esa-sceva/satcom-chunk-collection huggingface dataset:
# All You Need Is a Few Dots to Label CT Images for Organ Segmentation
## 4 Performance Tests
### Performance Evaluation with Varying the Number of Anchor Pixels  
The proposed model requires a few anchor pixels on the organ to be segmented. The performance of the model can be affected by the number of anchor pixels picked by the user. The anchors are sampled randomly in the ground truth for convenience in the experiments. We tried to find out the best number of anchor pixels for our method.  
As shown in Table 1, we measured the dice score and Hausdorff distance of our model according to the number of anchor pixels for the proposed method. The dice score is the lowest when using only one anchor pixel for each organ, and the dice score when using two or more anchor pixels is almost similar. However, the Hausdorff distance instead increases because other organs with similar textures to the target organ are often segmented.  
Figure 4: Segmentation Examples by Threshold. Each row represents each organ. First column shows original CT images, and from the second to sixth columns, the segmentation generated from 5 anchor pixels is shown. We visualize segmentation by different thresholds.  
Figure 5: Dice score and Hausdorff distance by varying thresholds. Left graph demonstrates change in dice score by changing thresholds. Right graph shows Hausdorff distance by varying thresholds.  
Figure 6 shows how the number of anchor pixels affects the segmentation reconstructed from anchor pixels. As those figures illustrate, our model can find a segmentation region with a missing area when we pick only one anchor pixel. However, as the number of anchor pixels increases, the proposed method captured a more accurate region.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & \multicolumn{4}{c}{**Number of Anchor Pixels per Organs**} \\ \hline Model & 1 & 2 & 3 & 4 & 5 \\ DICE SCORE & 0.717 & 0.752 & 0.761 & 0.762 & 0.759 \\ Hausdorff Distance & 29.155 & 14.956 & 18.748 & 20.433 & 17.951 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dice score and Hausdorff distance by changing the number of anchors.  
Figure 6: Case study to see how the generated segmentation is changed by a changing number of anchor pixels. The first and third columns are original CT images with some anchor pixels (red points). The second and fourth columns are generated segmentation using given anchor pixels.  
Generally, more anchor pixels give better segmentation than fewer anchor pixels. However, we could find some exceptional cases that fewer anchor pixels show better performance. We analyzed that the position of anchor pixels also affects the performance. For instance, even if we have only one anchor pixel, the segmentation prediction is more accurate when the anchor pixel exists around the center of the organ. If we have more than one anchor pixel, but they are in the boundary, our model tends to capture only a part of the region. Since we automated the process of picking anchor pixels by randomly sampling them from pixels of the organ, the case that some anchor pixels cover the same region or exist in the boundary region frequently occurred.
## 4 Experiment Setup
### Experimental Setup and Procedure  
Figure 5a,b presents the experiment setup for validating numerical simulations. Figure 5a depicts the experiment setup for the PTFE with LED. Figure 5b depicts the PTFEHS with LED and housing in a clear acrylic cylinder test box with a 38 mm diameter inlet and a full opening outlet with a diameter of 140 mm. The clear acrylic cylinder test box was used for jet impingement cooling. Air was blown at a constant temperature (\(T_{\text{jet}}\) = 25 \({}^{\circ}\)C) and velocity into the test box from inlet hole by using a cooling fan. The cooling fan, heat sink, and LED module were assembled and mounted on the top wall of the cylinder test box using screws under inlet hole for evaluating heat sink performance. The experimental setup was the same as that of the computational domain that was used for numerical analysis. Prior to experimental testing, two cooling fans were used for generating higher and lower Reynolds number flow and tested using an anemometer. The test results indicated a flow velocity of 6.13 and 2.71 m s\({}^{-1}\), respectively.  
Each experiment conducted in Table 1 was under the same ambient temperature 25 \({}^{\circ}\)C for constant input current driver of the 10 W LED. The velocity of the fan is measured by an anemometer. The calibrated J-type thermocouples were attached to some specified temperature points that are connecting to a data acquisition device. The first step for case 1 is to turn on the data acquisition system, PC based computer, and the cooling fan. To power on the LED, temperatures of specified points such as \(T_{\text{sp}}\), \(T_{\text{b}}\), \(T_{\text{hs}}\), \(T_{\text{jet}}\), and \(T_{\text{trim}}\) will rise up naturally. Every thermocouple is recorded using the 34980A Data Acquisition Unit (Agilent Technology). Experiment was conducting until the stable temperatures were reached. According to standard UL 8750 for LED, a temperature is considered stable if three successive readings, taken at 15 min intervals, are within 1 \({}^{\circ}\)C of one another and are still not rising. When case 1 is completed, similar cycles are completed for the rest of cases 2-9. The calculated steady state of heat transfer coefficients (\(h\)), Nusselt number (\(Nu\)), and heat sink thermal resistance (\(R\)) for cases 1-6 are achieved and the thermal resistance (\(R\)) for cases 7-9 with housing in practice were obtained thereafter.  
The experimental setups under testing are illustrated in Figure 6. The figure illustrates the heat sink with LED under 6.13 and 2.71 m s\({}^{-1}\) fan velocity when the fan height is 20 and 10 mm, respectively.  
Figure 4: Heat sinks (**a**) PTFEHS, (**b**) MFLHS, and (**c**) TTFHS.  
Figure 5: Schematic diagram of experimental setup for PTFHS with LED and housing. (**a**) Heat sinks with LED, (**b**) heat sinks with LED and housing.  
Figure 6: Illustrations of experimental setup under testing.
## 2 Materials and Methods  
### Data  
In order to accurately verify the performance of the algorithm, three hyperspectral image datasets are used for simulation experiments: Beach_Plane, Saliens_Synthetic, and Road_Vehicle. The Beach_Plane dataset is collected by an airborne visible/infrared spectral imager (AVIRIS), as shown in Figure 1. They are taken from the Cat Island beach, Australia, with a spatial resolution of 17.2 m/pixel. The 188 spectral bands are reserved, and the size of the spatial part is 140 \(\times\) 140 after interception. The image mainly includes surface objects such as the sea surface, beach, and island, among which the abnormal objects are aircraft on the sea surface. The Saliens_Synthetic dataset is the synthetic experimental data shown in Figure 2. The real background image was taken by AVIRIS in the Salinas region, CA, USA, with a spatial resolution of 3.7 m/pixel. Its original hyperspectral image data size is 512 \(\times\) 217, including 224 bands. The backgrounds are vegetable garden, land, vineyard, and other different types. In the experiment, a total of 204 bands, including 1-107, 113-153, and 168-223, were reserved. The 120 \(\times\) 140 sized area was selected and embedded with 12 abnormal objects. The Road_Vehicle experimental dataset is taken by AVIRIS, and the suburban roads imaging scene is shown in Figure 3. The space size of this data after being intercepted from the original hyperspectral data is 100 \(\times\) 100. The number of spectral bands is 186. The Road_Vehicle experimental dataset mainly includes road, grass, trees, and other ground objects. The abnormal objects are three vehicles on the road.
## 3 Results and Discussion
### Adsorption Properties  
#### 3.2.1 Effect of pH  
We used APTES solutions of different concentrations (volumes: 30%, 20%, and 10% in toluene) for this modification. The adsorption efficiency of the zeolite modified by the APTES solution with a concentration of 30% was the greatest. Therefore, we used the zeolite modified by the APTES solution with a concentration of 30% as the adsorbent for all the experiments. We present the results of the effects of the solution pH on the adsorption of Cu(II) onto the natural and modified zeolite in Figure 5.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sample** & **BET Surface Area (m\({}^{2}\)/g)** & **Pore Volume (cm\({}^{3}\)/g)** & **Average Pore Size (nm)** \\ \hline Natural zeolite & 22.79 & 0.058 & 10.188 \\ Modified zeolite & 9.06 & 0.035 & 15.243 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The surface properties of natural and modified zeolite.  
The maximum value of the adsorption capacity of the modified zeolite was 6.06 mg/g at a pH of 5, which was higher than the adsorption capacity of the natural zeolite (4.05 mg/g), which confirms that we successfully performed the modification. When we increased the pH from 5, the adsorption capacity decreased the presence of the Cu(II) ion species in the aqueous solution. Therefore, we chose a pH of 5 as the optimal pH for the adsorption studies of the modified zeolite.
# Broadcasting in Stars of Cliques and Path-Connected Cliques +
## 3 Bounds on Broadcast Time of Star of Cliques  
We define star of cliques \(G_{k}\) as a connected graph comprising \(n\) vertices distributed across \(k\) cliques \(C_{1},C_{2},\ldots,C_{k}\), where each clique \(C_{i}\) contains \(l_{i}\) vertices for all \(1\leq i\leq k\). The sizes of the cliques are ordered such that \(l_{1}\geq l_{2}\geq\cdots\geq l_{k}\geq 1\). Furthermore, the graph \(G_{k}\) includes a universal vertex \(u\), which is directly connected to all other vertices in the graph. Figure 1 illustrates an example of a star of cliques consisting of three cliques with sizes 10, 9, and 5.  
Let \(G_{k}\) be a star of cliques and the universal vertex \(u\) be the originator. Then, \(b(u,G_{k})\geq\max\limits_{1\leq i\leq k}\{i-1+\lceil\log\left(l_{i}+1\right) \rceil\}\).  
Let \(G_{k}\) be a star of cliques and the universal vertex \(u\) be the originator. Then, \(b(u,G_{k})\leq\max\limits_{1\leq i\leq k}\{i+\lceil\log l_{i}\rceil\}\).  
Let \(G_{k}\) be a star of cliques where the originator is the universal vertex \(u\). Then, the difference between the upper and lower bounds of \(b(u,G_{k})\) is at most 1.  
From Lemma 4, we know \(b(u,G_{k})\) has two possible values. Thus, Algorithm 2 is a 1-additive approximation algorithm.  
```
0: Star of cliques \(G_{k}\) (\(l_{1}\geq l_{2}\geq\cdots\geq l_{k}\geq 1\)).
0: A broadcast scheme for \(G_{k}\) with time \(\max\limits_{1\leq i\leq k}\{i+\lceil\log l_{i}\rceil\}\).
1:for\(i=1\to k\)do
2:\(u\) informs a vertex \(v_{i}\) in clique \(C_{i}\)
3:\(v_{i}\) broadcasts in \(C_{i}\)
4:endfor
```  
**Algorithm 2** Broadcast algorithm for star of cliques from the universal vertex \(u\)
## 2 Study Area and Data
### Data  
This study uses FY-3 data to test these 7 methods. The FY-3 satellite has an orbital altitude of 836 kilometers, an inclination of 98.75 degrees, and an orbit time of 102 minutes around the earth. FY-3 is loaded with up to 11 sensor types, including a visible infrared scanning radiometer (VIRR) and a medium resolution spectral imager (MERSI). MERSI on FY-3 can detect cloud and surface features with a width of 2000 km and a resolution of 250 m. The spectrum ranges from 0.41 to 12.5 \(\upmu\)m, and the ground resolution is 250 to 1000 m. In this paper, images of the first four bands in the FY-3/MERSI daily images were selected for surface water extraction. The spatial resolution is 250 m, including the blue, green, red, and near-infrared bands.
# A Novel Strategy for VNF Placement in Edge Computing Environments
## 3 Problem Statement and Modeling
### Edge Environment Model
#### 3.1.1 Delay Model  
Our model considers the following delays: (i) propagation delay \(d_{p}(l_{ij})\) between two edge nodes \(h_{i},h_{j}\in H\); (ii) transmission delay \(d_{tm}(h_{i},h_{j})\), which represents the time needed to transfer packets from edge node \(h_{i}\) to \(h_{j}\); (iii) and processing delay \(d_{prc}(v_{inst},h)\), which refers to the time needed to process the arriving packets in the VNF instance \(v_{inst}\); and (iv) queuing delay \(d_{q}(v_{inst},h)\) of the VNF instance \(v_{inst}\), which represents the time spent by packets at an edge node waiting for being sent to the next edge node after being processed by \(v_{inst}\). Table 3 summarizes those different delays and their notations.
# Susceptibility and Remanent Magnetization Estimates from Orientation Tools in Borehole Imaging Logs
## 3 Results
### Magnetic Corrections
#### 3.3.2 Magnetic Diurnal Variation Correction  
It is well known that the Earth's magnetic field suffers temporal variations during the day caused by interactions between the Sun and electrical charged particles in the ionosphere. Such diurnal variation is characterized by a slow but progressive increase in the field intensity as the solar incidence increases, decreasing towards the background value as the solar incidence reduces. As the logging operations can last for hours, Earth's magnetic field diurnal variations may occur during such periods of magnetic data acquisition. Once estimates are based mainly on field amplitudes, any temporal variation in the field introduces localized errors in the estimates. If the logging time interval is known, it is possible to remove such variations using for that the magnetic field behavior registered by the closest magnetic observatory. In the case of well 1-RJS-755-RJ, the magnetic field variations during logging operation (from 02:26 to 07:58 AM) was corrected using records obtained by Vassouras Magnetic Observatory (VSS) in Rio de Janeiro, Brazil, the closest to the well location. Figure 10 shows the variations of the Earth's magnetic field and its components along the day of logging (10 February 2021). Because the amplitude of field variations were not large during operation period, corrections were small and visually imperceptible in the scale used to represent the field and its components together and will not be shown here.  
After all corrections the measured magnetic field and its components can be used in the magnetic susceptibility and remanence estimates. Due to the visualization difficulties caused by the large scale necessary to show all components together, we exhibit in Figure 11 only the \(F\) field, which allows better evaluation of rock's magnetic response in depth. Notice that the field response to the igneous rocks becomes more evident only below the base of the first interval (around 6300 m). It is important to notice that there is no significant change in the transition between the carbonates and the first igneous interval, who shows only a slight increase in field amplitude with depth. Since the first interval represents the top of the igneous sequence, it seems possible that it has been exposed to a higher alteration degree, which may have reduced its contribution to the measured magnetic field.
## 2 Materials and Methods
### Mobility Model
#### 2.1.3 Reference Point Group Mobility Model  
The reference point group mobility model [22] is one of the group models. In this model, each group has a reference point (RP), and the path of group members' movement is determined by the RP. Each movement of group members is a random offset from the RP. The RPGM model is the most commonly used group mobility model, composed of one or more groups of nodes with a logical center. In the model, each group's logical center's motion mode determines the entire group's motion mode, including position, velocity, direction, and acceleration. The positions of other nodes in the group will change with the position of the logical center. The RPGM model can fully describe this movement trend of nodes in a network. However, it still has the following drawbacks: When using a reference point group mobility model, we need to master the information of all nodes. The drone swarm network is distributed, and the network topology undergoes high-speed dynamic changes. It is basically impossible to obtain all the information of nodes in the network [26].  
The RPGM model is shown in Figure 3. A random value of velocity and direction are generated for each node member of the group evenly at each time interval \(\Delta\)t. The movement of nodes in this model includes group movement and individual movement. The movement of RP and the random movement of members constitute two parts of individual movement. Generally, members are evenly distributed within a group, and each member is assigned an RP to form a group for movement. Specifically, each node member allows independent movement around the RP for each group. If the speed of RP is \(\overset{\rightarrow}{V}_{RP}(n)\) at time n, the speed of any of its member nodes is  
\[\overset{\rightarrow}{V}_{member}(n)=\overset{\rightarrow}{V}_{RP}(n)+ \overset{\rightarrow}{R}_{n} \tag{5}\]  
where \(\overset{\rightarrow}{R}_{n}\) is a random vector offset of the member node relative to its RP, with a value between [0, \(r_{max}\)]; \(r_{max}\) is the maximum offset distance defined; and the direction of \(\overset{\rightarrow}{R}_{n}\) is a random value between [0, 2\(\pi\)]. The main application case of RPGM is the logical relationship between officers and soldiers in military communications. This article selects the RPGM model as a typical representative of the spatial dependency model.  
Figure 2: A node movement trajectory in GM mobility model.
## 5 Results  
### Estimation of \(A_{sc}\)  
The equivalent scattering absorption area \(A_{sc}\) for the furniture was measured for configurations 1, 2, 4, and 5 given in Table 2. The \(A_{sc}\) is estimated according to Equation (39). In Figure 9, the results are presented together with the averaged values.  
Figure 9: \(A_{sc}\) for the furniture estimated from cases 1, 2, 4 and 5 in Table 2, (black) average, (blue) case 4, (red) case 5, (purple) case 1 and (green) case 2.  
\(A_{sc}\) depends on the absorption of furniture as well as the scattered sound energy transmitted to the non-grazing sound field and mainly absorbed by the ceiling absorber. As can be seen in Figure 9, the frequency behavior is quite similar for the different cases despite the fact that there is a variation of the ceiling absorber concerning airflow resistivity, thickness, and mounting height. This supports the idea that, for ceiling absorbers with a reasonably high absorption, see comment in Section 3.2.5, the correction for furniture absorption and scattering by \(A_{sc}\) is justified. For furnishing with tables, chairs, and shelves, the highest values of \(A_{sc}\) appears for the mid frequencies, as apparent from Figure 9. In practice, it is also possible to define \(A_{sc}\) per m\({}^{2}\) floor area to obtain a value that can be used for different sizes of rooms. In [29], values of \(A_{sc}\) per m2 floor area are suggested for what can be considered as sparse, normal, and dense furnishing. It is noteworthy that, in EN 12354-6, the correction for furniture and other objects in the room is independent of frequency.
# A Systematic Deep Learning Based Overhead Tracking and Counting System Using RGB-D Remote Cameras
## 3 The Proposed System Methodology  
This section discusses the overall proposed methodology that is used in our HASTAC system. The system framework is divided into 6 major steps. First, video preprocessing is done in which the video is converted into frames and complex backgrounds are removed from the image frames using the Kernel Density Estimation (KDE) technique. Second, heads are detected in the frames using Hough circular gradient transform and shoulders are detected by joint HOG based symmetry methods. Third, Robust features such as Fused joint HOG-LBP, Energy based point clouds and Fused intra-inter trajectories are extracted. Fourth, Apriori-Association is implemented to select the best features. Fifth, Convolution Neural Network (CNN) is used for accurate people tracking. Finally, heads are counted using the Cross-line judgment technique. The overall architecture of our HASTAC system is shown in Figure 1.
# Real-Time Optimization of RISC-V Processors Based on Branch Prediction and Division Data Dependency
## 4 Experiment and Result Analysis
### Stage Experiments and Result Analysis  
We conducted multiple comparative experiments on the processor before and after optimization. We used the CoreMark benchmark program to test the prediction accuracy of branch prediction instructions and the division calculation speed when executing division operation instructions.
# Optical and Electronic Expert System for Diagnosing Eye Pathology in Glaucoma
## 2 Methods
### Related Work  
Many authors have created models for the diagnosis and classification of glaucoma using a variety of research approaches and computer programs. These methods have led to the development of several research models. Most of these models include several layers of performance analysis using deep learning. Many similarities between retinal disease and glaucoma make it difficult to distinguish between them. The authors used such parameters as intraocular pressure, field of vision, excavation diameter to OD diameter ratio, excavation area to OD area ratio, excavation volume to OD volume ratio, excavation area, excavation volume, area neuroretinal belt, volume of the neuroretinal belt, average thickness of the nerve fiber layer along the edge of the disc, cross-sectional area of the nerve fiber layer along the edge of the disc.  
In the past, glaucoma could be identified from these digital recordings of retinal images. This strategy has been used in the vast majority of clinical trials, making it the most commonly used technique.  
The image classification method was used to analyze the data and organize things into categories. This project required numerical analysis of the image filled correctly. In order to finish the study on time, the information needs to be entered in different groups, such as \"normal\" and \"glaucoma\". They used the U-Net segmentation technique to accurately identify and segment the optic cup in the retinal fundus photographs they examined.  
A detailed analysis of the methods used today to diagnose glaucoma is presented in the work of the authors: [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; and [PERSON] [28].  
It should be noted that the CNN technique for the diagnosis of glaucoma involves combining the results of two different types of measurements, namely temporal and geographical data. Because they did it this way, they could use both time and place data. Thanks to these characteristics, it was possible to make a reliable diagnosis of the condition of the eye (on the basis of a static structural system).  
Recommendations for the Implementation of the Optical-Electronic Expert System for the Proceeding of Images of the Retina of the Eye  
To implement the tasks, the architecture of the optical-electronic expert system shown in Figure 2 was implemented.  
The user receives the data to be processed as a result of diagnostics based on a modern fundus camera. An interface for entering information and its pre-processing has been developed, which allows you to process images and accurately distinguish pathological zones.  
In the future, the received processed image must be recorded in the knowledge base replenishment block, for the sake of correct interpretation when the dynamics of progress or regression of this or that pathology is carried out.  
The block of formation of knowledge bases is responsible for the preservation of all previously obtained data and existing standards regarding the norms and pathologies of the fundus of the eye.