esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### X-Ray Diffraction (XRD)  
An analysis of the X-ray diffraction (XRD) spectra of native and modified starches (cassava, potato, wheat, corn, pea, and rice) revealed key crystalline characteristics. The XRD spectra demonstrated notable differences in the position and intensity of the main peaks, allowing the determination of crystallinity and the impact of thermal modification on physical and mechanical properties [36].  
As shown in Figure 3, highly crystalline starches such as potato and corn are characterized by strong peaks in the 20 range of approximately 17\({}^{\circ}\) and 23\({}^{\circ}\), indicating a high degree of molecular order. This structure enhances the material's strength and stability, making these starches suitable for applications requiring durable films with high mechanical resistance [43]. However, it is important to note that the high crystallinity observed in both native and modified starches may reduce biodegradability, which should be considered when selecting applications [44].  
In contrast, moderate- and low-crystallinity starches such as pea and rice exhibit lower peak intensities within these ranges, indicating a less ordered structure. The XRD analysis confirms that while high crystallinity (e.g., in potato and corn starches) improves mechanical properties, it also reduces biodegradation rates. Conversely, low-crystallinity samples (e.g., pea and rice starches) exhibit greater biodegradability, making them more suitable for environmentally friendly and short-term applications [45].
## 5 Measurements Processing & Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**RSSI** & **2.4 GHz 802.11 ax** & **2.4 GHz 802.11n** & **5 GHz 802.11 ax** & **5 GHz 802.11 ac** \\ \hline \(-\)80 dBm & 56,282 & 51,442 & 47,556 & 42,555 \\ \(-\)70 dBm & 24,711 & 20,418 & 18,316 & 16,231 \\ \(-\)60 dBm & 10,135 & 12,304 & 7023 & 7234 \\ \(-\)50 dBm & 3560 & 4704 & 996 & 1245 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Area in square meters that has an RSSI greater than the value listed in the first column for different frequency bands and WiFi standards derived from the radio environment maps (REMs) of Figures 8 and 9. Figure 9: RSSI and download speed heat maps for the 5 GHz band of 802.11 ax & 802.11 ac technologies overlaid on top of the satellite image of Figure 6.  
With respect to LoRaWAN, we are primarily interested in documenting the achieved communication range in the presence of obstacles, since it is included in our solution for providing an alternative communication channel for long distances or obstructed paths. Figure 10 presents a series of geolocalized RSSI samples that were taken alongside the WiFi measurements. LoRaWAN was able to establish communication between the concentrator and the node over distances exceeding 0.5 km in the presence of large buildings in between. It is, therefore, possible to act as a low bandwidth, high availability communication channel for distress or critical messages. This was achieved while using a spreading factor SF7, which results in a data rate of 5.5 kbps and receiver sensitivity of \(-123\) dBm. The communication range can be extended further if higher spreading factors are selected. However, this comes at the expense of a lower data rate and higher airtime usage. LoRaWAN utilizes an unlicensed spectrum with duty cycle restrictions, and higher spreading factors will limit the amount of data that can be exchanged.
## 2 Materials and Methods  
### Thin Film Deposition  
The controlled introduction of impurities into a material in order to manipulate its properties, in bulk or as thin films, is an essential tool in building electronic devices. The CuO films, pure or Al-doped, needed for the experiments presented herein have all been grown via chemical spray pyrolysis. An eco-friendly, cost-efficient process, it is a method of thin film growth consisting of spraying a solution onto a heated surface in order for the constituents to react and create the desired chemical compound [15; 16; 17; 18; 19; 20]. All the initial chemical reagents are chosen so that the products, other than the desired chemical compound, are volatile at the deposition temperature or during annealing. This technique is effective and uses simple equipment and is therefore very profitable. One of the most important aspects is that it requires an atomizer or nozzle. Thus, a precursor solution is pulverized into small droplets that are transported to a heated substrate and thus generate thin films. One should note that this procedure influences the surface properties of the film and the morphology of the material. Although in the literature, one might observe that most authors use inorganic salts, especially chlorides, as precursors, the precursors used in this work are salts of organic acids. Therefore, the precursor solutions were made using either 0.10 mole of cupric acetate--Cu(CH\({}_{3}\)COO)\({}_{2}\) solubilized in deionized water (90 mL)and pure ethanol (10 mL) or aluminium acetate--Al(CH\({}_{3}\)COO)\({}_{3}\) at the same molarity and a different percentage (0, 0.50, 0.75 and 1%). The substrate-to-nozzle distance was set at 25 cm, pumping at 1 bar (working air pressure during spray). The solution was then sprayed on cost-efficient, optical-grade, borosilicate-crown glass substrates, heated to 350 \({}^{\circ}\)C. The substrates, thoroughly cleaned with isopropanol, ethanol and water and finally dried in constant airflow, were continuously monitored during the deposition using a standard thermocouple. Control of the solution flow rate throughout the spraying procedure is of paramount importance. This was set at approximately 1 mL/min for a 5 min deposition time and kept constant throughout the experiment.
## 2 Materials and Methods  
### Cell Culture  
HeLa cells were cultured in Dulbecco's modified Eagle's medium (DMEM), high glucose with L-glutamine (PAA Laboratories), supplemented with 100 mg mL\({}^{-1}\) gentamicin, 12 mg mL\({}^{-1}\) ciprofloxacin, and 10% of heat-inactivated fetal calf serum (FCS) in a CO\({}_{2}\) incubator. Cells were passaged at a ratio of 1:5 twice per week. For the risk assessment assay, HeLa cells, cultivated in 48 well plates at a confluent cell density of 0.12 \(\times\) 10\({}^{6}\), were treated as described in Table 1. For all electrode experiments, previously described structured ceramic BDD electrodes were applied [18]. For direct application, an electrode with an active surface of 3 \(\times\) 4 mm was used in two modes: (i) potentiostatic at 6-9 V with increments of 1 V for 2.5 min (resulting in 2 to 35 mA); (ii) amperostatic for 5 min at 50 mA (6-7 V). For indirect application, an electrode with an active surface of 6 \(\times\) 8 mm was mounted to the bottom of a UV/Vis cuvette. A current of 50 mA at 10 to 12 V was applied to 3 mL of PBS for indirect application. A conventional laboratory power supply was used for running the electrodes. An Alamar Blue assay (Alamar Blue HS, Invitrogen, Fisher Scientific, Wesel, Germany) was performed according to the manufacturer's instructions.  
\begin{table}
\begin{tabular}{c l} \hline \hline
**Group** & **Treatment** \\ \hline Blank & HeLa Cells at confluency treated with 0.5 mL PBS for 2.5 or 5 min \\ \hline Potentiostatic & Application of BDD electrode inside the well plates (direct application) for 2.5 min in 0.5 mL of PBS overlaying the cells \\ Parameters: & \\
6 V & Direct application at 2-12 mA \\
7 V & Direct application at 5-18 mA \\
8 V & Direct application at 7-29 mA \\
9 V & Direct application at 11-34 mA \\ \hline \hline \end{tabular}
\begin{tabular}{c l} \hline \hline
**A**uperostatic & Application of BDD electrode in 0.5 mL of PBS for 5 min \\ Direct & Application at 50 mA for 5 min in 0.5 mL of PBS overlaying the cells \\ Indirect & Application at 50 mA by adding 0.5 mL pretreated PBS buffer for 5 min \\ NaCl & Application of 0.5 mL of physiologic NaCl solution for 5 min \\ \hline \hline \end{tabular}
\begin{tabular}{c l} \hline \hline
**H\({}_{2}\)O\({}_{2}\)** & **Application of 0.5 mL 3\% and 0.3% H\({}_{2}\)O\({}_{2}\)** (Otto Fischer GmbH \& Co. KG, \\ Saarbrucken, Germany) for 5 min** \\ \hline NaOCl & Application of 0.5 mL.3\% and 0.3% NaOCl (Heidinger, Stuttgart, Germany) for 5 min \\ \hline \hline \end{tabular}
\begin{tabular}{c l} \hline \hline
**C**HX & **Application of 0.5 mL chlorohexidine digluconate (Chlorohexamed FORTE alkoholfrei 0.2\%, ClaxoSmithKline Consumer Healthcare, Munich, Germany) for 5 min \\ \hline \hline \end{tabular}
\end{table}
Table 1: Overview of experimental groups and details of treatments rendered during cell culture experiments.
# Microseismic Signal Denoising and Separation Based on Fully Convolutional Encoder-Decoder Network
## 2 Methods and Network Training
### Data Preparation and Network Training  
In this paper, 7500 microseismic signals with high SNRs and 15,000 noise samples were selected to form the dataset, which was randomly split into training (80%), validation (10%), and test (10%) datasets. The validation set is used to determine the hyperparameters and prevent over-fitting of the network from achieving the best results, and the test set is primarily used to evaluate network performance. The amplitude of the recorded signals is in voltage value, and the response frequency ranges from 50 to 5 kHz. The data acquisition station, located in the Micang Mountain tunnel in China, worked at the sampling frequency of 20 kHz and a sampling window of 1.5 s, which results in all signals having the size of 30,000 sampling points. The noisy input signal for training were generated through the training dataset and from the noisy signal with different SNRs by superimposing the selected microseismic signals with randomly selected noise samples on each iteration. The network was trained on NVIDIA GTX 1060 GPU with Adam optimizer and the learning rate of 0.001. Moreover, the noisy signals, recorded in the Zijing tunnel (China) by the microseismic monitoring system with the same parameters as Micang Mountain tunnel, were applied to the well-trained model for validating its versatility.
## 2 Method and Approach
### Graph Construction with Topological Segmentation  
Once suitable paths have been found in the 2D image, a graph can be derived directly from them. For this purpose, a so-called topological segmentation is first performed. Based on the neighborhood condition of the individual points \(p_{i}\in\ S\), the respective disjoint set of endpoints \(W_{e}\), branch points \(W_{j}\) and skeleton points \(W_{s}\) can be assigned [34]. If a point is 8-adjacent with at least 3 other points, it is classified as an intersection point. In summary, all points \(p_{i}\) from the skeleton \(S\) can be classified as  
\[p_{i}\in\ S\backslash A\ \left\{\begin{array}{l}|N_{n}(p_{i})\geq 3|\ p_{i} \in W_{j}\\ |N_{n}(p_{i})=2|\ p_{i}\in W_{s}\\ |N_{n}(p_{i})=1|\ p_{i}\in W_{e}\end{array}\right. \tag{2}\]  
with \(S=W_{j}\cup W_{s}\cup W_{e}\) (see Figure 3) [34]. These different types of points can then be included in \(S\backslash A\) to find suitable paths as pixel chains \(J_{k}\). Here, both the first and the last point of the chain are either an end point or a branch point. The points between the start and end points are all skeletal points. Thus, the path can be defined as the set of 8-adjacent points \(p_{i}\) that are ordered with respect to each other as follows  
\[J_{k}=\left\{p_{i},p_{i+1},\ldots,\ p_{n}\right\} \tag{3}\]  
with \(\left\{p_{i},\ p_{n}\right\}\subset W_{j}\cup W_{e}\) and \(p_{i+j}\in W_{s}\) for \(j=\left\{1,\ldots,n-1\right\}\) (see Figure 3). Based on that segmented skeleton aa digital twin including the associated information such as the tasks can be derived. For this purpose, the pixel chains are first simplified using a so-called collapse metric. It is ensured that by simplifying the path \(J_{k}\), the distance \(d_{i}\) of a pixel to the simplified polyline \(P_{k}=\left\{p_{i+1},\ \ldots,p_{n-1}\right\}\subset J_{k}\) may differ by at most \(\epsilon\)-pixels (see Figure 3). In this simplification, each step checks that the topological properties are preserved. The following figure shows the segmentation of each point based on neighborhood connectivity, the segmentation of each path based on branch and endpoints, the collapse metric, and the simplification to a graph. Branch points, which are connected to each other (green), are reduced to one point.  
Subsequently, a graph \(G=(V,E)\) consisting of nodes \(V\) and edges \(E\) can be generated. For the edges of the graph, all information along the paths are used, while the tasks as well as the robots themselves are mapped via the nodes. This graph is now supplemented in the following by the passage width at the respective edges.
# On Reliable and Efficient Data Gathering Based Routing in Underwater Wireless Sensor Networks
## 11 Simulation Results and Discussions
### Non Optimized and Delay Optimized Protocols
#### Death Rate of Nodes and Rate of Energy Utilization  
In Figure 10, AEDG shows rapid sparseness due to absence of optimized AUV trajectory. Moreover, for AEDG no restriction is implemented on the number of predecessors that can be attached to a node. Therefore, over burdened nodes discharge their batteries earlier causing predecessors to search for some other non feasible cooperating node. This causes nodes' energy depletion at an earlier stage.  
Figure 11 shows that despite cooperative routing in RE-AEDG, AEDG initially shows comparatively equivalent energy dissipation rate, however, afterwards it depicts comparatively reduced network energy consumption. This happens because of the AUV trail in RE-AEDG: it employs an extended AUV trail so that it may select the GWs from both sides of its trajectory covering more number of MNs and supporting them in data gathering and communication phase. Later on the reduced energy consumption of AEDG is because of the fact that with the passage of time its packet drop rate increases (Figure 8) causing lesser packet forwarding burden on the intermediated nodes and consequently lesser network energy consumption. AEDG also remains with lower alive number of nodes causing per instance reduced energy consumption.  
AEDG-Dopt shows the minimum energy consumption because of optimized version of sink trajectory and predecessors attached to a specific node. On the other hand, U-LVRP consumes relatively higher energy. In-spite of random motion of sink U-LVRP-Dopt still shows reduced energy consumption. Delay optimization also leads to lower energy consumption in AEDG and RE-AEDG because the number of predecessors attached to a node (Equation (19e) and Equation (19b)) that restricts the maximum number of hops a data packet travels form source to destination. Figure 10 also strengthens our claim by depicting the relative number of nodes died among different protocols per interval of time.  
Figure 9: Message latency in delay optimized and non optimized protocols.
## 5 Results and Discussion
### Ranking of BIM Implementation Barriers  
The ranking of BIM implementation barriers is categorized into four parts with the data analysis results using mean value: (a) overall ranking of BIM implementation barriers, (b) ranking according to contractors' perspectives, (c) ranking according to consultants' perspectives, and (d) ranking according to clients' perspectives. Table 4 illustrates the detailed picture of mean value analysis.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Categories (r = 200)** & \multicolumn{2}{c}{**Subcategories**} & **Definition** \\ \hline Development BIM guidelines (  
Table 4 indicates that \"unavailability of standards and guidelines\" had the highest mean value of M = 4.89, while \"competing initiatives\" had the lowest mean value of M = 1.92. Likewise, the top five barriers were \"unavailability of standards and guidelines\", with M = 4.89, \"lack of BIM training\", with M = 4.78, \"lack of expertise\" with M = 4.52, \"high cost\", with M = 4.45, and \"lack of research and BIM implementation\", with M = 4.03.
## 2 Materials and Methods
### Pre-Processing
#### 2.4.3 High-Pass Filter along Time Axis  
To remove potential drift in the signal, a sliding average was subtracted, with the window width set to 2.5 ns. Although almost no drift was noticeable in the simulated signals, the same filtering process as for the real data was applied for the reasons mentioned above.
## 2 Methods  
### Imaging Dataset and Ground Truth  
The Stargardt dataset consisted of 141 eyes from 100 patients from ProgStar study. Each eye had FAF imaging (Spectralis HRA+OCT 1.11.2.0; Heidelberg Engineering, Heidelberg, Germany) [1; 2; 3; 4] performed at baseline (zero-month) and at six-, twelve-, and eighteenth follow-up. Of these eyes, 71 were identified as additionally having SD-OCT imaging (Spectralis HRA+OCT; Heidelberg Engineering) performed at initial baseline, six-month, and twelve-month visits. The dataset consisted of 60 eyes from 60 patients obtained from Doheny Eye Institute clinics that had FAF imaging performed at initial baseline visit and six-, twelve-, and eighteen-month follow-ups.  
The ground truth masks of both GA and Stargardt atrophic regions for all FAF images were manually delineated by certified graders at Doheny Image Reading Center. We used 85% of the images along with the ground truth for the training of the deep learning algorithms and used 15% of them for testing. For the GA dataset, the field of view was 30\({}^{\circ}\) with pixel dimensions of 768 \(\times\) 868. For the Stargardt disease dataset, the field of view was 20\({}^{\circ}\) with pixel dimensions of 512 \(\times\) 512. The FAF image scan dimensions were resized to a standard size of 256 \(\times\) 256. The SD-OCT volumes had scan protocols with 496 \(\times\) 1024 \(\times\) 49 (depth \(\times\) A-scans \(\times\) B-scans) pixels or 496 \(\times\) 512 \(\times\) 49 pixels. All B-scans were resized to be 1024 \(\times\) 496 pixels. After segmentation, enface feature maps were generated from each volume with dimensions of 1024 \(\times\) 49 pixels. We resized the enface feature maps to 512 \(\times\) 512 pixels when registering them to the initial-visit FAF images. They were further resized to 256 \(\times\) 256 pixels when assembled for input to the neural network because of limited memory space. All the right (OD) eye scans and images were horizontally flipped so as to match them to left (OS) eye scans and images, providing consistency through the course of the analysis. As described in our previous work, all Stargardt dataset FAF imagesand SD-OCT-derived feature maps were registered to the initial baseline visit through feature-based image registration [50]. All GA dataset FAF images were similarly registered to their initial visit images. For Stargardt FAF images, contrast-limited adaptive histogram equalization was performed as part of pre-processing.  
The ground truths utilized in this study for both GA and Stargardt atrophy were based on FAF images, as thus far, FAF images have been utilized as standard imaging modality for macular atrophy assessment in clinical trial studies. GA and Stargardt atrophy lesions on FAF images were graded using the semiautomated software tool RegionFinder 2.6.6.0 (Heidelberg Engineering, Heidelberg, Germany). Images were initially graded by a certified reading center grader, and the grading was subsequently reviewed by a senior grader. A senior investigator (SS) resolved discrepancies between the two graders.
## 4 Numerical Modeling and Methodology
### Case-Study Frames
By maintaining identical geometrical configurations across all frames, any behavioral difference observed can be attributed solely to the provision or absence of the ACPH device, eliminating the influence of size or reinforcement variability. All frames were subjected to uniform strong ground motions, facilitating the assessment of how the incorporation of a steel-based hinge assembly alters failure modes, stiffness loss, and energy dissipation properties. Observable differences in these metrics substantiate the practical advantages of including a replaceable plastic hinge in precast frames that are otherwise prone to brittle joint failures. Thus, the analyses not only clarify how ACPHs contribute to enhanced seismic resilience but also explore potential limitations or situations where their effectiveness may be diminished. This includes an evaluation of frame performance at varying drift levels, the concentration of inelastic rotations, and the need for maintenance of connector designs to avoid issues such as bolt loosening or steel fatigue. Ultimately, the objective is to ascertain whether the ACPH consistently delivers superior performance relative to traditional wet joints or minimally detailed connections, positioning it as a feasible alternative for various precast applications in areas prone to moderate-to-high seismic activity. Figure 7: Reinforcement details of (**a**) column elements and (**b**) beam elements.
## 3 Results and Discussions
### Effect of Operating Variables
#### 3.2.6 Repeated Sorption  
The sediment DX surface usually had remaining uncontacted sorption sites after one sorption. In order to achieve full sorption of heavy metal Mn(II) by DX, the sediments that had reached the sorption equilibrium state was air-dried and then adsorbed several times (Figure 7). When the DX sorption sites were not completely occupied, the DX still had some sorption capacity for Mn(II) during multiple repetitions of sorption, with significant decreases in both sorption amount and removal rate. The initial sorption and removal rates of Mn(II) were 5.11 mg\(\cdot\)g\({}^{-1}\) and 90.59%, respectively, which decreased to 2.31 mg\(\cdot\)g\({}^{-1}\) and 43.62% at the second sorption. After five repeated sorption, the DX sediments basically reached the sorption saturation state with the removal rate finally only 2.85%. Since the already adsorbed Mn(II) is partially oxidized, there are remaining binding sites in the adsorbent, so a more appreciable removal rate can still be observed by repeating the sorption process. In addition, the generated MnO\(\kappa\) can also provide more sorption sites for the removal of Mn(II). However, the heavy metal removal efficiency decreases with a further increase of MnO\(\kappa\) content. It indicates that there is a specific threshold of sorption sites within which the mixed adsorbent can exhibit the maximum sorption efficiency. Overcrowding of MnO\(\kappa\) particles on the clay surface may also lead to overlapping sorption [45].  
Figure 6: Effect of coexisting cations on manganese sorption.  
Figure 7: Repeated sorption using DX after sorption equilibrium state several times.
# An Efficient System for Automatic Blood Type Determination Based on Image Matching Techniques
## 5 Results and Discussion
### Simulation Results for the SURF Algorithm  
The SURF algorithm was also used in our evaluation for agglutination detection in blood samples. Figure 18 shows feature points matching according to Euclidean distance between the sample image shown in Figure 15 and the reference image in Figure 13. The confusion matrix was extracted for the SURF algorithm using the 1500 patches as shown in Table 9; the evaluation measures are listed in Table 10. The results revealed significant improvement in the accuracy compared to those of the SIFT algorithm because it is less sensitive to the variation in the brightness. Although the specificity was improved, the results indicate poor performance in detecting positive samples (the existence of the agglutination), where the sensitivity is 79.5%. For the 500 samples, it was able to detect the blood type correctly for a larger number of samples than the SIFT, where the percentage error is 15.6%. An additional improvement was the shorter processing time compared to the SIFT algorithm, where the average execution time per sample is 0.775 s.