimensions, as YOLO-based architectures require that input shape.
# Modeling Actual Evapotranspirationiration with MSI-Sentinel Images and Machine Learning Algorithms
## 2 Materials and Methods
### Training and Statistical Evaluation of Models
#### 2.5.3 Residual Analysis  
In addition to statistical metrics, a residual analysis was performed to evaluate the error behavior of predicted values for all models trained for both approaches. Therefore, an image was selected exclusively for this purpose (Table 3). In this image and inside the study area, 11 pivots were selected (highlighted in yellow in Figure 1) for residual analysis between the observed and the predicted values. The 11 pivots were chosen for being close to each other, making it easier to elaborate thematic maps, and because there was no cloud coverage near them. It is noteworthy that the image destined for residual analysis was not in the training process, so the data from this image are new information for the models already trained. Thus, models were applied. Then the predicted ET\({}_{\text{r}}F\) (approaches 1 and 2) and the observed ET\({}_{\text{r}}F\) (METRIC) were extracted from the 11 pivots (Figure 1) to reckon the residues (Equation (13)).  
\[\text{Residual}=\text{Observed value}-\text{Predicted value} \tag{13}\]
## 3 Materials and Methods
### Feature Transformation and Reduction Approaches
#### 3.2.2 Non-Negative Matrix Factorization  
Non-Negative Matrix Factorization (NNMF) is a sophisticated statistical technique aimed at decomposing a non-negative matrix \(V\) into the resultant of multiplying two smaller non-negative matrices, \(W\) and \(H\), where \(V\approx WH\)[52]. This method is especially effective for diminishing feature matrix size [53]. In contrast to conventional matrix factorization techniques like Principal Component Analysis (PCA), NNMF imposes non-negativity limits guaranteeing that every aspect in the resultant matrices is non-negative [54].  
The mathematical representation of NNMF could be articulated in the following manner: For a matrix \(V\in\mathbb{R}^{\sim}(m\times n)\), NNMF determines two non-negative matrices, \(W\in\mathbb{R}^{\sim}(m\times k)\) and \(H\in\mathbb{R}^{\sim}(k\times n)\), that diminish the discrepancy amongst \(V\) and the result of their multiplication \(WH\)[55]. This optimization problem is expressed as:  
\[minimize\ |\ |\ V-WH\ |\ |^{2}\ \text{{subject to }}W,\ H\geq 0 \tag{4}\]  
In this context, \(||\cdot||\) signifies the Frobenius norm, and \(k\) is typically selected to be lower than both \(m\) and n, yielding a reduced version of the initial data set [56].  
* \(W\)**:** A matrix of size m \(\times\) r that serves as a basis for the data, where each column represents a distinct component or part.
* \(H\)**:** A matrix of size r \(\times\) n containing the coefficients or weights corresponding to the components in W.  
The enforcement of non-negativity presents numerous benefits. Primarily, it enhances comprehension by depicting each feature as a summation of cumulative elements, consistent with the instinctive interpretation of systems derived from their individual components. In addition, the resultant sparse representation frequently encapsulates the fundamental patterns in the data more efficiently than conventional methods [57]. This sparsity improves generalization and uncovers latent patterns within the original high-dimensional dataset.
# Influence of Milling Techniques on the Performance of Wheat Straw Ash in Cement Composites
## 3 Results and Discussions
### Mechanical Properties  
Figure 3 depicts the results of the compressive strength test for various mortar types. Compressive strength enhanced with increasing curing age. For instance, control (REF) mortar specimens demonstrated a compressive strength of 49.3 MPa, 60.1 MPa, and 68.7 MPa at 7, 28, and 90 days, respectively. Apart from this, the results clearly describe that the compressive strength increased for the specimens with finer ash particles (W1 and W3 types). For example, a compressive strength of 51.5 MPa, 63.4 MPa, and 70.2 MPa was observed for W2/20 mortar specimens at 7, 28, and 90 days, respectively. W3/20 mortar specimens presented a compressive strength of 53.4 MPa, 66.7 MPa, and 72.3 MPa at 7, 28, and 90 days of curing, respectively. Apart from this, the addition of coarser ash particles  
Figure 2: XRD pattern of WSA specimens.  
reduced the compressive strength. The specimens containing 20% W2 type ash particles demonstrated a compressive strength of 42.8 MPa, 55.3 MPa, and 60.6 MPa at 7, 28, and 90 days of curing, respectively.  
Figure 4 displays the results of flexure strength test conducted on various mortar specimens. Flexure strength increased with increasing curing age. For instance, control (REF) specimens demonstrated flexure strength of 7.51 MPa, 9.1 MPa, and 9.72 MPa at 7, 28, and 90 days, respectively. Similar to the results of the compression testing, flexure strength increased with the inclusion of finer ash particles. For instance, a flexure strength of 7.86 MPa, 9.25 MPa, and 9.78 MPa was observed for W1/20 mortar specimens at 7, 28, and 90 days. Similarly, W3/20 mortar specimens demonstrated flexure strength of 7.92 MPa, 9.84 MPa, and 10.05 MPa at 7, 28, and 90 days of curing. On the other hand, the addition of coarser ash particles caused a reduction in flexure strength in comparison with the control mortar specimens. For example, W2/20 mortar specimens demonstrated a flexure strength of 7.05 MPa, 8.48 MPa, and 9.13 MPa at 7, 28, and 90 days of curing, respectively.  
Figure 4: Flexure strength of mortar specimens.  
Figure 3: Compressive strength of mortar specimens.  
Highest strength was observed for the specimens made with ash particles ground with a combination of ball and disintegrator mills. Similar behavior was observed in a previous study [32]. The results of that study demonstrate that the incorporation of finely ground RHA amplified the pozzolanic reaction and refined the pore structure, which resulted in an improved strength. [PERSON] et al. [33] and [PERSON] et al. [34] revealed that incorporating ultra-fine BA at an optimal content of 20% by weight of OPC resulted in improved strength values. Contrary to this, the specimens made with W2 ash particles caused a decrease in strength compared to the control specimens. It is due to the reason that the coarser ash particles negatively affect the nucleation effect and ultimately subside the strength [35].
# Modulation of Host Lipid Pathways by Pathogenic Intracellular Bacteria
## 2 Intracellular Bacteria That Replicate Inside a Vacuole during Infection
### Host Lipid-Droplet Modulation
tuberculosis_ is also able to survive in the absence of host LDs suggesting a bacterial mechanism for sequestering free fatty acids from other host sources [109]. Host LD accumulation may also be indicative of the virulence potential of various _Mycobacterium_ strains. For instance, it is speculated that the formation of LDs is detrimental to less pathogenic strains of _M. tuberculosis_ during infection likely due to the increased availability of signaling precursors able to be released from host LDs to produce a bactericidal response [110]. Conversely, infection with the highly pathogenic _M. tuberculosis_ strain, H37 Rv, supports that increased host LD formation is beneficial for bacterial growth and persistence, in that, the signaling molecules from LDs assist in producing the anti-inflammatory response required for the production of necrotic tissue and granuloma formation during severe mammalian infection [111]. Comparably, _Brucella abortus_, a facultative, intracellular bacteria that resides within the endoplasmic reticulum-associated vacuoles, increases host PPARY expression during infection. However, as observed for _M. tuberculosis_, this increase potentially modulates host triacylglycerol production and subsequent accumulation in LDs [112]. Another facultative intracellular pathogen that is thought to associate with host LDs is _Salmonella typhimurium_. Unlike, the pathogens described above, studies have suggested that the association of _S. typhimurium_ with host LDs is based on the defined functionality of T3 SS effector proteins, such as SseJ and SseL. As mentioned previously, SseJ not only has acyltransferase activity but also possesses cholesterol esterase activity similar to the _C. trachomatis_ enzyme used to increase host LD production described above [65; 66; 67; 68]. Infection of host cells with a _C. trachomatis sseJ_mutant results in a decrease in PCV production and in a decrease of LD accumulation. Together, these data support the use of Sse] as a cholesterol esterase for accumulation of stored cholesterol in host LDs during infection [67; 68]. On the other hand, SseL contains deubiquitinase activity and _sseL_ mutants stimulate an overaccumulation of host LDs (Figure 1). This strongly suggests that SseL and Sse] play a role in the maintenance of lipid homeostasis during infection, for direct bacterial use, and likely play roles in the modulation of the host response as described for other pathogens mentioned [113; 114].