esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Result on Depth Difference Between Two Objects and Its Precision--Experiment A  
Figure 12 shows a plot of the median values and the median fitted curve of the averaged depth difference estimates for all different lighting conditions for each possible combination over different distances for all three objects.  
As a measure of consistency of the estimated and measured depths, we calculated the coefficient of determination (R-squared) of such linear regression with \(p\)-values less than 0.0005 for each method. We obtained that 87% of consistency in the depths measured by RealSense over different lighting conditions and that for MiDaS 59% of consistency. However, only 22% of the depths estimated by Alhashim has consistency over different lighting conditions, as shown in Table 2.  
To assess the practicality of MiDaS & Alhashim's algorithms in estimating the depth difference, one of the ways is to calculate the correct differentiation of the depth estimation in the method for each setups in Figure 4 within each illumination condition. As shown in Table 3, we achieved 99% success rate in discriminating depth differences for RealSense, compared to 91% for MiDaS and 48% for Alhashim. These results support the use of the MiDaS method for depth discrimination in our application, as it is able to distinguish which object is closer to the egocentric camera and which object is farthest away, like our off the shelf solution (Intel RealSense Camera).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **RealSense** & **MiDaS** & **Alhashim** \\ \hline
**R-Square** & 0.87 & 0.59 & **0.22** \\ \hline \hline \end{tabular}
\end{table}
Table 2: R-squared of median in average of difference at each distance estimated by different Methods.  
Figure 12: Fitted median curve for RealSense (**a**), MiDaS (**b**) and Alhashim (**c**) data at various distance difference.
## 2 Materials and Methods
### Calculations of Specific Exposure Assessment Indicators  
Exposure is usually calculated as a combination of food consumption data, data on the concentration of a particular chemical in a food and body weight of the population [33]. Exposure to \"j\" active substances was calculated by combining data on the consumption of \"n\" fruits and vegetables, the concentration of the active substance in the selected \"n\" fruits and vegetables, and the body weight of the respondents, as in Equation (1).  
\[\text{EDI}_{\text{j}}=\frac{\sum_{i=1}^{\text{n}}\text{FV}_{\text{i}}*\text{ C}_{\text{i}}*\text{PF}_{\text{i}}}{\text{bw}} \tag{1}\]  
EDIj is the estimated daily intake of a given \"j\" active substance [ug/kg bw/day]; FVi is the daily amount of \"i\" fruit/vegetable consumed [kg]; Ci is the active substance concentration in \"i\" fruit/vegetable [mg/kg]. If necessary, PFi as processing factor has been considered for a specific food/active substance combination (made of \"i\" fruit/vegetables).  
MOEj is the margin of exposure to \"j\" active substance calculated by dividing the toxicological reference point (in our case NOAEL) of the \"j\" active substance and calculated EDI (Equation (2)). The NOAELj values (no observed adverse effect levels expressed in mg/kg bw per day) for \"j\" active substance were taken from [16]. Final risk characterization of the cumulative exposure was performed by calculating the total margin of exposure (MOET).  
\[\text{MOEj}_{\text{j}}=\frac{\text{NOAELj}}{\text{EDIj}} \tag{2}\]  
The total margin of exposure (MOET) was calculated using the direct method as the sum of all reciprocal individual MOEs contributing to the overall cumulative exposure, as in Equation (3) [17]:  
\[\frac{1}{\text{MOET}}=\frac{1}{\text{MOE}_{\text{1}}}+\frac{1}{\text{MOE}_{ \text{2}}}+\dots+\frac{1}{\text{MOE}_{\text{n}}} \tag{3}\]  
Comparison with acceptable daily intakes (ADIs) was performed for obtained EDIs for each individual active substance as additional risk characterization.  
In exposure assessments, uncertainties that could affect the results are as follows: (i) quality of water used for processing fruits/vegetables was not included; (ii) some of the respondents could have reported decreased or increased consumption amounts and frequencies due to different psychological reasons; (iii) contribution of metabolites and degradation product effect were not considered. Therefore, these constraints could result in overestimation/underestimation of some values [17].
# Ultrasonic Power and Data Transfer through Multiple Curved Layers Applied to Pipe Instrumentation
## 5 Conclusions  
Ultrasonic power and data were successfully transmitted through a multi-layered curved structure that was designed for wellbore monitoring. The pipe's geometry was based on the actual dimensions used in the field, as well as the direction of power and data. Power was sent from the inner pipe to the outer pipe, whereas data went on the reverse path, which is needed for wellbore monitoring systems. The experimental results showed the capability of the system to transfer the necessary energy to supply the electronics on the inside block, which, at full operating capability, has a power consumption of approximately 100 mW, and also the capability to communicate with a digital sensor at a rate of 1200 bps using Manchester coding. The proposed solution also suggested that any digital sensor can be wirelessly interrogated through the acoustic channel. The implementation of Manchester coding and the MCU also allowed the system to operate in a noisy condition compared with the detection method of the analog voltage system. The system was able to demodulate the sensor information with extremely low values of the enveloped signal (approximately 150 mV) when using a digital low pass filter implemented on the MCU. The proposed method is, therefore, promising for field applications with wellbore monitoring systems.  
In order to increase the comprehension of the acoustic channel behavior, future studies on how the misalignment between transducers and temperature affect the power and data transmission of the system need to be conducted. Furthermore, the use of techniques to increase the efficiency, such as electrical impedance matching and other types of modulation to increase the channel capability, need to be investigated.  
Conceptualization, V.L.T. and A.M.B.B.; Formal analysis, V.L.T. and A.M.B.B.; Funding acquisition, A.B.D.; Investigation, A.C.K., S.M.M.Q. and S.W.O.F.; Methodology, A.C.K.; Project administration, A.B.D.; Software, V.L.T.; Supervision, A.M.B.B.; Validation, S.M.M.Q. and S.W.O.F.; Writing--original draft, V.L.T., A.C.K., A.M.B.B., S.M.M.Q., S.W.O.F. and A.B.D.; Writing--review & editing, V.L.T., A.C.K., A.M.B.B., S.M.M.Q., S.W.O.F. and A.B.D.  
This research was funded by Shell Brazil through the project BG-40.  
This research was carried out in association with the ongoing R&D project registered as ANP 19062-9, \"MEMS-Wellbore Monitoring Systems\" (PUC-Rio/Shell Brasil/ANP)-Sistema de monitoramento para pocos baseado em dispositivos microelectromecanicos (MEMS), sponsored by Shell Brasil under the ANP R&D levy as \"Compromisso de Investimentos com Pesquisa e Desenvolvimento\".  
The authors declare no conflict of interest.  
Figure 11: Received signal for BASK 5% and BASK 18%.
## 3 Methodology
### Pupil Localization  
The pupil localization process is used to locate the pupil, which is the dark region of the eye controlling the light entrance. The flowchart of this stage is shown in Figure 5. (1) First, the eye image is converted into grayscale. (2) The Haar-like rectangle feature, same as that in Section 3.2, is applied to narrow the eye region. (3) Then another Haar-like feature, the center-surround feature with the variable inner radius of r and outer radius of 3r, is applied to the detected exact eye region of step 2. This feature makes use of the pupil being darker than the surrounding area. Therefore, the region corresponding to the maximum response of the Haar feature is a superior estimate of the iris region. The center coordinates and radius of the Haar feature is obtained and a region can be segmented with a dimension of 1.2\(\times\) radius, horizontally and vertically, on both sides from the center of the detected region, to make sure the whole pupil is in the segment. Then we perform the following techniques. (4) Gaussian filtering is used to reduce noise and smooth the image while preserving the edges. (5) The morphology opening operation is applied to eliminate small objects, separate small objects at slender locations, and restore others. (6) The edges are detected through the Canny filter, and the contour point is obtained.  
Given a set of candidate contour points of the pupil, the next step of the algorithm is to find the best fitting ellipse. (7) We applied the Random Sample Consensus (RANSAC) paradigm for ellipse fitting [33]. RANSAC is an effective technique for model fitting in the presence of a large but unknown percentage of outliers, in a measurement sample. In our application, inliers are all of those contour points that correspond to the pupil contour and outliers are contour points that correspond to other contours, like the upper and the lower eyelid. After the necessary number of iterations, an ellipse is fit to the largest consensus set, and its center is considered to be the center of the pupil. The frame number and pupil center coordinates are stored in memory.
## 3 Testbed Implementation,Prototype, and Software Development
### UDP Server Socket-Based GUI  
The UDP can fire-off a message and immediately free the server-side network resources as it is a connectionless protocol. This also makes the UDP one of the easiest protocols to write a client/server application for. Also, the UDP preserves message boundaries, transmitting entire messages at once.  
In our C#-based UDP server socket GUI program, we define and create a new battery object. We set the inbound client port number. By using the UdpClient, our job of implementing the UDP was greatly simplified. The client is created using the set local port. The battery client object is declared with events because it implements two events: the 'before receiveiving' event and the 'after receiveiving' event. The event 'before receiving' is fired immediately before receiving the data. The 'after receiving' event is fired immediately after the battery client finishes receiving the data. Figure 14 shows the UDP server socket GUI for our proposed system with two battery clients connected, receiving, and displaying the data in real-time. The data frame of the battery client is divided and displayed in the data-grid-view, as explained in the previous subsection. The start method in our program fires-off a worker thread that in turn waits for the data. We have set it on a separate worker thread because the receive method blocks the current thread until it is completed and we do not want it to lock-up our user interface. The thread exits immediately after a message is received by the worker thread. Hence, we start a new worker thread to continue waiting for the next message as these events are fired from the worker thread. This is necessary because of the nature of multi-threaded applications; the GUI from a worker thread should never be updated, the invoking required should always be checked and invoking used if it is true. Finally, when completed, the stop method should be fired to stop the worker thread and cleanly dispose the UDP battery client. The received messages in the server from three clients as shown in Figure 14 are divided and displayed in the data-grid-view in the same way as explained in the previous sub-section.  
Figure 13: Transmission control protocol (TCP) server socket GUI for our proposed system.
# A Novel Satellite PRN Code Assignment Method Based on Improved RLF Algorithm
## 3 Algorithm
### Algorithm Comparison
The height of the column in the figure is the approximate minimum number of PRN codes for the 6 constellations calculated by the 6 algorithms. DFS and GA are shown to be less effective when the number of satellites is large. The RLF algorithm and SA have better results on all 6 constellations, and the RLF algorithm is slightly more suitable for calculating the approximate minimum number of PRN codes. We chose the RLF algorithm for further analysis, and this is introduced and improved in the next section.
# Optimization of Inventory Management to Prevent Drug Shortages in the Hospital Supply Chain
## 5 Experiment and Numerical Results
### Results
#### 5.2.2 Evaluation of the Refilling Cost  
We consider the refilling cost in a time horizon with 30 timeslots (we refer to one week for each timeslot) as shown in Figure 5. We first make a comparison with a simple, commonly-used method, over-provisioning. Since the remaining volumes and the upper bound volumes of drugs are often used as the replenishment policy for each refilling inventory, they lead to a higher cost in most timeslots. Figure 5a illustrates the trace of the refilling cost during the considered horizon. The over-provisioning method has a higher cost than that of the DRLD in most of the timeslots. As shown in Algorithm 1, the system always explores the action space and exploits the knowledge at any given time step that is able to prevent the volumes of drugs from reaching bounded levels. On average, the DRLD can reduce the refilling cost by 12.31% compared to the over-provisioning method.  
Figure 5b illustrates the refilling cost of the DRLD and ski-rental methods. Ski-rental is an online algorithm that makes decisions based on the current situation. However, the ski-rental method does not involve the learning phase to deal with changes in the system. Furthermore, the performance of the ski-rental method is sensitive to a competitive setting. In a complicated system with a large number of drugs and uncertain demands, the ski-rental algorithm does not present a suitable approach to solve DR2O. On average, the ski-rental method can obtain a better refilling cost than the use of over-provisioning. However, it is more unstable and results in costs that are on average 10.4% higher than those of the DRLD.  
Finally, we evaluate the refilling cost by applying the max-min approach. The max-min inventory model seeks to reduce the gap between the max and min values, where the min value represents the reorder point and the max value represents the targeted quantity. Although this method is simple and non-optimized, it is able to provide an automation model for inventory management by using some triggers. Max-min is similar to over-provisioning, but more flexible if we set the lower and upper bounds to a high volume. The reorder quantity is calculated by the surplus between max and min. As shown in Figure 5c, this method flattens the filling cost during the time horizon. Its results indicate that it is a promising approach to control the shortage problem, but its cost is higher than that of the DRLD by 11.8%.
## 2 Materials and Methods
### Classification by a Sample-Encoding Generalization of the Kohonen Neural Network  
The Kohonen neural network is organized into an array of nodes, generally two-dimensional as illustrated in Figure 3. The purpose of the original network conception [21] was to materialize the development and function of an associative memory which runs an unsupervised algorithm to encode its input _sequentially_ in the form of weight vectors, of the same data type as the input, which it stores in the nodes.  
The network is said to be _topologically ordered_ because the encoding is realized in such a way that neighboring nodes have neighboring values. The network is also called a _self organizing map_, abbreviated SOM, because of this topologically-ordered encoding capacity. In the practice of pattern classification, mapping labeled data into a Kohonen neural network, once it is topologically ordered and its weights settle, affords a class label to each node and, therefore, provides the network with a classification function as an associative memory: to an input pattern, it associates the class label of the node with the closest weight. The Kohonen neural network can be looked at as a vector quantizer [22; 23] for its ability to reduce a data set to a group of representation prototypes. Several variants of a standard Kohonen network training algorithm have been investigated, such as the the Gibbs density modelling network [24], the probabilistic self-organizing map (PRSOM) [25], and the soft topographic mapping with kernels (STMK) [26].  
In this study, we investigate a generalization of the standard Kohonen neural network algorithm that encodes input in the form of a sample of pattern characteristic vectors rather than a single such vector as with the standard algorithm. Pattern similarity, which underlies both network training and the network subsequent classification function, is defined in this generalization by the two-sample Hotelling \(T^{2}\) statistic as presented next.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Characteristics** & **C1**:**FR** & **C2**:**FT** & **C3**:**FR-FT** \\ \hline Age (years) & 46.1 * \(\pm\) 11.7 & 59.5 * \(\pm\) 10.1 & 59.6 \(\pm\) 11.4 \\ Height (m) & 1.71 \(\pm\) 0.07 & 1.66 \(\pm\) 0.09 & 1.66 \(\pm\) 0.11 \\ Weight (kg) & 82.9 \(\pm\) 20.7 & 76.2 \(\pm\) 11.2 & 84.3 \(\pm\) 15.9 \\ BMI (kg/m\({}^{2}\)) & 28.3 \(\pm\) 7.1 & 27.4 \(\pm\) 3.9 & 30.3 \(\pm\) 5.5 \\ Men\% & 45 & 38 & 33.3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Demographic characteristics of the data in the three classes (columns FR, FT, and FR-FT)  
Figure 3: A 5 \(\times\) 5 Kohonen neural network.
## 2 Principle and Structural Design of the Active Support System for a 4m SiC Primary Mirror
### Finite Element Model of the Active Support System for the 4m SiC Primary Mirror  
A finite element model of the primary mirror support system was established, as shown in Figure 3. To improve the efficiency of the analysis, the hardpoints were simplified as rod structures in the model, the rod element was used, and the axial stiffness was set to 33 N/\(\upmu\)m. Gravity counter forces and correction forces were applied at the 54 support points to simulate the force actuator. The primary mirror was composed of SiC, and the mirror cell was composed of carbon structural steel Q235. The model contained 309,433 units and 893,116 nodes.
## 4 Data Transmission
### Tested Modulations
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Increasing the** & **Pros** & **Cons** \\ \hline \# of subcarriers & Increase throughput & Decreases the SNR per subcarrier \\ \hline \multirow{2}{*}{Symbol time} & Closer subcarriers & \\  & (more subcarriers and simpler equalization) & Approaching the coherence time of the channel \\ \hline Back Off & Better linearity of the amplifier & Decreases the mean power value \\ \hline Guard interval & Stronger inter symbol interference (ISI) rejection & Reduces spectral efficiency \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Pros and cons of Orthogonal Frequency Division Multiplexing (OFDM) parameters.
# the hexadecimal number specifies the device
### Symbol Diversification on Raspbian  
The symbol diversifier tool renames the symbols in shared libraries. In addition, the changed names are propagated to all ELF files that depend on the entities whose symbolic names were diversified. To experiment with symbol diversification on Raspbian, we employed our old tool that was previously used to diversify x86_64 Linux [18]. To be run on Raspbian, the tool needed to be modified to support 32-bit ELF files, which was relatively straightforward. The necessary source code for the symbol diversification tools in this experiment consists of approximately 2500 lines of C++ code and 300 lines of Python and Bash scripts. We also needed to modify the _glibc_ dynamic linker source code with 225 inserted and seven deleted lines in six files, plus an existing third-party SHA-2 implementation that consisted of two files and 1262 lines of code.  
The purpose of our experiments with the symbol diversifier was to find our how well it works in a new operating system in the IoT environment and how much manual work is involved in adapting the diversifier into a new operating system. In our experiments, diversified Raspbian was able to successfully start _systemd_ and initialize the system. Most of the services in the system started normally, although initially some did not. For example, because of the problems caused by run-time dynamic loading, the login service did not start at first.  
To overcome these challenges, we implemented a source code patch to _glibc_ that makes it diversify symbols passed to _dlsym_ on the fly. The patched _glibc_ contains a static data object that contains the diversification secret and the identification number of the algorithm to use. This data can then be rewritten to include the correct diversification secret in the library after compilation and linking, so including the secret is a very inexpensive operation.  
With the patched glibc, the symbol-diversified Raspbian can boot, using simple prefixation as the diversification transform. The _login_ command works, as does the shell's basic functionality. Programs _less_, _sudo_, _ssh_ and _readelf_ also worked normally in our experiments. The _ping_ command operated normally after we set the _set-user-id_ bit to its meta-data. In the case of the _man_ program, we had to manually specify the location of the library it needs with the _LD_LIBRARY_PATH_ environment variable.  
In conclusion, despite some challenges, interface diversification was successful in our experiments. Our memory layout shuffling implementation successfully prevented an exploit from working, and the symbol diversification tool managed to diversify the whole Raspbian operating system. Even though some manual fixes were needed with symbol diversification, IoT operating systems usually have only a limited set of applications, and many nodes in sensory networks are identical, which means these fixes can also made automatically for a number of nodes.
## 6 Simulation Setup  
_Simulation Network and Traffic Calibration_  
The downtown area in the city of Los Angeles (LA), shown in Figure 3, is used for the simulation and evaluation. The red points and surrounding circles are the RSU locations and their communication ranges, which are used in communication modeling. This road network is about 133 km\({}^{2}\). It has 1625 road network nodes, 3561 road links, and 459 traffic signals. The road network's vehicle traffic demand was calibrated based on the vehicle count data from loop detectors in the same area. These data are collected from multiple sources, as described in detail in [22]. This traffic demand represents the morning peak hour in the downtown area of the city of LA, which continues for 1 h from 7:00 a.m. to 8:00 a.m. The demand runs for one hour. However, we run the simulation for two hours to give the vehicles enough time to finish their trips and leave the road network. The total number of vehicles that are simulated is approximately 144,000 vehicles.  
Figure 3: Los Angeles Downtown Road Network.  
After applying the greedy algorithm on the set of traffic signals, 126 traffic signals were chosen by the algorithm to cover all the 459 traffic signals. We assume RSUs will be installed at the traffic signals controlling these intersections. This algorithm does not guarantee coverage of the whole road network. However, it covers the maximum number of signalized intersections with a minimum number of RSUs.