esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 The Bridge-Borne Noise Prediction Model
### The Track-Bridge FEM Model  
The bridge employed for this study is a standard double-line concrete box girder widely used in Chinese high-speed railways, and the track structure on the bridge adopts the CRTS II-type ballastless slab, which is composed of rail, fastenings, track slabs, CA mortar layer, and base plate. The rail profile is CHN 60 kg/m. The bridge span is 32.6 m, and the widths of the deck and the bottom flange are 13.4 m and 4.8 m, respectively. The thicknesses of the deck, bottom flange, and webs are 315 mm, 480 mm, and 300 mm, respectively, and the simulated rails are (\(40+32.6+40\)) m long. The track-bridge FEM model is developed using the software ANSYS 19.0, as shown in Figure 1. The rails are modeled with beam188 elements, and the track slab and base plate are modeled with Solid45 elements. The Combin14 spring elements are adopted for modeling the fastening/rail pad system and the CA mortar layer. The bridge is modeled as four thin plates using Shell181 elements, and the Rayleigh damping is used to deal with the damping of the bridge.
## 4 Discussion  
Gathering thermal destruction kinetic data is routine, and it has been practiced by numerous researchers. It simply requires collecting data on destruction kinetics at different temperatures when heated to different lengths of time. Often, one of the kinetic models is used, and the most common is the first-order log-linear model. The slope index of the logarithm of concentration reduction vs time is designated as a decimal reduction time or D-value (negative reciprocal slope). The D-value represents one logarithmic reduction in the original concentration or 90% reduction/destruction. Since the D-value is temperature dependent, a temperature dependency of the D-value parameter, defined as a z-value, is also computed as the negative reciprocal slope of the log D-value vs the temperature curve. There are some recent models that rely on non-log-linear curves and use Weibul type models [28].  
In the above kinetic considerations, the heating times are generally all assumed to be given to the sample at the target temperatures. Obviously, this is not possible under practical conditions, and, therefore, the early part of the heating time until the sample reaches the target temperature is generally discarded. In high-temperature destruction kinetics, this frequently may not be possible. In this case, suitable corrections are made to the come-up time to only take the effective portion of the come-up time. Such concepts are routinely used in thermal process calculations [29] in order to expand the time span for the kinetics and to accommodate some temperature fluctuations during the data gathering.  
Unlike thermal kinetics, in which a significant portion of the heating time exists at a constant target temperature, microwave destruction kinetics always occur at unsteady-state heating conditions. This implies that as long as the MW energy is absorbed, the temperature of the sample continues to increase. If attempting to hold a constant temperature by using an on/off mechanism to the magnetron or using a low power MW to maintain the temperature drop, very little MW energy is used during the holding period, and conditions would mostly represent \"thermal hold\" conditions. Hence, MW inactivation or destruction kinetics will need to be entirely carried out with MW \"on\" period, and, therefore, entirely carried out under non-isothermal conditions. Hence, the effective time concept described earlier (Equation (3)) needs to be adopted, as it is in this study, and, here, significant differences were observed between the thermal and MW kinetics.  
Furthermore, the destruction kinetics of \(\alpha\)-amylase are very much pH sensitive. At low pH, the inactivation rates are considerably lower, and \(\alpha\)-amylase as a TTI under this condition can be used for pasteurization. At higher pH levels, the enzyme is much more resistant to heat inactivation, and such higher pH levels can be adopted for using the same enzyme as a TTI for high-temperature applications, such as sterilization.
# Estimation of Antenna Pose in the Earth Frame Using Camera and IMU Data from Mobile Phones
## 5 Antenna Poses Estimated from Captured Images
### Approximate Pose Estimation for Initialization
#### 5.1.2 Circumferential Match  
However, the solution to Equation (8) is not unique: from a geometric point of view, \(R^{\prime}_{\perp}\) describes only the yaw and pitch of the antenna. Let the two angles be \(\alpha^{\prime}\) and \(\beta^{\prime}\); we can write \(R^{\prime}_{\perp}\) as \(R^{\prime}_{\perp}\) (\(\alpha^{\prime},\beta^{\prime}\)). The left roll angle, which describes the rotation around \(\overrightarrow{E^{\prime}_{0}E^{\prime}_{1}}\), is still undetermined. To eliminate the remaining uncertainty, we enumerate the discrete rotations of the model around \(\overrightarrow{E^{\prime}_{0}E^{\prime}_{1}}\) based on \(R^{\prime}_{\perp},T^{\prime}\) to find a rotation that minimizes the difference between the widths of the antenna silhouettes at their centers in the real images and in the projections (as indicated by the dashed lines in Figure 8b). Figure 9 shows an example of how the width difference changes with the rotation; two minima are observed because of the symmetry of the antenna model, and we select the correct one based on prior knowledge of which side of the antenna is facing the camera.  
Let the best rotation angle determined through enumeration be \(\gamma^{\prime}\), and let the additional rotation it represents be denoted by \(R^{\prime}_{\parallel}(\gamma^{\prime})\); then, by combining \(R^{\prime}_{\perp}\) and \(R^{\prime}_{\parallel}\), we obtain the following approximate rotation:  
\[R(\alpha^{\prime},\,\beta^{\prime},\gamma^{\prime})=R^{\prime}_{\parallel}\, (\gamma^{\prime})\,R^{\prime}_{\perp}\,\big{(}\alpha^{\prime},\beta^{\prime} \big{)}\,.\]  
Figure 8b shows an example of how the contours from a real image and from the antenna projection based on the recovered pose are aligned for a single viewpoint.  
Figure 8: Axial alignment. **(a)** The 3D/2D principal axes detected using a rotated bounding box; **(b)** projections of endpoints of model principal axis and recovered 3D axis are shown as the blue and red dots, and the endpoints of the detected 2D principal are shown as the blue dots. Note that some misalignment between them may exist.  
Figure 9: Removal of the rotational uncertainty about the (recovered) 3D principal axis. The antenna model is rotated around the recovered principal axis with a fixed step size (X axis), and the average difference between the antenna widths at the center in the projections and the real images (Y axis) is treated as the cost that represents the degree of closeness between the current pose and the ground truth. Two minima are clearly observed.
## 3 Methodology
### Prompt Engineering for LePB-SA4 RE  
#### 3.2.1 Prompt-Tuning Paradigm  
Full parameter FT classifiers, such as _BertForSequenceClassification_ (B4 SC), add a dense linear layer on top of the model to perform semantic (sentiment) mapping classification [37]. However, with small-scale datasets, capturing sufficient task-relevant features is often challenging, leading to weak generalization [38]. The _prompt-tuning_ + _B4 MLM_ paradigm addresses this by guiding the model to capture more nuanced contextual changes at the lexical level. It does so by constructing and utilizing appropriate prompts to deduce _[MASK]_ tokens and solve specific tasks [14]. The following mathematical model provides a detailed description of this process:  
A. The input \(I\) of the model including prompts \(P_{n}\) and text \(X_{n}\):  
\[I=[P_{1},P_{2},\ldots,P_{n}+[\text{MASK}]+X_{1},X_{2},\ldots,X_{m}] \tag{1}\]  
B. The input is converted into high-dimensional vectors through an embedding layer and then processed through multiple layers of _Transformer_. The output of each layer is calculated through the self-attention mechanism. Conditional probability formula:  
\[H^{(l)}=\text{Transformer}(H^{(l-1)})=\sum_{j}a_{ij}^{(l)}W_{\varphi}^{(l)}h_{ j}^{(l-1)} \tag{2}\]  
* \(H^{(l)}\) refers to the output of the Transformer at the \(l\)-th layer, which is obtained by applying self-attention mechanisms and feedforward networks to the output of the previous layer \(H^{(l-1)}\).
* \(a_{ij}^{(l)}\) represents the attention weights of the \(l\)-th layer, indicating the attention intensity from position \(i\) to position \(j\).
* \(W_{\varphi}^{(l)}\) represents the value weight matrix of the \(l\)-th layer.
* \(h_{j}^{(l-1)}\) represents the hidden state output of the \(j\)-th position in the previous layer.  
C. In the BERT model, the prediction of _[MASK]_ is made by calculating the conditional probability given the context. The formula for the mapping from the hidden state to the output vocabulary probability distribution is  
\[P(w_{i}\mid h)=\frac{e^{\frac{h_{\text{[MASK]}^{T}}^{T}w_{i}}{\sum_{w\in V}e^ {h_{\text{[MASK]}^{T}}w}}}}{\sum_{w\in V}e^{h_{\text{[MASK]}^{T}}^{T}w}} \tag{3}\]  
* \(w_{i}\) represents the terms in the vocabulary V.  
* \(h_{[\text{MASK}]}\) is the output vector of the Transformer layer corresponding to the _[MASK]_ position.
* \(e^{h_{[\text{MASK}]}^{\gamma}}\) represents the natural logarithm of the dot product result of \(h_{[\text{MASK}]}\) and \(w_{i}\).  
D. The model optimizes parameters by minimizing the cross-entropy loss between the predicted output and the true labels:  
\[L=-\log P(y_{\text{true}}\mid h) \tag{4}\]  
The loss function \(L\) optimizes the model parameters by minimizing the negative log probability of the true label \(y_{\text{true}}\).
## 7 Numerical Results
### Comparison with the LW and ESL Approaches  
In this last paragraph, the results obtained by means of the proposed ELW approach are compared to the ones achievable through the ESL and LW models. For this purpose, the laminated conical shell described in the works [61; 83] is considered. The same work can be taken as a reference for both the geometric and mechanical properties of the laminated composite shell at issue. For completeness purposes, the conical surface in hand is given by the following position vector  
\[\mathbf{r}(x,\theta)=R_{0}(x)\cos\theta\mathbf{e}_{1}-R_{0}(x)\sin\theta \mathbf{e}_{2}+x\sin\varphi\mathbf{e}_{3} \tag{63}\]  
where \(R_{0}(x)=R+x\cos\varphi\). The constant angle \(\varphi=\pi/3\) represents the slope of the straight meridian profile, whereas \(R=1\,\mathrm{m}\) is the radius measured at the top of the shell. The two-dimensional domain is bounded by the limitations \(x\in[0,L_{x}]\) and \(\theta\in[\theta_{0},\theta_{1}]\), with \(L_{x}=3\,\mathrm{m}\), \(\theta_{0}=0\) and \(\theta_{1}=2\pi\). The shell is made of two layers of Graphite-Epoxy characterized by the same thickness value \(h_{1}=h_{2}=0.15\,\mathrm{m}\). On the other hand, the lamination scheme is given by \((-45/45)\). As far as the boundary conditions are concerned, the conical shell is completely clamped along the top edge, whereas the lower circumference is free.  
The GDQ method is employed to obtain the numerical solution even in this circumstance, for all the considered approaches. Since various orders of kinematic expansion are taken into account, the comparison should be performed by considering the corresponding higher-order displacement field for each approach (ESL, ELW, and LW). In particular, the ESL solutions are taken from the paper [61], whereas the work [83] is the reference for the LW ones. The same grid distributions and total values of  
Figure 8: First nine mode shapes for a CCCC thin laminated helicoidal shell with \((0/90/0/90)\) as lamination scheme.  
discrete points specified in the works [61; 83] are used also in the present paper. The natural frequencies of the conical shell are shown in Table 11, where the solutions obtained through a three-dimensional finite element model are presented too. It should be noted that both the ESL and ELW models are taken with and without the Murakami's function. In general, the ELW approach provides values of natural frequencies placed between the ones given by the corresponding ESL and LW models. In other words, the results obtained by the ELW approach are closer to the LW solutions, as well as to the 3D-FEM, than the corresponding ESL ones. Nevertheless, the computational cost of the ELW model is reduced if compared to the one that characterizes the LW approach, since the proposed methodology does not require to consider each layer separately. For the sake of completeness, the reader can refer to the papers [61; 83] to understand the meaning of the ESL and LW models introduced in Table 11.
## 1 Introduction
Understanding depth via perspective projection is far more precise than using an orthographic technique because, in perspective depth, every light ray is tracked to the precise pixel of its source, unlike in orthographic depth, where light is considered to be emanating from infinity [7]. Although perspective projection has shown more accurate disparity estimation results [8; 9; 10], most depth estimation algorithms are performed on orthographic images due to the simplicity of the capturing mechanisms. Figure 1: Light beams from various perspectives (VPIs) hitting the same EI in a Holoscopic sensor. As seen in Figure 1, the EIs in the Holoscopic setup record light from different angles, resulting in perspective images that contain angular information. Conversely, VPIs are obtained from various locations on the primary lens, replicating different viewpoints. These images typically exhibit orthographic projection, predominantly capturing surface characteristics. The differentiation here between EIs and VPIs is linked to their ways of spatial representation [11].
# NPP-VIIRS DNB Daily Data in Natural Disaster Assessment: Evidence from Selected Case Studies
###### Abstract  
Whereas monthly and annual nighttime light (NTL) composite datasets are being increasingly used to estimate socioeconomic status, use of the National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) Day/Night Band (DNB) daily data has been limited for detecting and assessing the impact of short-term disastrous events. This study explores the application of daily NPP-VIIRS DNB data in assessing the impact of three types of natural disasters: earthquakes, floods, and storms. Daily DNB images one month prior to and 10 days after a disastrous event were collected and a Percent of Normal Light (PNL) image was produced as the ratio of the mean DNB radiance of the pre- and post-disaster images. Areas with a PNL value lower than one were considered as being affected by the event. The results were compared with the damaged proxy map and the flood proxy map generated using synthetic aperture radar data as well as the reported power outage rates. Our analyses show that overall NPP-VIIRS DNB daily data are useful for detecting damages and power outages caused by earthquake, storm, and flood events. Cloud coverage was identified as a major limitation in using the DNB daily data; rescue activities, traffic, and socioeconomic status of the areas also affect the use of DNB daily data in assessing the impact of natural disasters. Our findings offer new insight into the use of the daily DNB data and provide a practical guide for researchers and practitioners who may consider using such data in different situations or regions.  
NPP-VIIRS; Day/Night Band (DNB) daily data; nighttime light; natural disaster; earthquake; storm; flood +
Footnote †: journal:  
Article
## 3 Analysis of Processing Efficiency
### Power Used for the Laser Cutting of Anode \(P^{p}\)  
Power used in cutting to sublimate, melt, and evaporate the anode can be derived from the geometrical volume removed and shown in Figure 2. The volume removed by sublimation is expressed by  
\[V_{sub}=l_{g}d_{AE}W_{top} \tag{13}\]  
where \(d_{AE}\) is the thickness of an active electrode material. The energy required to sublimate the given volume is expressed by  
\[E_{sub}=c_{AE}\cdot m_{AE}\cdot(T_{sub}-T_{o})=\rho_{AE}V_{sub}\{c_{AE}\cdot(T _{sub}-T_{o})\} \tag{14}\]  
where \(c_{AE}\) is the specific heat of the active electrode material, \(m_{AE}\) is the mass of the active electrode material, \(\rho_{AE}\) is the density of the active electrode material, \(T_{sub}\) is the sublimation temperature of the active electrode material, and \(T_{o}\) is the ambient temperature. Volumes removed by melting and evaporation are, respectively, expressed by  
\[V_{melt}=l_{g}d_{CC}(W_{melt}-W_{\text{ker}f}) \tag{15}\]  
\[V_{evap}=l_{g}d_{CC}W_{\text{ker}f} \tag{16}\]  
where \(d_{CC}\) is the thickness of a current collector. In addition, the energy required to melt and evaporate, respectively, can be expressed by  
\[E_{melt}=m_{CC\_s}\{c_{CC\_s}\cdot(T_{melt}-T_{o})\}=\rho_{CC\_s}V_{melt}\{c_ {CC\_s}\cdot(T_{melt}-T_{o})\} \tag{17}\]  
\[\begin{array}{l}E_{evap}=m_{CC\_s}\{c_{CC\_s}\cdot(T_{melt}-T_{o})+L_{melt }\}+m_{CC\_s}\{c_{CC\_i}\cdot(T_{evap}-T_{melt})\}=\\ \rho_{CC\_s}V_{melt}\{c_{CC\_s}\cdot(T_{melt}-T_{o})+L_{melt}\}+m_{CC\_s}V_{ evap}\{c_{CC\_s}\cdot(T_{evap}-T_{melt})\}\end{array} \tag{18}\]  
where \(c_{CC\_s}\) and \(c_{CC\_j}\) are the specific heat of the solid and liquid current collector, respectively; \(m_{CC\_s}\) and \(m_{CC\_l}\) are the mass of the solid and liquid current collector, respectively; \(T_{melt}\) is the melting temperature of the current collector; \(T_{evap}\) is the evaporation temperature of the current collector; \(L_{melt}\) is the latent heat of fusion; and \(\rho_{CC\_s}\) and \(\rho_{CC\_j}\) are the density of the solid and liquid current collector, respectively.  
The total energy used to remove an anode is obtained as follows:  
\[E_{p}=E_{sub}+E_{melt}+E_{evap} \tag{19}\]  
Finally, the laser power used in the cutting of an anode can be calculated by dividing the total laser energy by the final time, or  
\[P_{p}=\frac{E_{p}}{t_{f}} \tag{20}\]
# Novel Techniques for Microbiological Safety in Meat and Fish Industries
## 3 Microbial Safety of Meat and Meat Products
### Pathogens in Meat
#### 3.2.2 Salmonella Enterica  
_Salmonella enterica_ was detected in Egypt at a percentage of 4.9 of beef samples. _S. enterica_ serovar Typhimurium, _S. enterica_ serovar Enteritidis, _S. enterica_ serovar Infants and non-typable servovars were also identified in various beef samples in range of 0.25 to 2.5%. _E. coli_ O157:H7 was recognized in 2.6% of hamburger tests. _Shigella_ spp. were distinguished in 1.8% of hamburger tests. _S. enterica_ serovar Infantis, _S. enterica_ serovar Enteritidis, _S. enterica_ serovar Typhimurium and non-typable servovars were recognized in 0.75%, 0.63%, 0.25% and 0.13% of chicken meat tests, respectively. E. coli O157:H7 and _Shigella_ spp. were also identified in poultry meat at -0.5% and 0.25%, respectively [26].
# Ensuring Agricultural Sustainability through Remote Sensing in the Era of Agriculture 5.0
## 2 Agricultural Remote Sensing
Normally sensors used in RS that are for crop monitoring detect the following electromagnetic wave bands, depending on specific objectives [7]:  
* Thermal infrared band;
* Red, green, and blue (RGB) bands;
* Near infrared (NIR) band;
* Red edge band (RE). The amplitude of the information retrieved from RS is considerable to support sustainable agriculture capable of feeding a rapidly growing world population. Among the prominent advantages or applications of RS are the identification of phenotypically better varieties, optimization of crop management, evapotranspiration, agriculture phenology, crop production forecasting, ecosystem services (related to soil or water resources) provision, plant and animal biodiversity screening, crop and land monitoring, and precision farming [8; 18; 19; 40; 41; 42].
# A Meta-Analysis on Dual Protocols for Chronic Stroke Motor Recovery: Robotic Training and tDCS
## 2 Materials and Methods
### Outcome Measures  
We determined the short-term and long-term effects of robotic training combined with tDCS protocols on the motor capabilities of both the upper and lower extremities of individuals post-stroke. The short-term effects were considered transient performance changes in motor functions and were evaluated by immediate post-testing administered less than 24 h after the final treatment session. In contrast, the long-term effects represented relatively sustained changes in motor functions evaluated by delayed post-testing administered more than 2 weeks after the final combined tDCS and robotic intervention session. Fifteen out of the twenty qualified studies reported primary outcome measures on motor function recovery of the upper limbs. Specific details on the primary upper limb motor function outcome measures from each study were evaluated by: (a) Fugl-Meyer assessment on upper extremity (FMA-UE): 12 studies; (b) straightness during a reaching task: one study; (c) movement aiming task: one study; and (d) average performance during a 3D reaching task: one study. We identified 20 total short-term effect comparisons from the 15 included studies, (a) single comparisons from 12 studies and (b) multiple comparisons from 3 studies, based on different tDCS timings (i.e., before, during, and after), montages (i.e., anodal and cathodal stimulation), and bimanual tasks (speed/accuracy trade-off, coordination, and force). Nine out of the fifteen included studies reported ten long-term effect comparisons, (a) single comparison from eight studies and (b) two comparisons from one study, based on different tDCS montages (i.e., anodal and cathodal stimulation). Five out of the twenty qualified studies revealed changes in the lower limb motor functions after the combined tDCS and robotic training protocols. The primary lower limb motor function outcome measures were: (a) 10 m walk test (10 MWT): two studies; (b) 6 min walk test (6 MWT): two studies; and (c) functional ambulation category (FAC): one study. The lower limb studies reported five short-term effect comparisons and four long-term effect comparisons.
## 4 Results
### Model Evaluation
#### 4.1.1 Decision Tree  
The maximum depth of the decision tree was chosen as the parameter to be adjusted. The scoring results of the classification indicators for each parameter value were converted into bar charts to interpret their trends, as shown in Figure 5. The accuracy score started to exceed 0.8 at a depth of 5 and then slowly increased until it reached the maximum value of 0.821 at a depth of 11. Thereafter, the accuracy decreased slowly to a score of less than 0.8 at maximum depth. The indicator trends evidenced that all classification indicators scored the best at a maximum depth of 11. The comprehensive evaluation results showed that this parameter, namely the maximum depth of the tree, was optimal when it was 11. The optimal parameter was brought into the model test for target prediction, and the prediction results were presented using a confusion matrix and receiver operating characteristic (ROC) curves (Figure 6) to verify that the model was optimal. The model validation process was as follows:  
**Grid search** The parameter, namely the maximum depth of the tree, was optimal when it was 11. In this case, the model accuracy was 0.821.  
**Confusion matrix** The validation results of the indicators for each power consumption level are presented in Table 4. The indicators of low and high energy consumption scored higher, showing that the analytical model had better classification effects in terms of low and high energy consumption.  
**ROC curve** For medium energy consumption, the area under the curve (AUC) was 0.85, showing that the model had good discrimination. For both low and high energy consumption, the AUC was greater than 0.9, confirming that the model had excellent discrimination. Overall, the model had clear discrimination for all power consumption levels.