esa-sceva/satcom-chunk-collection huggingface dataset:
# Atom Probe Tomography for Catalysis Applications: A Review
## 3 Case Studies
### Adsorbate-Induced Segregation on Pd-Au  
Pd-Au specimens, with a composition of Pd-38 at.% Au, were studied after NO gas exposure at different temperatures and 60 mbar [52; 91; 102]. The sample preparation was performed using the electropolishing method from a Pd-38 at.% Au wire, using an aqueous solution of 20% KCN with 3 V\({}_{\text{DC}}\). When the Pd-Au specimen undergoes thermal treatment up to 573 K under vacuum conditions, we can observe a homogeneous distribution of Pd and Au, showing no segregation. However, exposing the same specimen to NO at 573 K for 15 min leads to Pd surface segregation: the initial sample composition of Pd-38 at.% Au becomes Pd-20 at.% Au. Further 1 DAP experiments demonstrated the dissociation of NO on similar specimens, and the segregation behavior can thus be explained by the occurrence of NO dissociation on Pd-Au, with the formation of strong Pd-O bonds as compared to Au-O bonds at the surface [103]. This, then, is an adsorbate-induced segregation which is thermodynamically driven. Finally, exposing the same specimen to NO at 300 K for 15 min did not induce any segregation/depletion phenomena, confirming that the segregation is thermally activated.
## 2 Materials and Methods
### Crop Model--Dssat
#### 2.5.2 Soil Data  
The soil module comprised data from four sub-modules, namely, soil water, temperature, nitrogen, and dynamics, which were updated daily for each soil layer [45]. Soil plant atmosphere (SPAM) analyses root water absorption, potential evapotranspiration (ET), soil evaporation, and plant transpiration from soil, plant, and atmosphere inputs. ET can be estimated using Priestley Taylor or Penman-FAO [47]. DSSAT requires soil parameters, such as soil texture, sand, silt, clay percent, pH, bulk density, EC, cation exchange capacity, and organic carbon percent. These parameters were obtained from ISRIC 2.0 m [48], at a spatial resolution of 250 m. With the use of Google Earth, field-wise data were collected for each field at depths of 0 to 200 cm. Using the general soil database, additional characteristics, including depleted upper and lower limits and saturation limits, were determined based on the soil texture pedo-transfer characteristics provided in the DSSAT models.
## 4 Results and Discussion
### Thermal Expansion  
Thermal expansion and shrinkage occur in ordinary Portland cement concrete. Like ordinary Portland cement concrete, PC also sees thermal expansion, which occurs when the temperature of the hardened mixture rises; thermal shrinkage occurs when the temperature of the hardened mixture decreases. Thermal expansion includes linear and volumetric thermal expansion. The magnitude of the thermal expansion depends on differences in the crystalline and molecular structures of the material [35]. The polymerization of a polymer and changes in temperature are two main elements that change the volume of PC in a structure. If the stress generated by the volume strain exceeds the allowable tensile stress of the PC mixture, cracking will occur. The thermal strain and linear thermal expansion determined in this study are shown in Figures 10 and 11, respectively. By type of hardener, the coefficient of thermal expansion of the AAMT hardener ranged from \(18.2\times 10^{-6}\) to \(22.1\times 10^{-6}\) per \({}^{\circ}\)C, and for the CAMT hardener from \(24.3\times 10^{-6}\) to \(27.4\times 10^{-6}\) per \({}^{\circ}\)C. By filler type, the order from highest to lowest coefficient of linear thermal expansion was SF, GCC, BFS, and FA. The cause of the difference by type of curing agent is likely due to the different chemical compositions. The differences by filler type are likely closely related to the thermal expansion properties of each filler's chemical composition. To determine these causes more accurately, further study is required. According to the results of [PERSON] [31], the coefficient of thermal expansion of EP mortar ranged from \(20\times 10^{-6}\) to \(50\times 10^{-6}\) per \({}^{\circ}\)C, which is larger than the coefficient of thermal expansion obtained in this study. In comparison, the coefficient of thermal expansion of hardened Portland cement concrete is very small. [PERSON] [36] reported the value as ranging from \(6.3\times 10^{-6}\) per \({}^{\circ}\)C to \(11.7\times 10^{-6}\) per \({}^{\circ}\)C. [PERSON] [31] reported the coefficient of thermal expansion as ranging from \(7\times 10^{-6}\) to \(14\times 10^{-6}\) per \({}^{\circ}\)C. The results of these studies indicate that the coefficient of thermal expansion of epoxy resin polymer concrete is about twice as high as that of ordinary Portland cement concrete [31]; this aligns with the results of the present study. The cause of this significant thermal expansion could be high temperatures resulting from polymerization in the epoxy resin polymer concrete.  
Figure 9: Setting shrinkage by hardener and filler type.
# Improved Algorithm for Vehicle Bottom Safety Detection Based on YOLOv8n: PSP-YOLO
## 2 An Improved YOLOv8n Algorithm: PSP-YOLO
### P2 Small-Object Detection Head  
The YOLOv8n network includes detection heads for small, medium, and large object sizes. However, in under-vehicle scenarios, where the vehicle's underside is relatively large, objects such as knives, guns, and bottles are considered small targets. YOLOv8n struggles with accurately detecting these small objects, often resulting in poor recognition or missed detections. To enhance the detection capability for small dangerous objects, a tiny-object detection head branch optimized for the P2 layer is introduced into YOLOv8n.  
Figure 1: Structure diagram of PSP-YOLO.  
The P2 layer, positioned relatively high in the Feature Pyramid Network (FPN), provides higher-resolution feature maps. This high resolution aids the model in better locating and distinguishing small targets, which is crucial for capturing subtle changes and fine details within the image, making it particularly effective for small-object detection. However, while the P2 layer excels in localization, it has limitations in classification because the shallow features it provides are less sensitive to the overall semantic content of the image and lack contextual information.  
To enhance detection performance, shallow features from the P2 layer are combined with deep features obtained through upsampling. This fusion leverages the deep features' strong semantic understanding of the image, and the detection performance for small objects is significantly improved.  
As illustrated within the dashed rectangle in Figure 2, the introduced detection head branch is depicted. This paper integrates features from the P2 layer with deep features obtained through upsampling to create the P2 small-object detection branch. This approach preserves the sensitivity to fine details of small targets while also utilizing the contextual information provided by higher-level features. This fusion enhances the differentiation between the targets and the background, resulting in more accurate detection of dangerous small objects and effectively addressing the issue of missed detections in under-vehicle scenarios.  
Figure 2: Structure diagram of the P2 small-object detection head.
## 5 Conclusions  
In this work, a CH\({}_{4}\) sensor with the DA-OA-ICOS and WM-OA-ICOS measurement modes was proposed. To enhance the performance of the sensor, a small-size optical resonance cavity with a length of 7 cm was designed. An effective optical path length of 10.8 m and a cavity volume of 8.9 mL were obtained, which could realize rapid and efficient gas measurements with minimal gas samples. In addition, the cavity mode noise of the CH\({}_{4}\) sensor could be effectively suppressed by the application of the white noise perturbation of 30% modulation depth. The performance of the two measurement modes was compared by experiments. In DAS mode, the SNR of the OA-ICOS was 16.2 and the Allan deviation analysis of MDL was 2.2 ppm at 117 s. In WMS mode, the SNR of the OA-ICOS was 113.9 and the Allan deviation analysis of MDL was 1.2 ppm at 106 s. The results of the WM-OA-ICOS compared to the DA-OA-ICOS were improved 7.0 times in SNR and 1.8 times in MDL. The significant advantages of the WMS mode for CH\({}_{4}\) measurement were demonstrated. The long-term (3 h) measurement experiments showed that the concentration measurement in WMS mode was more stable than in DAS mode. The developed sensor achieved high sensitivity measurement at low concentrations and demonstrated wide application potential. In future work, the performance of the sensor can be further enhanced by optimizing the structural design of the optical resonance cavity and combining with the software for noise reduction.  
Writing-review and editing, C.Y., M.W. and C.C.; software, C.L.; validation, J.H.; formal analysis, L.S.; investigation, Y.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Key R&D Program of China under (Grant No. 2024 YFC2814300), in part by the Science and Technology Department of Jilin Province of China under Grant 20240304195 SF, in part by the Industrial Technology Research and Development Project of Jilin Provincial Development and Reform Commission under Grant 2024C007-5.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to data must not be used for commercial purposes.  
The authors declare no conflicts of interest.  
Figure 9: The concentration data were collected by the sensor under the DAS mode with the white noise perturbation (black) and the WMS mode with the white noise perturbation (red).
## 4 Results and Discussion
### SAR Data
The comparison shows that the highest producer accuracy was obtained when the texture features were included (Tables 6 and 8). It can be observed from Tables 4 and 5 that, although the producer accuracy increased for the bare land, built-up 1 and built-up 2 classes when the SAR backscatter values in classification is used instead of the Landsat 8, the producer accuracy decreased for the built-up 3 and vegetation classes from 27.1% and 63.8% to 15.6% and 59.6%. However, when texture features are included, an improvement is observed for all the land cover classes. Thus, in our study area, classification of land cover using SAR backscatter and texture feature is superior in terms of accuracy than classification from the Landsat 8 and SAR backscatter only. Furthermore, the user accuracy increased in all classes except vegetation for the ALOS-2-PCT. In case of the Sentinel-1, the user accuracy for the dataset including the texture measures improved in all the classes comparing with the dataset of backscattering values only and there was no improvement for the bare land and vegetation comparing with the Landsat 8 dataset. Figure 12: SVM classification results of backscatter values and their PCTs from the ALOS-2 image using a window size of 13 \(\times\) 13 (**a**) and the Sentinel-1 image using a window size of 11 \(\times\) 11 (**b**). The black frame represents the location of Figure 13.  
Figure 13 shows a closer look of SVM classification for the ALOS-2, Sentinel-1, ALOS-2-PCT, and Sentinel-1-PCT. The location of the area is shown in Figure 12. The improvement in the accuracy mentioned before can be appreciated visually in this figure. The most remarkable observation is that when using texture measures of the SAR data for classification, the producer accuracy improved significantly, therefore, the amount of noise and misclassified pixels for all classes decreased and the classes become more uniform comparing to that using only SAR backscatter values or optical data.
## 3 Results and Discussion
### Organoleptic Evaluation  
The results of the organoleptic evaluation of fat-free dairy desserts enriched with different amounts of dried vegetable powders are presented in Figure 6. Prepared final products were manufactured and stored overnight and then evaluated. Panelists (untrained) noticed differences between the tested products. The highest scores among all tested samples were noted in the product with championon (3 and 5%) and carrot (3%) addition. Panelists described the structure of tested products as uniform and without lumps. The aroma and taste did not contain a foreign aftertaste and were better than other samples. Products with the addition of 5% onion powder and 5% betroot powder obtained the lowest scores among all tested product samples for each of the tested features. These results may be associated with a firmer consistency and characteristic flavor connected with added vegetable powders. The panelists described the taste of the combination of whey protein concentrate (WPC80) with the tested powders as incompatible.  
There is a lack of research describing the organoleptic evaluation of dairy desserts based on WPC80 with the addition of dried vegetable powders. Typical dairy products that are comparable to the presented formula are yogurts. However, adding vegetables to the formula is not popular, even among different types of yogurt. It is connected with consumers' use of yogurts with fruits or fruity flavors. Most commercially available products are sweetened. Research suggests that consumers are more likely to give higher rates to available products [58]. Prepared products differ from typical commercial desserts, and repeated exposure can improve overall acceptance. In the study from 2004, tested yogurt, which was supplemented with different amounts of carrot juice, was acceptable due to its sweet and pleasant taste [59]. In our study, a product with 3% dried carrot powder addition received one of the highest scores.
# Resilience of LTE-A/5G-NR Links Against Transient Electromagnetic Interference
## 1 Introduction  
Wireless systems such as 5G-NR and LTE-A operate in complex propagation environments and are susceptible to EMI, such as transients generated by high-power generators or emissions from loose pantograph-catenary systems, which degrades their performance [1, 2]. This paper aims to establish the impact of transient EMI on the susceptibility of LTE-A/5G-NR communication links.  
Transient EMI can cover a wide range of the spectrum, including Wi-Fi and cellular bands. Hence, EMI can affect both the primary user link from the eNB (evolved Node B)/gNB (next-generation Node B) to the user equipment (UE) onboard and on the platform. It can also affect the performance of secondary wireless links, such as WiFi access points (APs), to onboard users. It can also introduce additional problems in spectrum-sharing scenarios where primary and secondary user networks coexist and use energy detection to detect the presence of primary users. Transient EMI sources are difficult to locate. Furthermore, transient EMI can appear to be random and inconsistent, depending on the physical state of the fault location. The intermittent contact between the catenary and pantograph generates a wideband signal capable of jamming communication links across a broad range of frequencies. This paper aims to investigate the effects of unintentional interference on these communication links.  
The key performance indicators (KPIs) used to measure these effects are error vector magnitude (EVM), bit error rate (BER), and signal-to-interference-plus-noise ratio (SINR). We divided our work into five sections. Section 1 is the introduction. In Section 2, we discuss the background of the problem and highlight the latest contributions. In Section 3,we introduce the setup of the experiment, our methodology and explain KPIs. Section 4 is about the measurement results and the effect of transient interference on LTE-A and 5G-NR. The conclusion of this study is given in Section 5.
# NFV/SDN as an Enabler for Dynamic Placement Method of mmWave Embedded UAV Access Base Stations
## 3 Architecture and System Models
### System Models  
As described in the previous section, this paper examines access UAVs. The backhaul [4, 5] is assumed to be ideal for a separate study. When the entire system is considered, the joint optimization of both backhaul and access UAVs against a fixed user distribution might have a significant impact on the overall system's performance [36]. However, due to the moving freedom of the UAVs, we can properly optimize the backhaul UAV placement to fulfill the required communication rates of the fixed access UAVs, as conducted in our previous work [4]. For this reason, for ease of investigation in this study, we separated the discussion of the backhaul UAV placement from that of the access UAV placement problem, such that the placement of the latter UAV will merely depend on the provided user distribution. The joint optimization of both access and backhaul UAVs will be investigated in our future work.  
In this paper, we assume that users always request data (full-buffer model). It is also assumed that the user distribution is known. In recent years, there has been much research on estimating user distributions, e.g., [37], so the estimation of user distributions is outside the scope of this paper. Therefore, we do not limit ourselves to a user's specific application but aim to improve the offered data rate in each situation.
## 2 Related Work
### Deep Learning-Based Keypoint Estimation Model  
To train a deep learning model to detect size measurement points, numerous clothing images in which pixel-level coordinates of measurement points are annotated for clothing items are necessary. In 2019, the DeepFashion2 dataset [16] published by The Chinese University of Hong Kong and SenseTime Research consisted of 491,000 images with a total of 294 annotated landmarks and 13 clothing categories. Landmarks are key characteristics of the outlines of clothes and the markers include size measurement points that are unique according to the type of clothing (e.g., left and right armpits and hem), as shown in Figure 11. This dataset contains much richer clothing types and keypoint information than previously known fashion datasets [17; 18].  
Regarding the point estimation problem, a high-resolution network (HRNet) [19] is a state-of-the-art deep neural network architecture. In 2019, it proved its effectiveness in the human pose estimation problem that predicted unique marked keypoints for each part of the human body. This model architecture is suitable for detecting different sizing points for each clothing type. In the landmark estimation competition hosted in the DeepFashion2 dataset, the models using HRNet architecture ranked first [20] and third [21], proving its potential as a sizing point detector. Inspired by the approaches, our team also adopted a HRNet-based keypoint detection model as a sizing point detector.  
HRNet has advantages over previous architectures [22; 23; 24; 25; 26; 27; 28; 29] through two different approaches. First, HRNet connects high-to-low-resolution subnetworks in parallel instead of in series. It efficiently preserves high representations, whereas previous approaches used low-level resolution to restore high representation. In addition, this architecture uses repeat multi-scale fusions. Unlike previous fusion schemes that simply aggregated low and high representations,this method keeps both representations at a similar scale. The high-resolution representations learned from HRNet are spatially precise.
###### Abstract
The RMSE of the proposed algorithm was less than 0.1 regarding used metrics, including playtime, average buffering, or buffering frequency [21]. This study aimed to investigate whether the online clustering methods can be used to assess the customer QoE level in the production TV system based on adaptive streaming technology. The evaluation was grounded on the data-driven approach and parameters that include stall occurrence, bitrate level, frequency of quality switches, time on the decreased bitrate, or stalling ratio. The event logs gathered from the set-top-box devices are related to bitrate level, and rebufferings were employed. A continuous data processing method which refers to online clustering was chosen since it analyzes the devices operating in a production television system in real-time. In the end, the QoE level outcome was applied to study the degradation within specified geographical areas based on aggregation routers across Poland. The primary motivation for this was to create a simple system that would allow service providers to assess the perceived quality of live TV streaming in their system. The assessment would be performed in real-time based on data collected from set-top boxes. The active number of devices at a particular time requires an analysis based on logs indicating what happens on the decoder. Of all the data, the most relevant is information about the video bitrate level, stallings, the start and end of watching an event, and the use of additional features. The analysis should be performed in real-time and return the result that can be checked against customer requests later. Based on the collected data, the operator would geographically locate faults and, as a result, in the time of an increased influx of errors, more resources can be redirected by the operator to correct the degradation which has occurred. The article is divided into four sections. In the second section, the gathered dataset, quality of experience assessment scale, as well as television background followed by clustering features has been provided. The third section covers the results of the geographical area study. The last section provides a discussion along with the proposed scope of future work.
## 4 Discussion
Compared to the control, wound healing was accelerated by 2.58 days, neoepidermal thickness improved by 32.40 \(\upmu\)m, and the epidermal cell density improved by 44.88 cells. Additionally, based on the results from the various treatment conditions, the enhanced wound-healing effects of rhGDF-5 were found to be dose dependent. For example, the local application of 500 ng and 1000 ng rhGDF-5 improved wound healing more than 100 ng rhGDF-5. However, 5000 ng rhGDF-5 did not further improve wound healing, possibly after having reached saturation. rhGDF-5 was not found in the analyzed blood serum. This shows that the local application of rhGDF-5 did not lead to measurable systemic doses. To ensure comparability, the values obtained from the rhGDF-5-treated groups were compared to untreated controls and the commercially available collagen-elastin matrix, which has been successfully used previously in full-thickness skin defects and showed positive long-term results [26, 27, 28, 29, 30, 31]. The collagen-elastin matrix is mainly used in combination with a simultaneous application of autologous split-thickness skin grafts for full-thickness skin defect cases [26, 27, 28, 29, 30, 32, 33, 34, 35]. In our study, the collagen-elastin matrix was not used in combination with skin graft transplantation to ensure comparability with our product, which does not use skin grafts. After comparing all the conditions, the most optimal results were seen for wounds treated multiple times with 500 ng rhGDF-5 embedded in the 75 g/m\({}^{2}\) collagen-gelatin scaffold. For future investigations, additional detailed histologic evaluations at different stages of wound healing, in order to obtain more detailed information regarding the skin regeneration process between the control and treated groups, could result in stronger statistical differences for those not statistically significant.