esa-sceva/satcom-chunk-collection huggingface dataset:
# User Mobility Modeling in Crowdsourcing Application to Prevent Inference Attacks
## 1 Introduction
How does one ensure that the user mobility model remains adaptable to dynamic contexts so as to cope with the flowing nature of user behavior and urban environments? 4. How does one deal with the challenge of countering inference attacks--what measures to effectively mitigate such threats within crowdsourcing platforms? These issues form the backbone of our research, guiding us toward developing a nuanced and context-aware framework for privacy protection. In this paper, we primarily focus on the first two challenges and aim to develop a mobility model for implementing inference attacks on crowdsourcing application, with the aim of gaining a deeper understanding of privacy leakage. The mobility model, constructed from the location history of users, provides rich and detailed insight into their movement behaviors. To summarize, our main contributions are as follows:  
1. Offering a comprehensive user mobility graph that provides an unprecedented view of spatial movements and interactions. This contribution is pivotal for designing secured crowdsourcing systems, ensuring that privacy-preserving measures are embedded at the core of digital infrastructures. 2. Developing an algorithm for the precise identification of POIs, which goes beyond traditional approaches by effectively categorizing significant locations in users' movement patterns. This enhances not only privacy protection strategies but also provides insights into urban mobility and spatial behavior, potentially influencing urban planning and smart city initiatives. 3. Evaluating location data disclosure risks via predictive inference attacks that unveil the latent vulnerabilities in existing privacy mechanisms. This critical insight empowers the development of next-generation privacy technologies, making it possible to proactively defend against potential breaches and safeguard sensitive personal information. The rest of this paper is organized as follows. Section 2 introduces the motivational scenario, discussing the specific challenges our approach aims to address. This clarifiesthe problems and gaps in current methodologies that our research seeks to fill. Section 3 reviews previous studies on extracting POIs and modeling user mobility. Next, Section 4 describes our proposed approach and the various algorithms we have developed to tackle the identified issues, detailing the theoretical foundation and practical mechanisms of our solution. Section 5 presents the set of experiments conducted to validate our approach, including the experimental methodology, the datasets used, and an analysis of the results obtained. Finally, Section 7 concludes this study by summarizing our main contributions and provides several future directions that we are willing to explore.
# Research on the Path Planning of Unmanned Sweepers Based on a Fusion Algorithm
## 6 Experimental Results
### Experimental Verification of Improved A* Algorithm and Fusion Algorithm  
Two random obstacles (white board and car) were added to the experimental environment, the starting point and the ending point were set on the same map, and the improved A* algorithm and fusion algorithm were, respectively, used to plan the path. The experimental map is shown in Figure 13.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters of Unmanned Sweeper** & **Numeric Value** \\ \hline Maximum line speed (km/h) & 2 \\ Maximum angular velocity (\({}^{\circ}\)/s) & 0.35 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Movement parameters of unmanned sweeper.  
Figure 12: Unmanned sweeper for the experiment.  
Figure 13: Environment map. (**a**) Simple environment map (**b**) Complex environment map.  
The solid blue line in Figure 14 indicates the global route planned by the improved A* algorithm. Figure 15 shows the effectiveness of the fusion algorithm for path planning and actual vehicle avoidance for an unmanned sweeper. The improved A* algorithm plans a path with better smoothness and planning time than the traditional A* algorithm, but the unmanned sweeper vehicle cannot avoid obstacles on the road. The fusion algorithm performs local planning on the basis of the global path. When an obstacle is detected ahead, the driverless sweeper performs an early obstacle avoidance maneuver and obstacle-avoidance path planning. Then, when the next obstacle is detected, it also performs an early obstacle-avoidance maneuver and changes its path until it reaches the target point. The experimental results are shown in Table 3. Compared to the improved A* and Dijkstra algorithms, the fusion algorithm outperforms both in terms of time and planned path length because it is able to plan ahead for static and dynamic obstacles in the path (Figure 16), reducing path wastage due to replanning. The fusion algorithm improves the planning efficiency by 65.46% in practice. It can be seen that the fusion algorithm can meet the requirements of optimal path planning and the car can reach its final destination efficiently and safely. [PERSON]'s algorithm produces replanning behavior when planning paths with known static and dynamic obstacles, which often leads to additional path loss. However, our proposed method enables early path planning in regions with obstacles through the evaluation function of the obstacle factor, and the reduction in the number of replans reduces unnecessary losses and, subsequently, relatively shortens the planned path. It is worth raising the point that, this study was carried out with undirected graphs and containing unknown obstacles, which may not be fair to the Dijkstra and A* algorithms; thus, future work is needed to add more experimental comparison groups to validate the soundness of the algorithms.  
Figure 14: Global planning route. (**a**) Simple environment map; (**b**) complex environment map.
## 3 Overview of UHS Globally and in the Baltics
### UHS Aspects in Lithuania  
Earlier, Lithuanian reservoirs were explored for CO\({}_{2}\) storage, specifically depleted subsurface hydrocarbon reservoirs and deep saline aquifers [85; 86]. These studies were based on multi-deterministic numerical models to evaluate the CO\({}_{2}\) storage potential. However, further data collection and integration are needed to develop a UHS project in Lithuania that will refine the storage capacity estimates for saline aquifers and depleted oil zones. The thickness of the Cambrian formation varies, from 90 to 100 m in Central Lithuania to 120-170 m in Western Lithuania, and 170-250 m in the Baltic Sea area. The Cambrian formation comprises the Deimena group sandstone, which is 70-90 m thick. The Deimena sandstone reservoir is sealed by thick carbonaceous-shaly caprocks and its depth ranges from 0.3 km to over 2 km. Mineralogically, the Deimena sandstone is a mature rock, comprising 90-99% quartz. The quartz grains are generally fine to medium in size (0.15-0.5 mm), though occasionally coarse grains can be found. Due to its deposition in an active marine environment, the original grains are well-rounded. In addition to the predominant quartz, the rock contains small amounts of feldspars, clay minerals, carbonates, pyrite, apatite, and other minerals [85; 86; 87; 88]. The Usenai dome is the only known salt body in the Upper Permian Prieglius formation in Lithuania, covering an area of 7 sq. km. The dome has a thickness of 56.5 m to 69.0 m in its central part, with a supposed thickness of about 75 m [89].  
The properties of Lithuanian reservoirs, particularly the Cambrian Deimena sandstone and the Usenai dome salt formation, suggest a strong potential for H\({}_{2}\) storage. The Deimena sandstone's depth (0.3-2 km), high quartz content (90-99%), good porosity, and thick carbonaceous-shaly caprock suggest that it can effectively trap hydrogen in porous media. Similarly, the impermeability, structural stability, and self-healing nature of the Usenai salt dome make it ideal for cavern-based H\({}_{2}\) storage. While these characteristics are promising, further studies on H\({}_{2}\)-specific factors like reactivity, cyclic loading, and long-term stability  
Figure 6: Map showing (**a**) the geographical extent and (**b**) the regional cross-section of the Baltic basin [85].  
are essential to confirm their suitability. Therefore, apart from CO\({}_{2}\) storage in Lithuania, it is imperative to examine the geochemical and geo-mechanical aspects of storing H\({}_{2}\), as the properties of H\({}_{2}\) and CO\({}_{2}\) are different. The properties of the explored saline aquifers and depleted hydrocarbon zones in Lithuania are presented in Table 1 [85,86,88], and the petrophysical properties extracted from the core samples are presented in Table 2 [90, 91, 92, 93].
# SIM-D: An Agent-Based Simulator for Modeling Contagion in Population
## 5 Implementation  
The SIM-D algorithm was implemented in C++ language using the concept of object-oriented programming (OOP). The persons were created using C++ objects interacting with each other objects through messages (function calls). A flowchart of the SIM-D is shown in Figure 4. The flowchart will be improved according to the selection of the disease.  
At the start of the simulation, the manager object first calls the input reader to initialize the Person, Location and Disease State objects. The person-person interaction graph is loaded into Person objects and the disease parameters are loaded into Disease State Object. The manager then starts the simulation time-step with the processing of person objects. As shown in Figure 5, each iteration has five basic steps.  
* Prepare Interactions--The person objects calculate the interactions that it has to perform with other persons. The interactions are computed based on the input interaction graph, its status (infected or susceptible), its vaccination status and isolation criteria.
* Send Interactions--The person objects send the calculate interactions to intended person objects.
* Compute Interactions--The person objects compute one-to-one interactions between itself and received messages. The probability of infection is calculated using Equation (1).
* Send Outcomes--If an interaction results in an infection, a message is sent to the relevant person object with the outcome of the interaction.  
Figure 3: A simple population network graph. The vertices represent persons and the edges show interactions between them. The weights on vertices show the demographics of the individuals.  
* The person objects and location objects update their status according to the outcome of interactions.
## 2 Materials and Methods
### Parametric Images from Radiofrequency Signals
#### 2.2.2 Phase Image  
The spectral representation of data generally provides distinct characteristics of the information carried by a signal in a different domain instead of a time (or spatial) domain. Fourier transformation is the most widely utilized technique to represent a time-varying signal in the frequency domain and provides information about the magnitude and phase in every frequency component. While the magnitude of the Fourier transformation contains the overall properties of the time-varying signal's behavior, the phase information preserves other significant features, including the instantaneous change in signal behavior. [PERSON] and [PERSON] first addressed the importance of phase information [23], and [PERSON] and [PERSON] also proved the characteristics of phase information from a statistical perspective [24]. The phase information of an incident wave in particular carries spatial information, such as edges or boundaries, which is preserved for phase-only reconstruction.  
In this study, a phase image was obtained from the same envelope image as an entropy image, which is technically the phase-only reconstructed image. It was reconstructed by taking the inverse FFT with the original phase information of the individual envelope image and the average magnitude information of the entire dataset. Because the individual RF signals in the dataset used in this experiment had the same lateral size but different axial sizes, the FFT was performed with a sufficiently large data point first, and further, the reconstructed image was re-sampled to its original size after inverse FFT. Entropy and phase images are also calculated from a B-mode image instead of an envelope image. However, we utilized the envelope image to obtain two parametric images because an envelope image is undistorted by log compression for human visualization. Figure 3c shows the sample phase image generated from the RF signals.  
Figure 3: Sample images generated from the RF signals. (**a**) B-mode image; (**b**) Entropy image; (**c**) Phase image; (**d**) Attenuation image.
## 6 Articles with Clinical Evaluation  
[PERSON] (1990) examined athletes during a sport climbing competition, reporting 18 climbers with hand injuries out of 67 examined. Clinical examination revealed increased bowstringing of the finger flexor tendon during resisted flexion compared to the contralateral side, indicating a previous A2 pulley injury [18]. In the same year, [PERSON] published a clinical case of a climber with an A2 pulley injury. The patient exhibited visible and palpable bowstringing with resistance during finger flexion [28]. In [PERSON] et al.'s study (1998), 21 cadaver fingers were used to simulate traumatic pulley ruptures by attaching tendons to a progressive loading system. A total of 17 of the remaining 19 fingers had an isolated A2 or A4 pulley rupture as the first event. This study suggested that clinical bowstringing for an isolated A2 or A4 pulley injury might be misleading [11]. [PERSON] et al. (1998) treated 13 experienced climbers for finger pulley injuries, diagnosing and treating based on clinical bowstringing, which MRI later confirmed. Only five patients with evident clinical bowstringing had a confirmed complete A2 pulley injury [29]. [PERSON] et al. (2019) presented a clinical case of a patient with an isolated A2 pulley middle finger injury. A silicone ring applied to the suspected injury region immediately restored the full range of motion, and the lesion resolved after three months without surgery. The proposed \"Wedding Band Test\" was potentially an accurate and cost-effective diagnostic tool for the clinical assessment of A2 pulley injuries [25]. [PERSON] et al. (2020) aimed to determine if a pulley injury implies a reduced grip strength and its relationship with clinical and sonographic signs. This observational study used a sample of 39 climbers with A2 or A4 pulley injuries in the third or fourth finger. The variables considered included palpation pain, ultrasound-calculated TBD, and grip strength reduction. The study suggested that a comprehensive diagnosis of finger pulley injuries involves considering clinical signs (pain), ultrasound findings, and grip strength measurement together [30]. [PERSON] et al.'s (2020) review proposed a potential classification scheme and approach for patients with A2 finger pulley injuries. The model is based on a pragmatic clinical analysis to help therapists distinguish patients who may benefit from treatments like rest and immobilization versus those who would benefit from an exercise and restorative resistance approach. The classification variables include pain, active range of motion (AROM), resistive finger flexion tests, and palpation [31].  
**7. Risk of Bias in Studies**  
The scores from the assessment of methodological quality of the studies are reported in Table 2 in ascending order
# Performance Analysis of On-Demand Scheduling with and without Network Coding in Wireless Broadcast
## 6 Performance Evaluation
### Simulation Results in Stretch Optimal Models
#### 6.3.2 Impact of Data Item Size  
Figure 7 shows the impact of the maximum data item size (\(SIZEMAX\)) on the performance of the stretch optimal algorithms. The larger value of \(SIZEMAX\) means a longer service time (i.e., transmission time) of a data item. Recalling that the response time is the summation of the request waiting time and the item service time. The large \(SIZEMAX\) increases the response time in two aspects. First, the transmission time is getting longer. Second, the longer service time of the current data item causes longer waiting time of other data items. As a result, with an increase of \(SIZEMAX\), the ARTs of both the non-coding and the coding versions increase (Figure 6(a)). Since the stretch is the ratio of the response time to the transmission time of the requested data item, the stretches of the non-coding versions do not changes much because both the response time and the transmission time increase with larger \(SIZEMAX\) (Figure 6(b)).  
In contrast, with an increase value of \(SIZEMAX\), the stretches of the coding versions increase. There is a tradeoff of applying network coding in scheduling for different item sizes. On the one hand, an encoded packet may consist of more than one item, which means that broadcasting an encoded packet can satisfy more pending requests simultaneously. Therefore, the average response time decreases. On the other hand, since the transmission time of an encoded packet equals the transmission time of the maximum size item of the corresponding clique, the transmission delay of an encoded packet increases, which results in the longer average response time. To sum up, whether an algorithm can take the benefit of network coding depends on which factors dominates the performance. From Figure 6(a), we note that ART of a coding version is better than its non-coding version under different \(SIZEMAX\) values. However, in terms of minimizing the stretch, a coding version cannot retain its superiority over the non-coding version when \(SIZEMAX\) increases to 60 (Figure 6(b)). For instance, when \(SIZEMAX\) is as low as 10, the coding versions of both LTSF and STOBS improve around 18% stretch over the non-coding versions; however, this improvement diminishes very quickly when \(SIZEMAX\) increases to 40 (3% maximum improvement). Even when \(SIZEMAX\) reaches to 60, the improvement becomes negative. The reason is due to the penalty of small data items, namely the coding version of an algorithm incurs higher AS for small data items. The penalty to small data items increases with the value of \(SIZEMAX\). Please note that \(SIZEMAX\) have no impact on theAELs (Figure 7c). Accordingly, the ABPs of different algorithms are similar under different item sizes (Figure 7d).
## 2 The Related Work
### Basic Scenarios and Parameters Setting  
In the previous work [6], we confirmed that the antenna gain is related to the deviation of the azimuth and elevation angle from the boresight, and we proposed a plan to protect the target. For the protection target, i.e., the FSS ES, the ES is assumed to be located at the center of the area with a coordinate of (0, 0). Further, the azimuthal orientation of the ES antenna is assumed to be true north, while the ES antenna elevation orientation, i.e., elevation angle, differs for various ESs, according to the ES deployment requirements. The cellular BS (eNodeB/gNodeB) was assumed to be deployed at any possible location (X, Y)  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Space Domain Exclusion Zones** & **Angular Domain Exclusion Zones** \\ \hline Advantage & Simple and effective & No/less restricted on the space domain \\ \hline Disadvantage & Network coverage dead zones & Advanced antenna is required to generate a directional beam \\  & Restricts 5G deployment & Increases the cost and complexity of the system \\ \hline \hline \end{tabular}
\end{table}
Table 1: Advantages and disadvantages of using various protection methods.  
around the ES. Moreover, the plan assumed that the azimuth of the BS antenna was true south. The relationship between the positions of BS and FSS ES is shown in Figure 2.  
During the simulation, we used a three-dimensional (3D) (X, Y, Z) coordinate system to present the results. In this coordinate system, the \(x\)- and \(y\)-axes represent the latitude and longitude coordinates of the BS, respectively, and the \(z\)-axis represents the target measurement value, such as the ES antenna gain, path loss, and 1/N data. Three scenarios were considered in this study: RMa, UMa, and UMi [14]. The ES antenna heights and diameters, BS antenna heights, intersite distances, propagation losses, shadow fading, and LOS/nonline of sight (NLOS) probability distributions are different for each scenario. Furthermore, the carrier frequency was set to 3.95 GHz, which corresponds to the center frequency of the 3.7-4.3 GHz range. The FSS ES simulation parameters used in all considered scenarios are presented in Table 2.
# A Feasibility Study of Utilizing Nuclear Energy for an Existing MED-TVC Desalination Plant
## 4 Water Cost Estimation  
The heat required to drive the existing MED-TVC desalination plant was obtained by simulating the desalination plant using Aspen Plus, and it was about 169 MW (th). This amount of heat is utilized as a heat input in DEEP, which was modeled and developed by the IAEA (International Atomic Energy Agency). DEEP was used in this analysis to estimate the desalinated water cost as USD/m3.  
The methodology used in DEEP is based on simplified models of several types of power plants, including fossil and nuclear types, as well as models for desalination plants, including MSF, MED, and RO. Any specific plant can be modelled in DEEP simply by adjusting input data in the program interface. Such data includes power and water plant size, cost, and performance characteristics. The power plant's economic evaluation model consists of capital costs and operating costs. Capital costs involve the total plant investment costs, including overnight construction costs, contingency factors, and construction time interest. The operating costs include fuel costs, O&M specific costs, and the carbon tax. After annualizing capital costs using a specific discount rate, both capital and operating costs represent the total annual power cost. An important parameter can then be estimated, which is the LCOE (levelized cost of electricity). LCOE is generally used to compare different electricity generating technologies and can be calculated using Equation (7).  
\[\text{LCOE}=\frac{\text{Total Annual Power Cost}\left(\frac{\text{S}}{\text{yr}} \right)}{\text{Annual power Production}\left(\frac{\text{kWh}}{\text{yr}}\right)} \tag{7}\]  
The desalination plant economic evaluation model is very much the same as the power plant economic evaluation model. The only difference is that the capital costs include additional costs related to the backup heat source, while the operating costs include energy costs for heat instead of fuel in the power plant evaluation model. The main output data calculated by DEEP is the level cost of electricity and water. The analysis in this paper considers two types of heat sources. The first one is a nuclear heating reactor dedicated to supplying heat only to the MED-TVC desalination plant. The second one is an oil-fired steam boiler, which is used in the existing MED desalination plants in Saudi Arabia. The same design parameters used in the simulated MED-TVC desalination plant were used in the DEEP model. These parameters are reflected in Table 6.