esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Effect of Wall Temperature on Cumulative Wall Film Mass  
If the initial conditions remain the same except for the change in air flow temperature, where the initial wall temperature is consistent with the air flow temperature, the cumulative wall film mass increases significantly with the decrease in wall temperature, as illustrated in Figure 12. In addition, the gradient of accumulated wall film mass increases with the decrease in initial wall temperature. This is because when the initial plate temperature is between the critical transition temperature and the critical heat flux temperature and the spray mass flux is kept constant, the lower the initial wall temperature, the greater the induced temperature drop. Since the spray mass flux decreases gradually along the radial direction, there is a larger cooling area of the spray in the non-core region as the wall temperature decreases that causes the increase in cumulative deposition mass. Figure 13 shows the curve of the wall temperature along the centerline of the air flow direction in each cooling zone at different initial air flow temperatures, which corroborates the above conclusion. Consequently, making the exhaust wall temperature exceed the critical transition temperature is an effective method for avoiding the wall film formation.  
Figure 11: Cumulative wall film mass curve predicted by different wall film models during an injection cycle.
## 3 Antenna Performance Enhancement Techniques
### Role of Hybrid Feeding Structure in Performance Enhancement  
A hybrid-feed wide-band and high-efficiency antenna for mmWave 5G communication is proposed in [58]. The proposed designed antenna consists of a novel hybrid-feed structure in a planar antenna array with excellent performance in terms of high efficiency and wide bandwidth is presented. To achieve the goal of high-performance key parameters such as wide bandwidth and high efficiency, the conventional high-loss ridged feeding network and substrate-based feeding network are replaced by an effective low-loss ridged-gap waveguide feeding network. The measured bandwidth ranges from 26.05 to 31.15 GHz (16%), and the maximum achieved gain is 25.15 dBi. The proposed design might be an excellent choice for 5G application. In comparison with [38], the results are much improved due to the use of ridge gap waveguide (RGWG) instead of gap waveguide GWG. Structural detail with different layers of the design is shown in Figure 13.  
Figure 12: Geometry of the proposed printed antenna: (**a**) top view; (**b**) bottom view; (**c**) side view; (**d**) isometric view [28].  
Figure 13: Hybrid feed geometry of a \(2\times 2\) unit for ME dipole subarray. (**a**) Perspective view. (**b**) Side view. (**c**) Exploded view [57].
# Detecting Cover Crop End-Of-Season Using VEN\(\upmu\)S and Sentinel-2 Satellite Imagery
## 3 Methods
### WIST Algorithm
#### 3.1.1 Generating Daily NDVI  
The WIST algorithm adopts partial processing from the WISE algorithm, and is represented schematically in Figure 4. First, the discrete remote sensing NDVI data were smoothed and gap-filled in time using a local moving SG filter. A flexible strategy was used to fill temporal data gaps according to the number of valid observations (adjustable) rather than a fixed time window. This strategy uses  
Figure 3: Cover crop fields (yellow polygons) in 2019 (yellow and white labels, 8 fields) and 2020 (green and white labels, 6 fields) over BARC were used in the validation. Four fields (white labels) planted cover crops in both years. Fields #1-37, #4-25, #5-8, and #5-9 (white labels) were planted with cover crops in both years. A total of 29 termination records for these fields in 2019 and 2020 were extracted. The text label near the field shows the field name used in BARC. The green rectangle is the sub-area for displaying near-real-time mapping detailed in Section 4.4. The basemap is a VEN\(\upmu\)S false color composite image (Red: near-infrared band; Green: red band; Blue: green band) acquired on 13 July 2019.  
observations within a large moving window to fill large gaps and uses a small moving window to fill small gaps. This part of the processing is the same as in the WISE algorithm [19]. The local moving window approach is flexible regarding the length of time-series and has no assumption on time-series curves shape as many mathematical functions require. We used different parameters from the WISE algorithm since cover crop termination is a more abrupt process than is emergence, and a large temporal gap may not be able to capture the rapid changes. The minimum number of valid and cloud-free observations for the SG polynomial fitting was set to four (five in WISE). The maximum moving window size was defined as 45 days (60 days in WISE). A smaller moving window size can preserve abrupt changes. If the conditions cannot be satisfied, a large temporal gap will not be filled and may affect the termination detection if the gap period contains a termination. These parameters are adjustable within the WIST approach.
## 3 Conceptualization of Last Mile Logistics
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Terminology** & **Reference** & **Definition** \\ \hline \multirow{4}{*}{Last mile} & [32] & Final leg in a B2C delivery process in which the parcels are delivered to the destination, either at the recipient’s place or at a collection point \\  & [38] & Last part of a delivery process of physical goods from a last transit point to a final drop point \\  & [39] & Distance from the main traffic station, such as rail transit, to the destination \\  & [40] & Last segment of distribution for a delivery with the specific distance \\  & [41] & Transport of goods from a local contact place to a point of consumption \\ \hline \multirow{4}{*}{Last mile delivery} & [14] & Final leg of transport of goods in the supply chain to their consumption point \\  & [42] & Delivery of purchased items to the doors of customers \\  & [43] & Delivery of goods to the home in the last link of the supply chain \\  & [44] & Delivery of parcels to their destination in a city \\  & [45] & Last segment of a delivery process that involves all required activities and processes of the delivery chain \\  & [46] & Delivery from the last upstream transit point to the last recipient \\  & [47] & Transport from the retailer’s local point to the final recipient’s place \\  & [48] & Delivery of items to their final recipient’s point within a city \\ \hline \multirow{2}{*}{Last mile distribution} & [49] & Last part of the supply chain delivery process, including necessary activities from the last transit point to \\ \hline \multirow{4}{*}{Last mile parcel distribution} & [50] & Delivery of parcels from distribution centers or substations to individual addresses \\  & [13] & Movement of goods from a distribution center to the last recipient’s doorstep \\  & [13] & Last stretch of a B2C consignment delivery process \\  & [11] & Last stretch of the logistics system from the last distribution point to the recipient’s preferred final drop point \\  & [1] & Last stage of a delivery from a distribution center to a customer’s place \\  & [52] & Last stretch of a B2C parcel delivery process of goods from a penetration point to the final consignee’s point \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Definitions of LML-related terminologies. that the nature of LML-related research can be categorized into four LML main research areas in Section 4 (see Table 2).
## 3 Results
### HI Results  
The analysis of HI from the 717 quarters of the 24 games (Table 7)--defined as impacts exceeding 8G--using a five-cluster k-means model, reveals the distribution of mechanical load intensity among U14 basketball players. In Zone 1 (<1.13 contacts/min), players recorded 27.20% of all HI, with a cluster center of 0.63 contacts/min. Zone 2 (1.14-2.11 contacts/min) accounted for 23.01% of HI, centered at 1.63 contacts/min.  
Zone 3 (2.12-3.13 contacts/min) represented 24.83% of HI, with a cluster center of 2.58 contacts/min, while Zone 4 (3.14-4.42 contacts/min) comprised 14.64%, centered at 3.67 contacts/min. Finally, Zone 5 (>4.42 contacts/min) accounted for 8.37% of all HI, with a cluster center of 5.15 contacts/min (Table 7). These results indicate that the number of high-impact events are distributed across all zones; the majority occur at low to moderate numbers, with fewer numbers of events at the highest intensities.  
The distribution of HI actions, from the 717 quarters of the 24 games (Figure 7)--defined as impacts exceeding 8G--among U14 male basketball players revealed a gradual decline in frequency across increasing numbers of zones. The majority of HI actions were concentrated in Zones 1 to 3, with Zone 1 (<1.13 contacts/min) accounting for 27.20% of  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**HI (cont/min)** & **Zone 1** & **Zone 2** & **Zone 3** & **Zone 4** & **Zone 5** \\ \hline Cluster centers & 0.63 & 1.63 & 2.58 & 3.67 & 5.15 \\ Ranges & \textless{}1.13 & 1.14 to 2.11 & 2.12 to 3.13 & 3.14 to 4.42 & \textgreater{}4.42 \\ n & 195 & 179 & 178 & 105 & 60 \\ \% & 27.20 & 24.97 & 24.83 & 14.64 & 8.37 \\ \hline \end{tabular}
\end{table}
Table 7: Results of the five-k means cluster of the variable HI.  
Figure 6: Graphical interpretation of results from five-k means cluster of the variable impacts.  
total actions, followed closely by Zone 2 (1.14-2.11 contacts/min) at 24.97% and Zone 3 (2.12-3.13 contacts/min) at 24.83%. Collectively, these three zones represented 76.99% of all high-impact events, indicating that most HI actions occurred at low to moderate amounts.  
Zone 4 (3.14-4.42 contacts/min) comprised 14.64% of HI actions, while Zone 5 (>4.42 contacts/min)--representing the most extreme number of events--accounted for only 8.37% of total actions, with a cluster center of 5.15 contacts/min (Figure 7). This decreasing trend across zones suggests that although U14 players are capable of producing very high-intensity impacts, such occurrences are relatively infrequent during match play.
# PSHNet: Hybrid Supervision and Feature Enhancement for Accurate Infrared Small-Target Detection
## 4 Experiment  
### Experimental Setup and Evaluation Metrics  
To objectively evaluate the proposed PSHNet framework, comprehensive experiments were conducted on two widely used public benchmark datasets: NUDT-SIRST and IRSTD-1k. The NUDT-SIRST dataset contains a total of 1327 infrared images, each sized 256 \(\times\) 256 pixels, which we divided into 995 training and 332 testing samples using a 3:1 ratio. Similarly, the IRSTD-1k dataset comprises 1001 infrared images and was split into 800 training and 201 testing samples following a standard 4:1 ratio, consistent with prior works. To standardize input dimensions while preserving object geometry, all images were resized to 256 \(\times\) 256 using aspect-ratio-preserving interpolation and zero-padding. Data augmentation--including flipping, rotation, and brightness jittering--was subsequently applied to the fixed-size images, ensuring geometric consistency across inputs and labels. This sequence follows common practice in infrared small-target detection tasks.  
The PSHNet architecture was implemented in PyTorch v1.13 and trained from scratch on an NVIDIA RTX 3090 GPU. We employed the AdaGrad optimizer with an initial learning rate of 0.05 and applied cosine annealing over 300 training epochs. A batch size of four was used, and gradient clipping was activated whenever the gradient norm exceeded a threshold of 10 to ensure training stability. The multi-branch output heads were trained using a combination of modified focal loss for the heatmap branch and L1 losses for the offset and size branches. Furthermore, a Complete IoU (CIoU) loss was incorporated as an auxiliary geometric regularization term applied to the reconstructed bounding boxes, promoting spatial alignment and shape consistency without overriding the dense heatmap supervision.  
To assess detection performance, we adopted three widely accepted metrics: Intersection over Union (IoU) for pixel-level localization accuracy, Probability of Detection (Pd) for recall sensitivity, and False Alarm Rate (Fa) to quantify robustness against background noise and spurious responses.
## 2 Establishment of the Calculation Model for FSPR Steady State Temperature Field
### Conformal Mapping and Calculation Model Transformation
Analytical Solution for Freezing Temperature Field Model in the Image Plane of Non-Equidistant Single-Row Tube with Asymmetric Development of Frozen Curtain  
For the A\({}^{\prime}\)-type model in Figure 5a, according to [PERSON]'s single-row tube freezing model theory and characteristics of asymmetric frozen soil curtains, the general form of its steady-state analytical solution can be obtained as:  
\[T=\frac{T_{f}-T_{0}}{\ln\frac{2\pi r_{0}}{l}-\frac{\pi}{l}\cdot\frac{2\xi_{1} \xi_{2}}{\xi_{1}+\xi_{2}}}\left\{\frac{1}{2}\ln\left[2(\cosh\frac{2\pi y}{l}- \cos\frac{2\pi x}{l})\right]-\frac{\pi}{l}\cdot\frac{2\xi_{1}\xi_{2}}{\xi_{1}+ \xi_{2}}+\frac{\pi}{l}\cdot\frac{\xi_{1}-\xi_{2}}{\xi_{1}+\xi_{2}}y\right\}+T_ {0} \tag{8}\]  
Using Equation (8), the solution for Equation group (6) can be obtained as:  
\[T_{1}=\frac{T_{f}-a-T_{0}}{\ln\frac{2\pi r_{0}}{l}-\frac{\pi}{l}\cdot\frac{2 \xi_{1}\xi_{2}}{\xi_{1}+\xi_{2}}}\left\{\frac{1}{2}\ln\left[2(\cosh\frac{2\pi r _{0}}{l}-\cos\frac{2\pi(d+nl)}{l})\right]-\frac{\pi}{l}\cdot\frac{2\xi_{1}\xi_ {2}}{\xi_{1}+\xi_{2}}+\frac{\pi}{l}\cdot\frac{\xi_{1}-\xi_{2}}{\xi_{1}+\xi_{2} }y\right\}+T_{0} \tag{9}\]  
Substituting the boundary condition for B\({}^{\prime}\)-type freezing tubes in Equation group (6) into Equation (9), we get:  
\[b=\frac{T_{f}-a-T_{0}}{\ln\frac{2\pi r_{0}}{l}-\frac{\pi}{l}\cdot\frac{2\xi_{1 }\xi_{2}}{\xi_{1}+\xi_{2}}}\left\{\frac{1}{2}\ln\left[2(\cosh\frac{2\pi r_{0 }}{l}-\cos\frac{2\pi(d+nl)}{l})\right]-\frac{\pi}{l}\cdot\frac{2\xi_{1}\xi_{2 }}{\xi_{1}+\xi_{2}}+\frac{\pi}{l}\cdot\frac{\xi_{1}-\xi_{2}}{\xi_{1}+\xi_{2}} y\right\}+T_{0} \tag{10}\]  
In the same way, the B\({}^{\prime}\)-type model in Figure 5b is equivalent to the A\({}^{\prime}\)-type model where each freezing tube is shifted to the right by a distance \(d\). From Equation (8), the solution for Equation group (7) can be obtained as:  
\[T_{2}=\frac{T_{f}-b-T_{0}}{\ln\frac{2\pi r_{0}}{l}-\frac{\pi}{l}\cdot\frac{2 \xi_{1}\xi_{2}}{\xi_{1}+\xi_{2}}}\left\{\frac{1}{2}\ln\left[2(\cosh\frac{2\pi y }{l}-\cos\frac{2\pi(x-d)}{l})\right]-\frac{\pi}{l}\cdot\frac{2\xi_{1}\xi_{2}} {\xi_{1}+\xi_{2}}+\frac{\pi}{l}\cdot\frac{\xi_{1}-\xi_{2}}{\xi_{1}+\xi_{2}}y \right\}+T_{0} \tag{11}\]  
Substituting the boundary condition for A\({}^{\prime}\)-type freezing tubes in Equation group (7) into Equation (11), we get:  
\[a=\frac{T_{f}-b-T_{0}}{\ln\frac{2\pi r_{0}}{l}-\frac{\pi}{l}\cdot\frac{2\xi_{1 }\xi_{2}}{\xi_{1}+\xi_{2}}}\left\{\frac{1}{2}\ln\left[2(\cosh\frac{2\pi r_{0} }{l}-\cos\frac{2\pi(nl-d)}{l})\right]-\frac{\pi}{l}\cdot\frac{2\xi_{1}\xi_{2}} {\xi_{1}+\xi_{2}}+\frac{\pi}{l}\cdot\frac{\xi_{1}-\xi_{2}}{\xi_{1}+\xi_{2}}r_ {0}\right\}+T_{0} \tag{12}\]  
Since Equations (10) and (12) are relatively complex, they are simplified before the simultaneous solution:  
\[\left\{ \begin{array}{l}a=(T_{f}-b-T_{0})\frac{\eta_{\xi}}{\varphi_{\xi}}+T_{0 }\\ b=(T_{f}-a-T_{0})\frac{\eta_{\xi}}{\varphi_{\xi}}+T_{0}\end{array} \right.
# Targeted Therapy in Metastatic Bladder Cancer: Present Status and Future Directions
## 3 Pharmacological Inhibition of Fibroblast Growth Factor Receptor Signaling
AZD4547 is an inhibitor of FGFR1/2/3, tyrosine-protein kinase KIT (c-KIT), and colony-stimulating factor 1 receptor (CSF1R) with weaker activity against FGFR4 [23]. AZD4547 is currently being investigated in the BISCAY trial (NCT02546661) as part of a biomarker-directed, multidrug phase Ib open-label trial for treatment-relapsed mUC patients. AZD4547 treatment is also examined in the biomarker-directed MATCH Screening Trial (NCT02465060), which is a basket trial including mUC patients. Patients harboring somatic FGFR1-3 mutations or translocations, determined by DNA sequencing of tumor biopsies, will receive AZD4547 treatment until disease progression or unacceptable toxicity occurs. Rogaratinib (BAY1163877) is an FGFR1-4 inhibitor that was found to be well tolerated in a phase I/II clinical trial [24]. Rogaratinib is currently under investigation as a second-line treatment in patients with locally advanced or mUC expressing FGFR1/3 mRNA, identified by in situ hybridization of archival tissue, in the FORT-1 trial (NCT03410693). Derazantinib is a selective FGFR2 inhibitor that is being investigated as a single treatment or in combination with atezolizumab as a first- or second-line treatment in the FIDES-02 trial, which enrolled an estimated 303 patients with mUC harboring genetic FGFR aberrations (NCT04045613). Erdafitinib, a pan-FGFR inhibitor, showed promising results due to its limited toxicity, an overall response rate (ORR) of 40%, and a complete response rate (CR) of 3% in mUC patients with FGFR3 alterations [25]. Based on these results, erdafitinib was granted accelerated FDA approval as a second-line treatment for mUC patients with FGFR alterations. Nonetheless, it remains to be determined whether mUC patients with FGFR1 or FGFR3 overexpression or alternative splicing of FGFR1 benefit from FGFR inhibitors.
## 5 Diagnostic Methods and Techniques
### Non-Invasive Imaging Modalities
#### 5.2.2 Cardiac Computed Tomography (CCT)  
CCT is a diagnostic imaging technique that utilizes X-rays to create detailed cardiac images. This technology can generate 3D geometric data of the entire heart and reconstruct images across different cardiac phases and planes, facilitating accurate evaluation of the LAA anatomy (see Figure 2D). As a result, CCT is considered the \"gold standard\" for visualizing the LAA and is widely employed for obtaining LAA morphological parameters [109]. However, CCT has limitations in distinguishing between thrombi and stagnant blood flow, which can lead to lower specificity in thrombus detection [110]. Additional disadvantages of CCT include significantly higher radiation exposure, the use of iodine-based contrast agents, and lower temporal resolution compared to TEE [111].
# Evaluation of the Use of Compressed Sensing in Data Harvesting for Vehicular Sensor Networks
## 5 Evaluation
### Reconstruction of the Data
(t \(-\) 1)** & **Variance** & **Variance/Avg (Variances)** & **Temp (t)** & **Projection** \\ \hline (1,1) & 16.21 & 5.13113104 & 1.442784308 & 16.45 & 23.73380187 \\ (1,2) & 16.01 & 4.26505104 & 1.199257759 & 16.06 & 19.26007961 \\ (1,3)  
In this execution, the FC has received 51 data packets with some replicated information. So, in Table 11, we present the data in 18 rows without duplicates. Each data packet received by the FC corresponds to each row. Let us focus on the first packet received by the FC, i.e., the first row of the table that we present in Table 12. The sequence of 0 s and 1 s corresponds to the cells of the RoI where nodes took the measurements. The first five elements correspond to the cells of the first row of the RoI, the second five elements to the second row, and so on (see Table 13). On the other hand, the rightmost column provides the projection values associated with the measures received by each node. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c c c c c c c c c c c} \hline \hline \multicolumn{11}{c}{**Obtained Boolean Vector and Associated Projections**} \\ \hline
0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 1 & 1 & 0 & **50.1967**  
As we can observe, the RelError is lower than 0.45% for CSPure and 0.3% for CSAccum. In light of these results, we can also manifest the following two statements: the error in the reconstruction is independent of the TMAX parameter, and that RelError and the density of vehicles are related. This latter relation makes sense because the denser the scenario, the more data the FC receives, and therefore, the more accurate reconstruction it can obtain.
## 2 Materials & Methods
### Knee Actuator Module Design  
Based on a normal person's clinical gait analysis, the torque required for the knee joint in one gait cycle is approximately 40 Nm and 15 Nm in the stance and swing sections, respectively [12]. In a paraplegic patient wearing an EGO, crutches are used to walk, which distribute the load on the knee joint during walking. Therefore, the load on the knee joint in this patient will be lower than in a normal person. In this study, to minimize the system's volume and weight, the actuator output was designed to cover the swing section at a maximum of 20 Nm, and an electric brake was also built in to prevent falls in cases of emergency. Figure 3 shows the developed knee actuation module, and the design specifications are shown in Table 1.  
The low-level motion controller is an integrated control module that includes a motor driver and various sensor interfaces and performs torque control of the knee actuation module. In addition, it acts as a sensor interface that transmits the knee and hip joint angle measured from the potentiometer and the inertial measurement unit (IMU), the ground reaction force (GRF) sensor signal, and the motion status parameters of the actuator to the high-level controller through the controller area network (CAN) interface.
## 2 Materials and Methods  
### Study Area  
In the aftermath of the 2015 Nepal Earthquake, fourteen out of the seventy-five districts were designated as the most severely affected (crisis-hit) areas, including the epicentral area of Gorkha District [2], which is the study area for this work. As shown in Figure 1, the study area is in the central region of Nepal (84\({}^{\circ}\)27\({}^{\prime}\) E-84\({}^{\circ}\)58\({}^{\prime}\) E, 27\({}^{\circ}\)15\({}^{\prime}\) N-28\({}^{\circ}\)45\({}^{\prime}\) N), with an elevation range from 260 m to 8163 m above mean sea level, and covers 3610 square kilometers.  
The annual average precipitation in the study area is approximately 1942 mm, while the temperature fluctuates between 5 \({}^{\circ}\)C and 30 \({}^{\circ}\)C. The area is characterized by mountainous terrain abundant in natural resources, with the local populace predominantly reliant on agriculture for their livelihood. The area spans from mid-hill mountains to high Himalayan mountains, characterized by rugged terrain with deeply incised gullies. Its delicate topography includes steep to moderate slopes combined with tectonic structures that contribute to higher slope instability.  
Geologically, the study area of Gorkha District lies within the Lesser, Higher, and Tibetan Tethys Himalayas. It comprises rocks from the Precambrian to Mesozoic era, including sedimentary, metamorphic, and igneous rocks. Common rock types include phyllites, schists, marbles, quartzites, gneiss, leucogranite, migmatite, sandstone, shale, and limestone [20]. The study area is bisected by the northward-dipping Main Central Thrust (MCT), which serves as the tectonic boundary between the Higher Himalayan Sequence and the Lesser Himalayan Sequence. Historically, the mountainous terrain within the study area has been subjected to a considerable frequency of landslides [9].