esa-sceva/satcom-chunk-collection huggingface dataset:
# High Throughput and Acceptance Ratio Multipath Routing Algorithm in Cognitive Wireless Mesh Network
## 6 Simulation and Results
### Comparison Wireless Acceptance Ratio  
The wireless acceptance ratios with a different number of requests are shown in Figure 4. From Figure 4, we can see that when the number of wireless request was \(|\Delta|\) = 5, the wireless acceptance ratio of the three algorithms was one. This meant that the five wireless requests could be accepted. When the number of wireless requests was larger than five, then the wireless acceptance ration was higher than the other two algorithms because MRIR uses the strategy of interference link reuse, which can effectively save the network resources. With the increase of wireless requests, MRIR's advantage was more obvious. When the wireless request was \(|\Delta|\) = 40, the wireless acceptance ratios of MRIR were 32% and 37% higher than that of the Route-Stability and Route-Closeness.  
From Figure 5, we can see that the wireless acceptance ratio increased with the increment of the number of available channels for the three algorithms. This was because more wireless requests could be accepted when the number of available channels increased. When the number of available channels was \(|K|\) = 6, the wireless acceptance ratios of MRIR were 77% and 81% higher than that of the Route-Stability and Route-Closeness.  
Figure 4: Acceptance ratio with different numbers of requests.  
Figure 3: Average throughput with different numbers of available channels.  
Figure 2: Average throughput with different numbers of requests.
# Implementation of Constrained Swept Synthetic Aperture Using a Mechanical Fixture
## 1 Introduction
Motion has been extensively used in ultrasound to form larger fields of view [19] and 3-D images [20], both using external tracking systems [21; 22] and image-based motion estimation [23; 24] approaches; however, these methods simply stitch together B-mode images rather than coherently combining data for higher resolution. The SSA approach requires precise array position and orientation knowledge during the sweep to perform synthetic aperture beamforming of the data from each array position to a common imaging field of view. It is known that synthetic aperture imaging is particularly sensitive to errors in the axial direction, requiring sub-wavelength precision [25]. SSA imaging similarly requires sub-wavelength jitter error (random position-to-position variation) but is much less sensitive to calibration errors (a fixed error in position/orientation) [12]. Initial SSA investigations found varying impacts of translation and rotation errors in different directions with approximately equal impacts of translation in the axial and lateral dimensions (an achieved reconstruction precision of 0.1 mm given jitter error around \(\lambda/5\) or calibration error around \(2\lambda\) for both dimensions). Previous approaches to SSA imaging have used either a robotic positioning system or a mechanical fixture with external position measurement to achieve this required precision [12; 17]. These approaches also required precise spatial calibration to relate the position measurement to the actual position and orientation of the array. These requirements significantly limit the translation of the approach to clinical settings. This work presents an alternative SSA approach that makes use of the ultrasound array signals themselves to estimate transducer motion with sufficient precision for synthetic aperture beamforming. This approach constrains the motion to one degree of freedom using a simple external fixture with no measurement capabilities to simplify the estimation problem. This work characterizes two methods of estimation--lateral speckle tracking and channel signal correlation--for use in this application. Preliminary phantom results were presented in conference proceedings [26] and are expanded here in addition to a simulation study.
# Security Threats and Cryptographic Protocols for Medical Wearables
###### Abstract
A wearable medical device to measure the temperature of the foot consists, fundamentally, of a series of sensors that continuously (in a given period of time) measure the temperature at different regions of interest (ROI) of the foot that have been previously selected. A sock with sensors located in specific ROI is a wearable capable of sending data to an external device and create an alarm in case of ulceration development. Furthermore, it is made up of conductive tissues that carry these measurements to a base station or sink. A health surveillance wireless sensor network designed to deliver personalized healthcare is also called a body area network (BAN) [19]. Figure 1 shows a representation of the connections of a wearable that takes measures of foot temperature, the architecture of a conventional wearable network and the main elements: the wearable itself, a base station/gateway, cloud servers, users, and their control tools. The wearable includes elements of the network that are responsible of creation, detection and (pre-)checking of basic information. On the other hand, routing protocols in a WHMS are established in four tiers: the first one is within the device, where sensors collect the data and send them to the controller through a thermal-aware routing protocol, such are ATAR [20] or WETRP [21]. Then, the controller processes the received information  
and send it to the gateway. Finally, collected data are transmitted to the user mobile phone by Bluetooth Low Energy (BLE), Wi-Fi or Radiofrequency (RF), and from here to his/her computer or the corresponding health-center through Internet [22; 23]. The security needs for IoT-based healthcare solutions are similar to those considered in common communication scenarios: integrity (patient information cannot be altered or changed by unauthorized personnel), availability (data must be available to authorized persons upon request), confidentiality (sensitive information cannot be used by unauthorized individuals, entities or processes) and non-repudiation (individuals cannot deny an action they have carried out) [24; 25]. The main limitation of medical applications is their constrained resources. This drawback makes several cryptographic protocols not appropriate for these devices with small size and memory, that require low computation power, short time battery, short memory, and low bandwidth. Lightweight cryptography (LWC) reduces computation time and complexity and assure security [26; 27; 28]. This paper is organized as follows: Section 2 includes a detailed description of the security in medical devices, with an emphasis on vulnerabilities in their design and functioning protocols. A simulation of a malware attack against medical devices is included in Section 3. As a solution to make the devices more secure, a proposal of lightweight cryptography protocol is included in Section 4. Finally, the conclusions of this study are stated.
# Data Hiding and Authentication Scheme for Medical Images Using Double POB
## 3 The Proposed Scheme
### Information Extraction and Lossless Recovery
#### 3.3.3 Image Reorganization and Recovery  
The obtained ShareA and ShareB undergo bit plane separation, and the data are reorganized. To obtain the ROI data, the high five bits of ShareA and the low three bits of ShareB are extracted, and then they are combined. Then, extract the low three bits of ShareA and the high five bits of ShareB and combine them to obtain the RONI data. Finally, combine the ROI and RONI data to recover the ROS. Figure 6 depicts the detailed steps involved.
## 3 Indoor AR Navigation Framework Based on Geofencing and Image-Tracking
### Indoor AR Navigation Application Use Case  
In this subsection, we introduce an example of a smartphone application for the Mineral Industry Museum, Akita University, that was implemented using the proposed simple framework for indoor AR navigation. The application's main function is visual navigation by displaying 3D signs on the AR display. In contrast, the current position, movement trajectory, and route to the destination are simultaneously displayed on a 2D floor map. As revealed by [PERSON] et al., the combination of AR and traditional 2D maps can enhance wayfinding performance and spatial knowledge acquisition [36]. Auditory navigation is achieved using high-accuracy positioning to provide a push-type service for voice narration at the appropriate location and time. In this application, the exhibits' locations in the museum and spatial information, such as stairs and turns along the route to the exhibits, are registered in the POI and route database as spatial data. Their positions are unified in a local coordinate space based on the origin of the location reference marker at the entrance of the building (e.g., a signboard of a floor map). First, the user chooses an exhibit as a destination from a list of registered exhibits on the smartphone screen. Visual navigation then guides the user to the destination using the 3D sign at the center of the screen, and the route to the destination on the floor map is displayed at the bottom of the screen, as shown in Figure 2. Specifically, the 3D sign indicates the direction to each waypoint, such as each turn and stairway required to reach the destination. The route to  
Figure 1: Data flow diagram of a simple framework for realizing indoor AR navigation.  
the final destination is displayed on the floor map, which corresponds to the intermediate destination on each floor. For example, if the destination is on the second floor and the user is on the first floor, the route to the stairs to move to the second floor is displayed on the first floor's map. Auditory navigation is realized by placing geofences at turning points and staircase junctions and automatically playing voice narration when the user enters a geofence.
## 5 Experiments and Results
### Regression Models
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Unit A**} & \multicolumn{2}{c}{**Unit B**} \\ \cline{2-5}  & **RMSEmean** & **MAEmean** & **RMSEmean** & **MAEmean** \\ \hline Baseline 1 (C5 median) & ** 1.1179 & **0.6028** & **0.1174** & *** 0.0853** \\ \hline Baseline 2 (LR) & ** 1.1794** & 0.7601 & 1.4150 & 0.7248 \\ \hline Model 1 (LR) & 1632.9693 & 560.4650 & 96826.5563 & 46730.6566 \\ \hline Model 2 (SVR) & ** 1.0754** & **0.6087** & * 0.1240 & _0.0991_ \\ \hline Model 3 (MLPR) & ** 1.0728** & _0.7122_ & 0.2115 & 0.1424 \\ \hline Model 4 & **1.0352** & ** 0.6127** & * 0.1201 & * **0.0871** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Experiment 1 results. Mean RMSE and MAE values we obtained for different models with a ten-fold cross-validation, repeated 50 times. Best results are bolded, second-best are reported in italics. The results within the same column, which have no statistically significant difference between them when tested with a Wilcoxon paired rank test at a 95% confidence level, are marked with * and **. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Unit A**} & \multicolumn{2}{c}{**Unit B**} \\ \cline{2-5}  & **RMSEmean** & **MAEmean** & **RMSEmean** & **MAEmean** \\ \hline Baseline 1 (C5 median) & 1.1760\(\downarrow\) & _0.6141\(\downarrow\)_ & **0.1152\(\uparrow\)** & **0.0818\(\uparrow\)** \\ \hline Baseline 2 (LR) & _1.0603\(\uparrow\)_ & 0.7009\(\uparrow\) & ** 0.2126\(\uparrow\)** & 0.1978\(\uparrow\) \\ \hline Model 1 (LR) & 1198266158.9503\(\downarrow\) & 411001902.3054\(\downarrow\) & ** 0.2753\(\uparrow\)** & 0.1900\(\uparrow\) \\ \hline Model 2 (SVR) & 1.1098\(\downarrow\) & _0.6193\(\downarrow\)_ & * 0.1287\(\downarrow\)_ & 0.1021\(\downarrow\) \\ \hline Model 3 (MLPR) & 1.0771\(\downarrow\) & 0.7234\(\downarrow\) & 0.2044\(\uparrow\) & 0.1581\(\downarrow\) \\ \hline Model 4 & **0.9655\(\uparrow\)** & **0.5743\(\uparrow\)** & * 0.1270\(\downarrow\) & _0.0852\(\uparrow\)_ \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Experiment 2 results. Mean RMSE and MAE values we obtained for different models with a ten-fold cross-validation, repeated 50 times. Best results are bolded, second-best are reported in italics. The results within the same column, which have no statistically significant difference between them when tested with a Wilcoxon paired rank test at a 95% confidence level, are marked with * and **. The arrows indicate whether the mean result improved (\(\uparrow\)), or degraded (\(\downarrow\)) when compared to Experiment 1.
# A Survey on Open Radio Access Networks: Challenges, Research Directions, and Open Source Approaches
## 2 Evolution from Traditional RAN to vRAN
### vRAN  
Virtualization means creating virtual instances over abstracted physical hardware [2]. In the context of vRAN, the virtual instances are network resources. Virtualization in RAN is heavily related to concepts such as software-defined networking (SDN) and network function virtualization (NFV) [8]. Simply, vRAN brings virtualization to Cloud RAN. In the cloud server, multiple virtual BBUs (vBBUs) are deployed. vBBU can be deployed on a Virtual Machine (VM) or container. This system enables orchestration and resource scaling in vRAN. Ease to scale up and down network resources led to lower energy consumption, dynamic capacity scaling, efficient use of network resources, improved service reliability, and better service quality [2; 13]. Approximately 50% of data-processing resources required can be reduced when virtualization is applied on Cloud RAN [14].  
Overall, vRAN minimizes the operational and investment costs for MNOs. However, vRAN has to deal with the properties of the wireless channel. Network resources must be shared and distributed fairly and efficiently to different RRUs while considering QoS requirements. This made the vRAN system to be significantly complex. vRAN also still uses proprietary interfaces that prevent interoperability and a multivendor environment. As a result, vendor lock-in and monopoly prevent the network equipment price from being cheaper.
## 3 Results
### Performance of DMSA/PDA/ZnO in the Analysis of Ulltra-Trace Cd(II)
#### 3.7.2 Accuracy of the Method  
An environmental reference material (GB807-1185-2000) with a Cd(II) concentration of 0.298 \(\pm\) 0.01 mg/L was analyzed. The environmental reference material was diluted by a factor of 20. The reference material solution after dilution was determined by the experimental procedure that is shown in Section 2.5. The obtained Cd(II) concentration was 0.289 \(\pm\) 0.004 mg/L, which was in good agreement with the certified value. The \(T\)-test results indicated that the analyzed value was not statistically significantly different from the certified value.
## 3 Results
### Reliability of Reactive Agility Components  
The reactive agility components demonstrated high reliability, with ICC values of 0.95 for the initial 5 m sprint time, 0.91 for the COD-phase time, and 0.97 for the total Y-Shaped Reactive Agility time. The SEM and CV values were consistently low, with CVs ranging from 1.15% to 2.40% (Table 4). These findings support the reliability of the Y-Shaped Reactive Agility test for evaluating reactive agility performance in U12 male soccer players.  
Overall, our findings highlight strong associations between sprint speed, change-of-direction agility, and reactive agility, emphasizing the importance of integrating physical and cognitive training elements in regard to young soccer players (Figure 5).  
\begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline
**Test** & **Test 1** & **Test 2** & **ICC (05\% CD)** & **SEM** & **\%SEM** & **CV (\%)** & **MDC** & **SWC0.2** & **SWC0.6** & **SWC1.2** \\ \hline
5 m (s) & 1.24 \(\pm\) 0.11 & 1.25 \(\pm\) 0.12 & 0.90 (0.79-0.95) & 0.012 & 0.94 & 2.91 & 0.032 & 0.02 (good) & 0.07(good) & 0.14 (good) \\
10 m (s) & 2.25 \(\pm\) 0.20 & 2.27 \(\pm\) 0.21 & 0.96 (0.91-0.98) & 0.009 & 0.41 & 2.04 & 0.026 & 0.04 (good) & 0.12 (good) & 0.25 (good) \\
15 m (s) & 3.12 \(\pm\) 0.26 & 3.19 \(\pm\) 0.29 & 0.93 (0.86-0.96) & 0.022 & 0.69 & 2.60 & 0.060 & 0.06 (good) & 0.17 (good) & 0.33 (good) \\ \hline COD & & & & & & & & & & \\ MAT & 8.33 \(\pm\) 0.97 & 8.28 \(\pm\) 0.86 & 0.97 (0.94-0.98) & 0.028 & 0.33 & 1.86 & 0.077 & 0.03 (good) & 0.08 (good) & 0.15 (good) \\ \hline Y-SRA (s) & & & & & & & & & & \\ Y-SRA Initial 5 m & 1.39 \(\pm\) 0.12 & 1.38 \(\pm\) 0.13 & 0.95 (0.89-0.97) & 0.007 & 0.53 & 2.40 & 0.020 & 0.02 (good) & 0.07 (good) & 0.13 (good) \\ Y-SRA COD & 1.58 \(\pm\) 0.12 & 1.60 \(\pm\) 0.10 & 0.91 (0.82-0.96) & 0.008 & 0.49 & 1.70 & 0.022 & 0.04 (good) & 0.13 (good) & 0.26 (good) \\ Y-SRA Total & 3.01 \(\pm\) 0.21 & 3.02 \(\pm\) 0.22 & 0.97 (0.95-0.98) & 0.006 & 0.20 & 1.15 & 0.017 & 0.18 (good) & 0.55 (good) & 1.10 (good) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Test–retest reliability of sprint, Modified Agility T-Test (MAT), and reactive agility (Y-SRA) in U12 male soccer players.  
Figure 4: Relationship between 15 m sprint time and total Y-Shaped Reactive Agility time in U12 male soccer players.
# Structural Health Monitoring Using Machine Learning and Cumulative Absolute Velocity Features
## 2 Setting of the Computer Experiments  
In this study, a computer experiment has been conducted to evaluate the damage detection and assessment capabilities of one-dimensional CAV based features. These features were extracted from acceleration responses of a single-degree-of-freedom (SDOF) system. Nonlinear time history analysis (NLTH) was utilized to generate the acceleration responses from this SDOF system. The computed features were subsequently utilized as training and test data for a comparative study between five well-known ML algorithms. In the following subsections, the computer experiment is described in detail.
# Combining Advanced Feature-Selection Methods to Uncover Atypical Energy-Consumption Patterns
## 5 Analyzing the Selected Features
### Household Income Levels  
The analysis of household energy consumption concerning income levels is presented in Figure 15, showcasing the distribution of various income levels across different consumption profiles.  
Figure 14: Cumulative bar chart of the BED percentage in each consumption profile. (**a**) K-means, (**b**) AHC, (**c**) SOM.  
In the medium-consumption profile, dwellings at the lowest income level comprise only about 9%, which reduces to zero in the high-consumption profile. A similar decreasing trend is observed for the second income level, representing around 19% in the medium profile (22% in the AHC profile) and never exceeding 15% in the high profile, irrespective of the clustering method.  
Considering that most households in both the medium- and high-consumption profiles fall into the highest income category, it is reasonable to infer that dwellings within these profiles' first and second income levels are outliers or 'out-profiled' households. These findings emphasize the influence of income on household energy consumption and the divergence of lower-income households from the typical consumption patterns of their respective profiles.
## 4 Results
### SAM vs. SAM 2
#### 4.1.7 Hypothesis Test  
A paired \(t\)-test was performed to determine if the differences between SAM and SAM 2 were statistically significant, as the segmentation results came from the same set of images segmented by both systems. The results are shown in Table 5 and Figure 11.  
The analysis of the results reveals that SAM 2 consistently outperforms SAM in the Coronacases and Radiopaedia datasets, as indicated by highly significant \(p\)-values across both the Jaccard and Dice metrics. The t-statistics are strongly negative, confirming SAM 2's superior performance in these cases. However, in the Montgomery dataset, the results show no significant difference between SAM and SAM 2, with \(p\)-values well above the typical threshold for significance. This indicates that the two systems perform similarly on this dataset, suggesting that SAM 2's advantages may be less pronounced in specific contexts. While SAM 2 shows robust improvements in most cases, its performance relative to SAM may vary depending on the dataset.  
\begin{table}
\begin{tabular}{l l l|l l|l l} \hline \hline  & \multicolumn{2}{c|}{**Coronacases**} & \multicolumn{2}{c|}{**Radiopaedia**} & \multicolumn{2}{c}{**Montgomery**} \\ \cline{2-7}  & **Jaccard** & **Dice** & **Jaccard** & **Dice** & **Jaccard** & **Dice** \\ \hline
**T-statistic** & \(-9.00\) & \(-8.70\) & \(-5.60\) & \(-5.58\) & \(-0.68\) & \(-0.76\) \\ \(p\)**-value** & \(4.72\times 10^{-19}\) & \(6.38\times 10^{-18}\) & \(2.97\times 10^{-8}\) & \(3.36\times 10^{-8}\) & \(0.498\) & \(0.447\) \\
**Result** & Reject & Reject & Reject & Reject & Fail to reject & Fail to reject \\ \hline \hline \end{tabular}
\end{table}
Table 5: SAM vs SAM 2 paired t-tests.  
With these results, we can conclude that SAM 2 improves SAM performance in medical image segmentation tasks. Given these results, it is safe to test SAM 2 in a more challenging scenario, such as video segmentation, knowing that it will not behave worse than SAM.
# Globally Optimal Facility Locations for Continuous-Space Facility Location Problems
## 3 Location Estimation Based on the Manhattan Distance  
In this section, we first illustrate the Manhattan distance and present the objective function based on the Manhattan distance. Then, we apply a statistical approach to obtain the global minimizer for the continuous-space single-facility location problem, where several properties of the minimizer can be obtained. With these properties of the minimizer, we can drive optimization procedures in searching for the globally optimal facility locations.
## 2 Materials and Methods
### Multifunctional Flexural-Electrical Characterization  
Electrical properties assessment and flexural properties were conducted concurrently while the test specimens were subjected to electrical conductivity and flexural tests. This test was conducted by connecting the two ends on the samples with a spacing of 49 mm, with a Keysight B2987A Electrometer (Keysight Technologies, Santa Rosa, CA, USA) to measure the electrical conductivity from end to end along the carbon fiber direction when exerted under flexural loadings. A three-point bending test was used for flexural properties assessment per ASTM D7264-15 [26]. A test rate of 0.00127 m/minute was used to conduct the flexural testing. The testing temperature was ambient, and relative humidity for the testing was ambient. Dimensions of the 3D printed test coupons were 140 mm \(\times\) 13 mm \(\times\) 3 mm. The average distance between the electrical contacts was 49 mm. The test coupons' coupled effects were measured using instrumentation and then computed for electro-flexural properties. After every 18 N, the electrical resistance of test coupons was measured until the test coupons failed. A schematic detail of the multifunctional flexural-electrical characterization test at RTD is shown in Figure 1. Similarly, the failure modes of the test coupons after the multifunctional flexural-electrical characterization test at RTD are shown in Figure 2. Lateral failure mode was observed on the test coupons with respect to its longitudinal axis on the lower side in the outer fibers. Resistance measurements were taken every 18 N until the failure of the test coupons. The maximum resistance value was chosen that occurred during the testing of coupons.