esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Results and Performance Evaluation
### Inter-nodal Distances  
We varied the inter- nodal distance from 5 to 30 meters and observed that the jamming indices of the nodes increased as we increased the inter-nodal distance and decreased when we decreased the inter-nodal distance. For example, the JIs in respect of Node-13 in the set-up described in Figure 6 were found to be 44.49, 62.93, 77.21, 88.95, and 98.27 for the inter-nodal distances of 5, 10, 15, 20, 25, and 30 m respectively, keeping the other factors unchanged. It indicates that a denser WSN is less vulnerable to jamming.
# Enhanced Three Layer Hybrid Clustering Mechanism for Energy Efficient Routing in IoT
## 2 Related Work
For example, if a CH node dies, the data packets from its neighboring nodes should be re-routed, but it cannot be happen in a fully centralized mechanism until the new schedule is not updated by the central controller. On the other hand in distributive case, sensor nodes can handle re-routing locally by themselves very quickly [20]. A combination of centralized and distributive approaches has better outcomes. In [21], the authors propose a traffic deduction algorithm that reduce the overall all data packets volume by exploring intra correlation of data packets generated by the sensor nodes. This algorithm prioritizes the unusual detected data packets and guarantee its delivery to the base station. Geographic routing is also broadly used in WSN due to its efficiency and simplicity. However, it suffers from routing holes where there is no eligible node to forward data packets further and thus data packets are stopped at the hole boundary. The authors in [22] propose a distributed hole bypassing mechanism that tackles the load imbalance as well as routing path enlargement issues. In [23], a distributed learning automation (DLA) based algorithm is proposed to improve network lifetime by taking many routing constraints such as end to end delay and reliability into consideration in the selection process of routes for data forwarding toward the base station. A traffic minimization mechanism is proposed in [24] to reduce the data required for communication by exploring the intra correlations of the sensors data. This mechanism which is proposed to detect landslide, reduces energy consumption of WSN without degrading the detection performance of sensor nodes. The authors in [25] define a topology control protocol in which the deployed sensor nodes learn and choose proper transmission range from the reinforcement signals of neighboring nodes. The network lifetime is increased by adopting lower transmission range for every node. The authors in [26] propose algorithm that provides certain level of protection to each sensor node with minimum energy consumption.
## 1 Introduction
### Related Works
In both centralized schemes, the final decision is informed back to the CRs through the control channel. The access algorithm adopted by the secondary network then takes place. It is worth mentioning that the role of an FC in a centralized cooperative spectrum sensing can be assigned to a cluster-head in the context of clustered network topologies [19], which is the case of most wireless sensor networks (WSN). This clustering approach can be of particular value in large area networks, where the adoption of a single FC could prohibitively increase the control channel traffic and lead to inefficient spectrum utilization. This inefficiency can be caused by the distinctive spectrum occupancy in different regions of the network, which could be misled by large-area-based centralized decisions.
## 3 Results
### The Relation between pH and Soil Microarthropods  
We correlated the 2019 soil microarthropod data (between 2019 a + b and 2019 a) and abundance of soil microarthropods 2019 (a + b) with pH. We can see a significant correlation between 2019 a + b and 2019 a, \(r\) = 0.917, \(p\) = 0.028. Moreover, Table 2 gives the values for 6 species positively correlated with pH, i.e., have a preference for more neutral environments and for 17 species negatively correlated with pH, i.e., have a preference for more acid environments. There are two examples of species belonging to the same genus with opposite pH preferences: _Hypocththonius luteus_ and \(H\). _rufulus_ (Oribatida) and _Rhodacarus calcarulatus_ and _R. coronatus_ (Mesostigmata). Figure 4: pH (KCI) values in 1959, 1987, and 2019 with standard deviation. Among species listed in Table 2, just four species showed a relationship with pH values, also considering the past samplings: _Hypochthonous rufulus_, _Platynothrus peltifer_, _Nanhermannia nana_, and _Achiptera coleptrata_. All these species show a similar pattern (Figure 6): a clear preference for the more acid plots in 1959 (_Achiptera coleptrata_, Figure 6d, is more intermediate), an increase of the range and numbers towards the lower plot numbers in 1987 (acidification of the area), and a retraction again to the higher plot numbers in 2019 (resilience of pH, compare Figure 5). \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **1959** & **1987** & **2019 a** & **2019 a + b** \\ \hline Orbitaida & 330 & 637 & 317 & 645 \\ Astigmata & 31 & 34 & 2 & 5 \\ Endeostigmata \# & & & 1 & 48 \\ Mesostigmata & 16 & 126 & 98 & 121 \\ Prostigmata & 2 & 80 & 14 & 56 \\ Collembola & 19 & 142 & 128 & 266 \\ Protura & 2 & & 2 & 7 \\ Diplura & & 1 & 3 & 5 \\ Pauropoda & & & 1 & 5 \\ Symphyla & & & 1 & 1 \\ Pseudoscorpionida & & & 1 & 1 \\ Species richness \({}^{*}\) & 48 & 43 & 122 & 133 \\ \hline \hline \end{tabular} \# Endeostigmata were included in Prostigmata in 1959 and 1987. \({}^{*}\) In 1959 and 1987, only orbidids were identified to the species level and Oppiidae/Suctobelidae were pooled: 13 spp. in 2019.  
\end{table} 
Table 1: Numbers of microarthropods (total number of specimens/number of samples) found during the years. Figure 5: Densities of all microarthropods (**left**) and Orbitaida only (**right**) in the three periods 1959, 1987, and 2019 (a + b). Species density in our paper is defined as the total number of individuals m\({}^{-2}\). A comparison of datasets of 2019 (a) and 2019 (a + b) reveals that the complete set 2019 (a + b) has four more microarthropods (_Nanorchestes arboriger, Nothrus anauiensis, Friesea mirabilis,_ and _Pseudosinella alba_) that are significantly positively correlated with pH and two significantly negatively correlated (_Dissorhina ornata_ and _Poecilophysis pseudoreflexa_) (Table 2). On the other hand, one species turned out to lose its correlation in the completed dataset: _Isotoniella minor_ appears to give a false positive result in set 2019 (a).
## 3 Compact Model Validation
### De-Embedding Procedure
#### 3.1.3 InGaAs/InP UTC-PD De-Embedding  
The method of de-embedding therefore can be generalized and extended to a wide range of frequency and various Open test structure designs. Taking advantage of this methodology, a dedicated InGaAs/InP UTC-PD Open test structure was characterized using the same setup and measurement procedure as the one used for the test structures of the InGaAs/InGaAsP UTC-PDs. The Open test structure (Figure 9a) was modelled by a \(R_{n}L_{n}C_{n}\) cascade of nine sections. This was achieved by optimizing the \(\sum_{i=1}^{N=9}C/N\) and \(\sum_{i=1}^{N=9}L/N\) values on the imaginary part of \(Y_{Open}/\omega\), as shown in Figure 9c. Note here that since we did not have a Short test structure, we used a Thru test structure (Figure 9b) to determine the series resistance, \(\sum_{i=1}^{N=9}R/N\). This was achieved by converting the measured tow-port \(S\)-parameters into the transfer matrix \(ABCD\), then by determining the complex propagation constant, \(\gamma=\alpha+j\beta\), and the characteristic impedance of the Thru test structure, \(Z_{c}\), from the \(ABCD\) matrix (Figure 9d). Finally, the linear series resistance, \(R_{Thru}\), (Figure 9e) can be determined from the real part of the product \(\gamma\cdot Z_{c}\)[29]. By multiplying the \(R_{Thru}\) by the length (278 \(\mu\)m) of the pads and the access lines of the InGaAs/InGaAsP UTC-PD, we obtain \(\sum_{i=1}^{N=9}R/N\). The number of the \(R_{n}L_{n}C_{n}\) cascade is a bit high in this case since, on the one hand, we work at higher frequencies (up to 110 GHz), and on the other hand, the access lines of the test structures are relatively longer than the previous case. The values of these elements are reported in Table 4. Once the electrical equivalent network is accurately modeled, two-port \(S\)-parameter simulations of the distributed \(RLC\) network are used to determine the intrinsic parameters of the UTC-PDs by applying Equation (9).  
\begin{table}
\begin{tabular}{c c c} \hline \hline \(R\) & \(L\) & \(C\) \\ \hline
0.19 \(\Omega\) & 47.55 pH & 11.49 fF \\ \hline \hline \end{tabular}
\end{table}
Table 3: Extracted RLC lumped elements values from GaInAsSb/InP UTC-PD Open and Short test structures.  
Figure 9: **(a)** Microscope image of the Open test structure; **(b)** microscope image of the Thru test structure; **(c)** extracted \(LC\) parasitics of the equivalent circuit for the InGaAs/InP UTC-PD Open test structure; **(d)** extracted attenuation constant \(\alpha\), and phase constant \(\beta\) of the Thru test structure; **(e)** extracted characteristic impedance, \(Z_{c}\), and calculated linear series resistance \(R_{Thru}\) of the Thru test structure.
# Real-Time Automated Segmentation and Classification of Calcaneal Fractures in CT Images
## 2 Materials and Methods
### Fractures Classification in Calcaneus  
On the basis of subtlar joint involvement, calcaneal fractures observed in CT images have been divided into two categories: Intra-articular and extra-articular. Intra-articular is a fracture involving the joints, including damage to the connective tissue between two bones. Extra-articular is a fracture that does not involve the joints, rather, it includes pieces of bone drawn from the calcaneus by the ligament.  
The intra-articular fractures of the calcaneus represent about 75% of all calcaneal fractures in adults [1; 2], where the prospect of recovery depends on how severely the calcaneus was damaged during injury. Several classification systems for intra-articular fractures of the calcaneus have been developed, in which the Sanders system [20; 21] is the most commonly used due to its correlation with clinical outcomes and lower inter-observer variability [22]. This classification divides the intra-articular fractures into four types, based on the number of fragments in the calcaneus, i.e., Type I, Type II, Type III, and Type IV.  
Figure 1 shows examples of calcaneal fractures on the transverse and coronal planes, the red circle shows the location of the calcaneus in the tarsal structure. Type I is a non-displaced extra-articular fracture with a displacement of less than 2 mm; this is also knowns as a line fracture. Types II-IV are displaced intra-articular fractures corresponding to an increase in the number of fragments, in which Type IV has more than three fragments.  
Extra-articular fractures represent about 25% of calcaneal fractures and include all fractures that do not involve the posterior aspect of the subtlar joint [1; 8]. Extra-articular fractures are normally caused by trauma, such as a crush or mild injury. Based on the location of the fracture, several methods [1; 10; 12] classify extra-articular fractures of the calcaneus into three types, i.e., Type A, Type B, and Type C. Figure 2 shows an example of fractures on the sagittal plane. The red circle shows the location of the calcaneal fractures in sagittal images. The Type A fracture involves the anterior process of the calcaneus (Figure 2b). The Type B fracture extends through the calcaneus or middle calcaneus, including the lateral processes (Figure 2c); and the Type C fracture is a calcaneal fracture involving the posterior (Figure 2d).
# CityBuildAR: Enhancing Community Engagement in Placemaking Through Mobile Augmented Reality
## 3 Research Design
### Literature-Based Application Review  
The literature review concentrated on discovering AR applications pertinent to participatory urban planning by the utilization of certain keywords, including 'Participation', 'Augmented Reality', 'Planning', and 'Public Spaces'. The criteria for selecting research papers were restricted to peer-reviewed articles, journal publications, and conference proceedings authored in English. Grey literature, book chapters, and non-English language publications were excluded. Skim reading was initially conducted, followed by a full reading of the main body, and papers lacking AR and placemaking content were subsequently excluded.  
Academic databases like Google Scholar, Scopus, and ScienceDirect were employed to guarantee thorough coverage in the search. Furthermore, the snowballing technique was employed to broaden the review's scope. This involved scrutinizing reference lists(backward snowballing) and evaluating citations (forward snowballing) to uncover supplementary articles of interest.  
A total of 33 AR applications were discovered, of which only 13 expressly focused on participatory urban planning components, including traffic planning, disaster visualization, infrastructure planning, and digital placemaking. However, the remaining 20 AR applications show the potential of using AR for community engagement, enhanced user interaction through real-time visualization, and user experience in various other sectors. Therefore, all the applications identified using the keywords were included in the study.  
Table 1 provides an overview of 33 AR applications, categorizing them by type (Mobile/Web), focus areas, and developers. The majority of these applications are mobile-based, while a few are web-based. Developers range from individual creators to research institutions, reflecting a wide array of AR innovations across various sectors, including urban planning, tourism, education, and business. The results underscore considerable deficiencies and constraints in the existing realm of AR tools for urban planning, establishing the foundation for the development of the CityBuildAR--application. Current AR applications designed for placemaking encountered technological issues, including location retrieval failures, the absence of a personalized user platform, and restricted functionalities in certain  
Figure 1: Methodology flowchart.  
applications, such as displaying information on screen and enabling comments instead of facilitating substantial contributions to placemaking.
# An Efficient and Secure Fog Based Routing Mechanism in IoT Network
## 4 Results and Discussion
### Decoding of Data at Cloud Server  
The decoding of data after it reaches the cloud server is described in Figure 8. The integrity of the data stays the same when it is decrypted using the decryption key at the cloud end, demonstrating that it was not altered on the way to the cloud server.
# Role of Radio Telescopes in Space Debris Monitoring: Current Insights and Future Directions
## 3 Radio Telescope Measurements with Active Transmitters  
Previous investigations have shown that several available facilities around the globe participated in collaborative space debris monitoring, based on the first architecture discussed in Section 1.2. These measurement campaigns have included Germany's Effelsberg Radio Telescope in collaboration with TIRA since 1996 [8], which is described in detail in Section 3.1. Another validation experiment was performed in the late 90s using Goldstone bistatic Radar in the USA [22], which is discussed in Section 3.2. The Arecibo Radio telescope, once the largest telescope in the world, in Puerto Rico, reported a few debris observations during the early 1990s. Sadly, this architecture has been dismantled due to collapse in 2020, as discussed in Section 3.3 [23]. Moreover, an initial stage space debris observational test was conducted in 2007 using the Medicina-Evaporia bistatic radar system, where the system consisted of the Evaporia RT-70 radar antenna in Ukraine as the transmitter and the Medicina VLBI 32 m dish in Italy as the receiver [24]. Unfortunately, due to the ongoing war in Ukraine, the Evpatoria antenna is no longer available for collaboration with the Western world.  
Moreover, Italy has two active bistatic radars based on radio telescopes. One of them is the BIRALES system, whose acronym stands for BI-static RAdar for LEO Survey, located in Medicina, Bologna [25]. The other is the BIRALET system, which is an acronym for BI-static RAdar for LEO Tracking, and its receiver is the Sardinia Radio Telescope (SRT) located in Sardinia [26]. A detailed review and the results from the BIRALES and BIRALET experimental campaigns are discussed in Sections 3.4 and 3.5, respectively. Our group in Pisa, Italy, has also contributed to this field by investigating the performance metrics and operational benefits of multibistatic configurations using three Italian radio telescopes as receivers [27; 28; 29], and it is discussed in detail in Section 3.6. In the last few years, experiments have been conducted by NATO Science and Technology Organization (STO) members for Geostationary Earth Orbit (GEO) monitoring [30], and it is reported in Section 3.7. A relevant additional investigation was carried out with SKA in South Africa, specifically utilizing the MeerKAT radar system, which is also explored in detail in Section 3.8.  
Recent studies have further advanced the application of radio astronomy to space debris tracking. For instance, Tianlai radio arrays have been explored as part of a bistatic radar system for LEO debris detection [31]. Additionally, ref. [32] demonstrated that passive RF observations using collocated antennas can refine orbit determination and reduce TLE-based errors to below 100 m and 0.2 m/s, as validated by the Canadian Space Agency. However, these specific findings are beyond the scope of this article and have not been reported here.
## 4 Implementation  
In this section, we present details of the implementation of our proposed method. The human-robot collaborative environment was set up in VR due to its advantage of quickly and safely establishing and testing collaborative scenarios in a close-to-reality situation. The experimental setup is illustrated in Figure 2A. The workspace is modeled using the Unity3D game engine and with the ROS# package provided by Siemens [39; 40]. In this environment, a human worker and a robot collaborate to assemble products designed using the ExperienceDNA tool [41]. An HTC Vive Pro was used to interact with a virtual reality environment. A Kinect 2 camera was used for skeleton tracking. The specifications of the system used to set up the simulated virtual reality environment, REBA score calculations, and the processes performed in this work are as follows: CPU: AMD Ryzen(tm) 9 5950X, 64 GB RAM; OS: Windows 10; GPU: Nvidia RTX 3070; HDD: Samsung SSD 970 1 TB. The virtual robot used during the collaborative task is Franka, a widely used robot platform in human-robot collaboration.  
A user wears the VR headset and starts the assembly task in which the user and the robots collaboratively assemble a product consisting of 22 workpieces. The assembly task scenario is as follows (see Figure 2B-D). At the start, the workpieces are placed on the table. The robot hands over the workpieces one by one to the user to assemble the product. The task is finished when all workpieces are assembled correctly. At each step during the collaboration, the PSO algorithm selects the robot's optimal end-effector positions for handovers. The evaluation involved measuring users' ergonomics from different starting points as feedback for the system to calculate the error between the previous handover and the most optimal location for handover. This error is influenced by factors such as height, handedness, and distance from the robot.  
In most cases, the optimization process took less than 5 s to reach the settling point. Figure 3 shows the control effort and the output of an example case. The value of \(k\) is calculated by the PSO algorithm (see Equation (1)). In this case, the system took 2.5 s to find the optimal solution. This time is comparable with the previous studies in human-robot object handover [42, 43]. It is worth mentioning that this time depends on the speed of the robot, which needs to be within the safety regulation and the specific task and can be decreased by increasing the speed of the robot.  
Figure 2: The system implementation. (**A**) The experimental setup. (**B–D**) The human–robot collaborative task in virtual reality.
# Improving Human Activity Monitoring by Imputation of Missing Sensory Data: Experimental Study
## 2 Methods
### Data Imputation
#### 2.3.1 Missing Samples Identification  
After data acquisition and cleaning, the existence of missing samples in each record was performed. Regarding the training of the artificial intelligence methods, the existence of missing samples causes some impact in the correct recognition of human activities. It may occur by different reasons, including the user not performing an activity for a complete defined activity duration, failures of the sensors, environmental noise, or problems with the mobile device used for data acquisition.  
Firstly, the number of missing samples in each record of the dataset was identified, analyzing the duration of each activity and the frequency rate of the sensors. The frequency rate differs from the sensors, where the frequency rate of the accelerometer and gyroscope was 100 Hz, and the frequency rate of the magnetometer was 10 Hz. Thus, the methods analyzed 500 samples for the accelerometer and magnetometer sensors, and 50 samples for the magnetometer sensor for each 5 s of activity.  
Figure 1: Workflow of the proposed methodology.  
Next, the number of missing samples for each capture was analyzed, excluding the samples that had less than 4 s of the data. Thus, the captures with more than 100 missing samples in the accelerometer and gyroscope sensors and the captures with more than 10 missing samples in the magnetometer sensor were discarded. This was done to be closer to the originality of the data than filling all synthetic samples to fulfill the space of missing samples.  
From the above analysis, the missing samples count was identified with Equation (1).  
\[\text{Missing Samples Count}=\text{(Frequency rate}\times\text{Activity Duration})- \text{Samples Count}\text{ in the Given Excerpt} \tag{1}\]  
Now, based upon the accelerometer and gyroscope specifications, Equations (1) and (2) were used, while in case of the magnetometer, Equations (1)-(3) were used:  
\[\text{Missing Samples Count}\text{ for Acc. and Gy}=\text{(100 Hz}\times 5\text{ s)}-\text{Samples Count}\text{ in the Given Excerpt} \tag{2}\] \[\text{Missing Samples Count}\text{ for Acc. and Gy}=\text{500}-\text{Samples Count}\text{ in the Given Excerpt}\] (3) \[\text{Missing Samples Count}\text{ for Magnetometer}=\text{(10 Hz}\times 5\text{ s)}-\text{Samples Count}\text{ in the Given Excerpt}\] (4) \[\text{Missing Samples Count}\text{ for Magnetometer}=\text{(50}-\text{Samples Count}\text{ in the Given Excerpt}. \tag{5}\]