esa-sceva/satcom-chunk-collection huggingface dataset:
# Polycrystalline Formamidinium Lead Bromide X-ray Detectors
###### Abstract  
We have investigated the performance of formamidinium lead bromide (FAPbBr\({}_{3}\)) perovskite X-ray detectors fabricated from polycrystalline material that is pressed into a pellet at high pressures. FAPbBr\({}_{3}\) has been shown to exhibit a remarkable combination of electrical and physical properties, such that mechanically-formed polycrystalline pellets exhibit good charge transport properties suitable for use as X-ray detectors. We characterise the morphology and structure of FAPbBr\({}_{3}\) pellets using photoluminescence (PL), electron microscopy (SEM) and X-ray diffraction (XRD), and demonstrate an improvement in the microstructure, density, and charge transport performance of the material as the pressure is increased from 12 MPa to 124 MPa. The use of annealing of the pellets after pressing also improves the stability and charge transport performance of the devices. Using a 40 kV X-ray beam, a maximum X-ray sensitivity of 169 uC Gy\({}^{-1}\) cm\({}^{-2}\) was measured, and the fast time response of the devices was demonstrated using a chopped X-ray beam.  
formamidinium lead bromide; perovskite; X-ray detector +
Footnote †: journal: Applied sciences
# Preliminary Analysis of a Lightweight and Deployable Soft Robot for Space Applications
## 3 Inflatable Link Prototype and Characterization  
A prototype of the inflatable link has been designed to have a cylindrical shape and good air pressure resistance. It consists in a soft cylindrical polyvinyl chloride (PVC) coated fabric, closed by two stiff PVC caps each of them secured by a seal and a metal band to ensure cohesion and avoiding air losses. Compressed air is supplied throughout one of the rigid caps. The link has a length \(L=600\) mm, radius \(r=55\) mm and wall thickness is \(t=0.5\) mm.  
An experimental set-up has been arranged to perform the static characterization, analysing the link as a cantilever. The purpose of the tests is to evaluate the static performance of the link as a function of inflation pressure, evaluating the stiffness constant \(k\), that appears in the robotic arm model. The main set-up of the components is shown in Figure 7.  
The inflatable link is fixed to a rigid structure, and the internal pressure \(p\) is measured by a pressure sensor. During a single test, the pressure is maintained constant by a pressure regulator. In order to ensure the repeatability of the test condition a UR5 robot is used to impose the link a position set \(v_{UR}\), that involves an angular deflection \(\theta\), as schematized in Figure 8.  
Figure 6: Recursive algorithm for inverse kinematics and state estimation using an elastostatic approach.  
The zero position corresponds to the rest position. The contact between the UR5 and the link takes place through a probe, allowing to better identify the distance \(h\) from the fixed joint and the axis of the probe, having minimized the contact surface. During the tests, the UR5 end-effector moves orthogonally with respect to the initial axis of the inflatable link. The exchanged forces \(F\) are measured by a force sensor.
## 4 Experiment and Analysis
### Analysis of Results
#### 4.4.2 Qualitative Results  
We selected and visualized the prediction data for six scenarios, as illustrated in Figure 9. To provide a more intuitive representation of the model's predictive performance, we simultaneously visualized the prediction data for real trajectories alongside those generated by SGC-LSTM, S-LSTM, Sophie, and S-GAN. In our experiments, the historical trajectory data step was set to 8, while the prediction step encompassed 12 time steps, with each time step representing 0.4 s. In scenarios featuring sparse individual flows, panels (a) and (b) demonstrate the model's predictive capabilities. In this context, the predictions generated by the SISGAN model prove to be quite accurate. Notably, in panel (a), the SISGAN's attention module effectively captures interactive data concerning the target pedestrian's left side and the tree on their right, resulting in prediction outputs that closely align with the real trajectory. Panel (c) depicts the target pedestrians walking along the edge of the snow, where only the SISGAN model predicts a trajectory that successfully avoids the snow. This outcome is attributed to the SISGAN scenario module, which employs CNN to map spatial probability density to the semantic information within the region. In panel (d), the pedestrian navigates around a car before continuing along the path. Although some errors occur, the SISGAN model effectively captures the pedestrian's walking intentions. Panels (e) and (f) illustrate scenarios involving dense crowds. In panel (e), while predictions from all models exhibit deviations, the SISGAN model's predictions successfully navigate around pedestrians on both sides, making them the closest to the ground truth.  
Figure 8: Experimental results of different methods. We present the results of different models on all datasets from ETH and UCY in the form of a bar chart. The X-axis represents different trajectory prediction methods, while the Y-axis shows the prediction error of each method across all datasets, with the unit in meters. (a) The Average Displacement Error (ADE) of different methods on ETH and UCY. (b) The Final Displacement Error (FDE) of different methods on ETH and UCY.  
Figure 9: Comparison of trajectory prediction of different models, where (**a**,**b**) are from the hotel dataset, (**c**) is from the ETH dataset, (**d**) is from the zara1 dataset, and (**e**,**f**) are from the zara2 dataset. (**a**) shows pedestrians avoiding obstacles on both sides; (**b**) shows pedestrians’ trajectories in a sparse scene; (**c**) shows pedestrians avoiding snow; (**d**) shows pedestrians walking along the edges of a car; (**e**) shows pedestrians adjusting their paths in the face of a stationary, dense crowd to avoid a collision; and (**f**) shows pedestrians avoiding oncoming pedestrians.
# Enhancement of EPR Effect for Passive Tumor Targeting: Current Status and Future Perspectives
## 3 Morphological Characteristics of the Vascular Network
### Vascular Network Morphology and Hyperavasularity  
The microvascular network of normal tissues appears as an organized structure of mature blood vessels (arteries, arterioles, capillaries, venules, and veins) [74; 76; 77]. In contrast, the tumor vasculature, due to its aggressive growth and overexpression of angiogenic factors, forms a disorganized network of immature vessels with structural and functional abnormalities. While blood flow follows a similar course to normal vessels, tumor vessels exhibit irregular branching and spiral pathways, forming dendritic structures and creating a disorganized maze [74; 76; 78; 79]. Cancer vessels vary in diameter and often appear larger, with discontinuities such as bumps and blind ends, disrupting tissue nutrition [74; 76]. Their shape varies from serpentine to compressed channels with small lumens and includes microvascular outgrowths, vascular complexes, garland formations, and glomerular proliferation [80; 81; 82; 83].  
The tumor vascular network shows significant spatial heterogeneity and hypervascularity, i.e., zones of increased microvascular density (hot spots) in which the likelihood of metastasis is increased [8; 84; 85]. Hypervascularity is associated with EPR, usually in smaller tumors, although it varies depending on the type of cancer [3; 8].
## 4 Performance Testing
### Effective Distance Testing  
In order to follow the only variable principle, the test vehicle was parked in a quiet environment (no obvious noise) with good GPRS signal. The testing distance was changed, and then the control button on the APP was pressed to send instruction for 20 times. After that, each operation time of the controlling instruction was recorded successfully. However, the ratio of success time to press time was called the success rate. Specific details are shown in Table 1.  
When the test distance was within 2 m, the success rate decreased gradually with increasing test distance, and the success rate would be as low as 10% when reaching 2 m. To sum up, (1) the acoustic wave communication distance of vehicle terminal was 1 m, and the effective distance was within the user acceptance range; and (2) when the communication distance was 1 m or less, the probability of successful operation was almost 100%; when communication distance was greater than 1 m, there was a certain probability of successful operation, but relatively low.
## 5 Observation Model
### Case 3  
Figure 12 shows another CT scan with a false positive (a bifurcation in a blood vessel tree). Figure 13 shows its VOI and templates, and Figure 14 the most likely object models. The posteriori probabilities are 0.0960, 0.111 and 0.126, and the ratio is 0.762, that is smaller than the threshold. The candidate is correctly determined to be a blood vessel.  
The candidate region observed in Figure 13(b) seems to be a nodule that has an irregular shape. It is difficult to recognize the candidate region only from the single slice section. However, by considering the relations between the candidate region and the other regions in the adjacent slice sections, it becomes to be easy to determine that the regions arise from a blood vessel bifurcation.  
Figure 11: The most likely object models for the nodule shown in Figure 9.  
Figure 12: A bifurcation in a blood vessel tree.  
instead of Equation (24), the false positive number increases to 60.3 per case. It implies that the blood vessel models play an important role for nodule recognition.  
The geometric averages of probabilities are also important. If the following probability ratio that consists of the ordinary joint probabilities is used:  
\[\rho_{3}(v_{C})=\frac{p\left(o^{N}(\mathbf{\omega}^{N})|v_{C}\right)}{\max\limits_{ \tau\
eq N}p\left(o^{\tau}(\mathbf{\omega}^{\tau})|v_{C}\right)} \tag{26}\]  
the false positive number increases to 34.8 per case. The ordinary joint probabilities suppress all the priori probabilities of bifurcated blood vessel models as mentioned in Section 4., and therefore most bifurcated blood vessel models are ignored in the maximum operation in Equation (26) and the anatomical knowledge on bifurcated blood vessels is not made use of at all. It makes the recognition accuracy degraded.  
The present method needs approximately 40 seconds for recognition of one candidate with a computer that has a 3 GHz CPU. We have already proposed an efficient template-matching method [35], but it uses simple object models that possess less parameters. A more efficient technique are needed for high-dimensional object models used in the present paper.  
Our previous methods [8, 27, 28] are applied to CT scans for initial detection of nodule candidates as mentioned in Section 1. The method [8] is a Morphological [7] image filter that is designed to respond only to blob regions that are major shapes of pulmonary nodules on CT slice sections. The method [27] discriminates pulmonary nodules from false positives based on analysis of image features such as intensities, sizes and shapes. The method [28] recognizes nodules using a principal component analysis based clustering followed by a subspace method [36]. Although these methods use only 2-D image features and therefore yield many false positives, they can complete the recognition in a shorter time. From the practical point of view, we use these initial detection methods before applying the method proposed in the present paper.
## 4 Results and Discussion
### Shisper Glacier (Pakistan), PlanetScope DOVE
In this study, 12-ASTER DEMs were generated from September 2017 to September 2018. Most of the ASTER DEMs failed to provide a complete elevation information over the Shisper glacier due to steep slope (\(>\)40\({}^{\circ}\)) and cloud cover. In order to compute the elevation differences, the multiple generated ASTER DEMs were stacked and compared to the SRTM elevation model. Results showed that 60 \(\pm\) 21 m of ice was increased compared to SRTM and the thickness at the terminus was 127 \(\pm\) 21 m. These results seem reasonably consistent with our measurements. It should  
Figure 14: **(a)** Elevation changes over Shisper glacier computed from the difference of 2019 and 2017 DOYE-C DEMs, overlaid with a hill-shaded DOYE-C 2017 DEM; **(b)** Elevation profiles from 2017 and 2019 Dove-C DEMs. Profiles are extracted along the transect AA’ indicated in panel **(a)**; **(c)** histogram of the elevation difference over stable terrain. be noted that the PlanetScope daily coverage data offers a higher probability to obtain cloud-free images within a short time period than ASTER imagery. Therefore, it overcomes the cloud cover issue. In addition, our image preselection method designed to maximize the convergence angle alleviates the steep slope issue. To reach better vertical accuracy with PlanetScope images, the RFM refinement process described here could additionally make use of GCPs as outlined in [6]. In any case, our study shows that, with our image preselection method for estimating orientation bias, our technique provides a tool to measure topographic changes in areas not easily accessible for ground data collection, or due to past event (an ice surge, or a landslide for example) for which no ground data might exist.
## 3 Results and Discussion
### Evaluation of Worker Safety in EF Famigation  
In the ventilation process, the EF concentration rapidly decreased to <100 ppm (TLV-TWA of EF) within 10 min. However, it required >1 h to decrease the hazardous conditions, which might cause unexpected acute inhalation risk in the workplace (Figure 4b).  
Similarly, the ventilation of EF after fumigation does not leave residual EF inside and outside of a vinyl house [35]. When the EF concentration was measured after ventilation, it  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Applied Dose (g/m\({}^{3}\))**} & \multirow{2}{*}{**Fumigation Time (h)**} & \multicolumn{3}{c}{**EF Concentration (g/m\({}^{3}\))**} & \multirow{2}{*}{**Mortality (Mean \(\pm\) SE, \%)**} \\ \cline{3-3}  & & & **Front** & & **Middle** & **Rear** \\ \hline
0 & - & - & - & - & 2.5 \(\pm\) 0.5 \\ \hline \multirow{4}{*}{35} & 0.1 & 35.3 \(\pm\) 0.5 & 29.1 \(\pm\) 0.6 & 29.6 \(\pm\) 0.5 & \multirow{4}{*}{100 \(\pm\) 0.0} \\  & 1.0 & 34.7 \(\pm\) 0.5 & 26.5 \(\pm\) 0.8 & 27.0 \(\pm\) 0.8 & \\ \cline{1-1}  & 2.0 & 21.5 \(\pm\) 0.6 & 25.5 \(\pm\) 0.5 & 23.9 \(\pm\) 0.7 & \\ \cline{1-1}  & 4.0 & 16.4 \(\pm\) 0.6 & 19.0 \(\pm\) 0.6 & 19.0 \(\pm\) 0.6 & \\ \hline \multicolumn{2}{c}{Ct products (g h/m\({}^{3}\))} & 99.7 \(\pm\) 0.9 & 97.7 \(\pm\) 0.6 & 96.1 \(\pm\) 2.1 & \\ \hline \hline \end{tabular}
\end{table}
Table 6: Mortality of _Halymorpha halys_ adults according to EF concentration and concentration–time (Ct) products by location after 4 h EF fumigation commercial trial in the PVC parapulin fumigation chamber (30 m\({}^{3}\)) on agricultural machinery at 23 \(\pm\) 0.33 \({}^{\circ}\)C.  
Figure 4: Sorption rate and evaluation of worker safety in commercial trial. (**a**) Gas concentration of ethyl formate (EF) in the commercial trial on agricultural machinery (35 g/m\({}^{3}\) for 4 h fumigation). Sorption was expressed as concentration loss ratio (C/C\({}_{0}\)), where C = EF concentration was determined at one of the time intervals, and C\({}_{0}\) = EF concentration was determined at 0 h. (**b**) EF concentration (ppm) under natural ventilation after 4 h fumigation in the commercial trial (23 \(\pm\) 0.33 \({}^{\circ}\)C; red line indicates that the current permeable level of EF is 100 ppm).  
reached much less than the TLV of 100 ppm after 5 min. In the present study, it took only 10 min to reach less than the TLV of 100 ppm, and no EF residue was detected at 1 h after ventilation. In the other study, EF did not exhibit any detectable risk to the public, truck drivers, or workers from 2-day exposure [36]. EF treated in the in-transit containers or inside the cab of the truck was not detected in the environment, and there was minimal risk to workers when the containers were ventilated after a 2-day journey. However, [PERSON] et al. (2019) suggested ventilation of EF after 48 h of in-transit fumigation because of the EF concentration was more than the TLV of 100 ppm [36]. As EF is used for shipment, our data suggest that it can be used to control agricultural insect pests in a vinyl house environment.
## 3 Results
### The Effect of EO Concentration on the Growth of F. grameinerum on Maize and Wheat Grain  
The effect of EOs on the _F. grameinerum_ growth on maize and wheat grains was determined by the measurement of ERG concentration by HPLC analysis (Figure 4). The reduction percentage of ERG in grain samples treated with EO preparations was calculated in comparison to the control samples, where the grains were treated with a mixture of deionized water and Tween 80 without the addition of EO. The ERG was reduced significantly in the concentration range of 1% to 10% by all tested EOs, in both maize and wheat grain. Cinnamon and verbena EOs effectively reduced the ERG content in both wheat and maize grains at the concentration range of 1% to 10% (ERG level reduction in the range of 95-100%, depending on the EO and grain type). Palmarosa EO effectively inhibited the growth of _F. grameinerum_ at concentrations of 5% and 10%, with the ERG reduction at a level of 96-100%. In the 0.1% EO concentration, the reduction in ERG amount was strongly differentiated, depending on both the EO type and the cereal grain. In wheat grain, ERG was reduced at levels of 55-65%, depending on the EO type, while in the maize grain, only cinnamon EO exhibited significant fungistatic activity compared to the control sample.  
Figure 3: Morphology of _F. grameinerum_ hyphae under optical microscope (magnification \(\times\) 400): untreated: (**a**) control, stained with cFDA; (**b**) control, stained with PI; treated with cinnamon EO: stained with cFDA, EO concentration: (**c**) 0.1%; (**e**) 1.0%; (**g**) 5.0%; (**i**) 10.0%; stained with PI, EO concentration: (**d**) 0.1%; (**f**) 1.0%; (**h**) 5.0%; (**j**) 10.0%.
## 2 Materials and Methods
### Measurement Set-Up  
Figure 1 shows the prospective and longitudinal design used for this study. All patients had been diagnosed with medial knee compartmental OA verified by MRI, arthroscopy of the knee and clinical check performed by an experienced (30-40 HTO surgeries yearly) orthopedist. Only one surgeon was included in this examination to ensure the highest level of observation equality.  
In total, 37 patients with symptomatic medial knee compartmental OA were initially (exam 1) recruited between May 2018 and August 2019 in this single-center (SportsClinic Halle) controlled trial. The following inclusion criteria were used:  
* A body mass index (BMI) of less than 40 kg/m\({}^{2}\) based on an age range from 18 to 70 years;
* A tibiofemoral angle of less than 10\({}^{\circ}\) of varus;
* Intact knee ligaments;
* An asymptomatic range of motion (minimum of 120\({}^{\circ}\) flexion);
* Chronic knee pain during rest and motion.  
Patients with instability of the knee ligaments and contralateral knee OA requiring treatment were excluded, as were those with primary patellofemoral OA, bi-compartmental OA, tibial or femoral osteeoncrosis and those with a history of an inflammatory rheumatic disease. The patients did not participate in the study if they were unable to perform the balance tests (posturography) owing to pain or limited motion of the lower limb [17].  
Patients were assessed initially (exam 1) and then again postoperatively at six weeks (exam 2), twelve weeks (exam 3) and six months (exam 4) (authors are currently investigating 2-year follow-up). Between exam 1 and exam 2, the HTO surgery was conducted. The time between exam 1 and HTO surgery was on average 6.38 \(\pm\) 10.2 days (range: 0-40 days). Overall, 34% (11/32) of patients had surgery and exam 1 on the same day; 22% (7/32) of patients had a time interval of at least seven days. The primary reasons for these deviations were mostly organizational issues and the short-term illnesses of patients. The aim of this study was to determine the long-term impacts of HTO and the subsequent standardized rehabilitation and individual treatment (Figure 1, Table A1). All measurements were performed at the same time of day and in a quiet room to minimize any disruptions during testing.
# Gateway-Assisted Retransmission for Lightweight and Reliable IoT Communications
## 4 Simulation Model
Otherwise, just go to Step 18 directly. * Check whether RECEACK_serialNum of the received ACK is less than PUBLISH_serialNum. If the RECEACK_serialNum is less than PUBLISH_serialNum, just go to Step 18 because this ACK is not the one the device is waiting for now. Otherwise, go to Step 17. * The device receives the ACK message. Therefore, numACK is increased by 1, BUSY is set to 0, and the device calculates the RTT according to the received time of the ACK message. Finally, update RTO computed by the gateway and then go to Step 18. * This step checks the ending criterion. The criterion we use is whether the simulation is more than 1200 s or not. If the ending criterion is matched, end the program. Otherwise, go back to Step 3. In our simulation, MQTT-SN [4], CoAP [5], CoCoA [10] and RFC 6298 [8] are also implemented based on relevant documents, and the details are ignored. Note that MQTT-SN uses persistent policy, and the other methods use RRP. In our experiments, we suppose that the fixed RTO value in MQTT-SN is set to 10 s. In CoAP, IoT devices choose a random number from 2 to 3 s as their initial RTO. Additionally, we suppose that the message generation intervals follow exponential distribution with a mean of five seconds. The maximum number of retransmissions in all methods is set to three.
# Single-Class Data Descriptors for Mapping
## 2 Materials and Methods  
### Study Area and Data  
Our study area comprises two independent blocks which are situated in Wenshan City, Wenshan Prefecture, Yunnan Province, in the south-west region of China (see Figure 2). These places mainly lie between longitude 103.71\({}^{\circ}\) E-104.46\({}^{\circ}\) E and latitude 23.07\({}^{\circ}\) N-23.73\({}^{\circ}\) N. Wenshan Prefecture is on a plateau, where the temperatures are quite constant throughout the year, with more precipitation during the summer months. Due to its low latitude and tempered by its high elevation, Wenshan Prefecture has a mild, humid, and subtropical climate, particularly suitable for planting _Panax notoginseng_. This is the reason why Wenshan Prefecture is the specific geographical location and origin of _Panax notoginseng_ (i.e., why it is called a geoherb).  
The \"sa\" and \"sb\" are two typical square regions. There are 151 \(\times\) 151 pixels in the image space, respectively, corresponding to an area 20.5209 km\({}^{2}\) or equivalent to 2052.09 ha for both. The two blocks have a typical representation of the dense _Panax notoginseng_ fields, upon which mapping _Panax notoginseng_ fields will be carried out using multiple SCDDs. Multi-spectral cloud-free images acquired by the Landsat-8 Operational Land Imager (OLI) at a 30 m spatial resolution were utilized in this study. Their acquisition date was 18 March 2015. Since only one scene (path/row: 128/044) was utilized for the analysis in this study, the atmosphere can be considered to be homogeneous, and therefore the atmospheric correction may be not necessary [41; 55]. Note that the planting fields of _Panax notoginseng_ are rather sparse in most cases, and the cloud-free satellite images are not easy to collect because of the special geographical environment (i.e., a mountainous area is often in heavy clouds, refer to Table 1).