esa-sceva/satcom-chunk-collection huggingface dataset:
# User-Cell Association for Security and Energy Efficiency in Ultra-Dense Heterogeneous Networks
## 1 Introduction
### Our Contribution  
Having considered the aforementioned state-of-the-art, a study that comprises the use of user-BS association in ultra dense networks to integrate physical-layer security alongside taking care of the energy efficiency has not been conducted so far. The contribution of this paper can be thus summarized as follows:  
* the investigation of the benefits of UE-BS association in improving selected performance indicators as well as minimizing energy consumption;
* the definition of a new utility function which integrates together the secure throughput per user and the energy consumption of the network. Thus, the cell activation and users' association are jointly performed;
* proposal of a heuristic for user-BS association and cell-activation selection able to achieve performance close to the optimal one;
* the application of the above-mentioned utility to the use case of an ultra dense network;
* the comparison of the proposed utility with three other utilities known in literature.  
The paper took into account mobile terminals, but the results are valid for any device able to connect to a cellular network. In the future cellular network (6G), the intelligence will be given to even small devices, such as sensors of any type, installed on the human body or in the environment or in portable objects (a bottle of water, etc.), or in moving objects (vehicles, drones, etc.), together with classical portable devices (smartphone, tablet, etc.). All these devices have to be constantly connected, and energy consumption as well as security are two of the main features to be absolutely guaranteed anytime-anywhere. Moreover, physical-layer security (PLS) is a promising mechanism to provide security to low-resourced devices like sensors, or, in general, when classical cryptography cannot be directly applied or to provide an additional level of security to the devices in the network. This paper proposes a metric which takes into account simultaneously the energy consumption and the physical-layer security provided to the user (in terms of secure throughput), based on the association between users and base stations. The term \"user\" can be interpreted in many ways, from a smartphone to, e.g., a wearable sensor.
# Privacy-Preserving Data Mining on Blockchain-Based WSNs
## 4 Methodology
### Indoor Air Quality Dataset  
In order to validate the proposed distributed data mining approach, we look at the GAMS indoor air quality (IAQ) dataset, which is publicly available in [37]. This dataset was used for experimenting and benchmarking in several research works [38; 39; 40]. In this work, we use the GAMS dataset to compare the distributed data mining approach with traditional centralized data mining.  
As reported in Table 1, the GAMS dataset includes observations from six IAQ indicators. Observations were collected from 21 November 2016 to 28 March 2017 on a minute basis. From further dataset inspection, we identified that, for benchmarking, it could be interesting to estimate the CO\({}_{2}\) level from T, RH, and VOC.  
The data were pre-processed by averaging observations on a six-minute interval. Table 2 summarizes the statistics of the pre-processed GAMS dataset.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Feature** & **Sample Value** & **Unit** \\ \hline Carbon dioxide (CO\({}_{2}\)) & 708.0 & ppm \\ Relative humidity (RH) & 40.09 & \% \\ Particulate matter 2.5 micron (PM\({}_{2.5}\)) & 10.2 & \(\upmu\)g/m\({}^{3}\) \\ Particulate matter 10 micron (PM\({}_{10}\)) & 9.0 & \(\upmu\)g/m\({}^{3}\) \\ Temperature (T) & 20.83 & Celsius \\ Volatile organic compounds (VOC) & 0.093 & ppm \\ \hline \hline \end{tabular}
\end{table}
Table 1: The IAQ indicators of the GAMS dataset.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **CO\({}_{2}\)** & **T** & **RH** & **VOC** \\ \hline mean & 712.1 & 23.00 & 28.40 & 0.1168 \\ std & 405.61 & 2.08 & 5.46 & 0.086 \\ min & 370.2 & 18.04 & 22.05 & 0.062 \\
25\% & 431.70 & 21.43 & 34.70 & 0.0635 \\
50\% & 494.0 & 22.89 & 38.30 & 0.0740 \\
75\% & 900.1 & 24.73 & 42.05 & 0.1405 \\ max & 2603.0 & 27.95 & 72.09 & 0.8702 \\ \hline
# Using Virtual Soundwalk Approach for Assessing Sound Art Soundscape Interventions in Public Spaces
## Materials and Methods
### Field Recordings and Measurements  
The on-site collection of the data on the acoustic environment consisted of environmental noise measurements, three-dimensional audio recordings and panoramic pictures, intended for laboratory use, as recommended by the ISO/TS 12913-3: 2019 and applied in recent soundscape studies [71; 72; 39; 73]. The soundscape assessments were made in laboratory conditions. Quantitative analysis was performed using the ArtemiS psychoacoustic analysis software, while the qualitative one was based on listening experiments in Auralization laboratory conducted using the questionnaire based on the SSQP (Appendix B) and the virtual soundwalk approach. The semantic differential method was chosen due to the built-in relation with the component model for soundscape quality prediction based on connection between types of sound sources and soundscape perception descriptors by [PERSON] et al. [74]. A series of rank-based Kruskal-Wallis tests was performed as the non-parametric equivalent of one-way ANOVAon the results of the subjective responses to determine the statistical significance of the subjective responses, looking at the difference between the 'baseline' and 'intervention' conditions.  
Field recordings and measurements were conducted as a part of the monitoring of the selected urban open spaces within the framework of a wider research project. The recordings used for the research in the Auralization laboratory were made in the early afternoons as reported in the Table 1. Weather conditions at the time of the recordings were within the following span: 10 degrees Celsius, wind 6 km/h, partially cloudy; 20 degrees Celsius, wind 9 km/h and sunny.  
Ten-minute intervals were recorded in order to capture usual sonic event circles (containing several road traffic circle exchanges) [33]. All analyzed sound sources produced sounds characterized by different temporal alterations: (1) Sea Organ were continuous during the whole recording due to the consistent wind speed; (2) Time Piece Graz repeats itself hourly, lasting for five minutes; (3) the echo effect in Maksimir depends entirely on impulse sounds generated within the pavilion. Five-minutes-and-15-seconds-long excerpts were selected from each recording as the only predefined factor was the designed length of the sound art piece in Graz (five minutes).
## 2 Materials and Methods
### Model Runs  
The STICS model was initialized by using the abovementioned input variables. Two types of model runs were performed: control-runs vs. heatwave-runs. The control-run is based on observed conditions, using the observational unmodified weather data. Conversely, for the heatwave-runs, the observed weather conditions were modified, generating heatwave pulses at specific periods of the year and with specific durations (synthetic heatwave disturbances). Therefore, for each year, a heatwave pulse was added to the temperature time series by increasing the daily maximum temperature by 5 \({}^{\circ}\)C with respect to the climatological maximum (following the WMO definition). This change in daily maximum temperature also impacts daily mean temperature. Regarding the onsets of the heatwaves, they were generated with a 15-day leap period, starting on July and ending in September: 1 and 15 July; 1 and 15 August; and 1 and 15 September. Therefore, the most vulnerable period of grapevine exposition to heatwaves is covered. The heatwave then lasted for a period ranging from 5 to 9 days (heatwave length/duration). Only one heatwave per year was considered. During the heatwave, the daily precipitation was set to 0 mm, which is typical during a heatwave. A comprehensive chart of these synthetic heatwaves is shown in Figure 2.  
STICS performance under the recent-past conditions has previously been evaluated and revealed good agreement with field data [35]. Thus, the remaining model parameterization followed the previous study [35]. Hence, model runs were carried out for each of the 18 regions, year (30 years; 1986-2015) and type of run (control vs. heatwave), corresponding to a total of 6480 simulations. In order to evaluate the potential impacts of heatwaves on grapevine yields, the heatwave-runs and the control-runs where compared. More specifically, the relative differences (%) in their corresponding yields were analyzed, thus disregarding absolute values and restraining the effects of some uncertainties in the STICS simulations. Nonetheless, it is important to state that the STICS model already showed a high agreement with statistical yields over Europe [35].
# Data Fusion Analysis and Synthesis Framework for Improving Disaster Situation Awareness
## 3 Problem Statement and Approach  
Based on the literature study, we observe that the current research proposals on enhancing disaster situation awareness have one or more of the following shortcomings:  
* There is a lack of a model-based framework where, with the help of model management tools, a large category of data sources and geographical elements can be defined.
* A domain model of data sources for earthquake detection is missing.
* There is a lack of analysis tools to evaluate various prospective data source fusion alternatives for the purpose of achieving higher effectiveness.
* A toolset for automated synthesis is lacking, which can help in finding out the best data fusion alternative for a given set of constraints.
# Generation and Distribution of Quantum Oblivious Keys for Secure Multiparty Computation
## 3 Results and Discussion
### Security
Finally, we point out that the OT extension algorithms that are used during SMC often rely only on collision resistant hash functions [37] anyway. If those protocols are used to extend the base OTs produced by \(\pi_{HOK}\), we can effectively speed up the OT rates without introducing any additional computational complexity assumption.
# Multi-Device Security Application for Unmanned Surface and Aerial Systems
## 4 Results
### Experiment Three--MAUI ROS Live
#### 4.3.2 Testing and Results  
During this phase, the first metric of interest was to integrate the CASSMIR, the ScanEagle, and the ground control and share UxS data. We successfully used MLS to securely exchange data between the ScanEagle and CASSMIR. The ScanEagle UAV shared its odometer data with the CASSMIR USV which displayed the UAV positional data on the CASSMIR terminal. The CASSMIR USV shared its GPS data, also displaying the USV positional data on the ScanEagle terminal.  
For our second metric of interest, the command and control testing of the ScanEagle was also a success. The ScanEagle received an MLS encrypted turn-rate message from the ground control VM, decrypted the message, and then published this command to the appropriate ROS topic. The ground control commands were sent while simultaneously sharing data with the CASSMIR USV using MLS over an ad-hoc wireless mesh radio network.  
For our final metric of interest, we repeated our second metric test while running the testing mode of our program to calculate setup times and the number of MLS updates sent. We attempted to conduct a total of five MLS key updates within a single transmission of 1000 messages during this test. However, our implementation's lack of concurrency made it difficult for ground control to join the MLS group while updates were taking place. To solve this issue, we artificially reduced the speed at which the application processed messages on the ScanEagle specifically, as the group manager, putting the odometer data sending process to sleep on the ScanEagle before processing data for encryption and sending, resulting in a ScanEagle send rate of two messages per second, and receive rate of eight messages per second. Thus, as the group manager, the ScanEagle processed a total of 10 messages per second. This new setting allowed enough time to add ground control to the MLS group before the key updates started. We were then able to successfully process five group key updates while transmitting 1000 messages while ScanEagle and CASSMIR shared data, and the ScanEagle received commands from the ground station. However, it is important to note that if we had implemented a Delivery Service or concurrency controls (data processing sequence scheme) there would not have been a need to put the process to sleep, and therefore, we have been able to fully utilize the available computational capabilities and network. Nonetheless, with this testbed workaround, it is notable that MLS ran successfully on the multi-UxS testbed.
# A Double Rate Localization Algorithm with One Anchor for Multi-Hop Underwater Acoustic Networks
## 5 Experiment Results
### Ranging Experiment
#### 5.2.4 The Localization Experiment of MDS-MAP  
With the same network system, we test the MDS-MAP approach based on the real data. The experimental result and localization error are exhibited in Figure 19 and Table 7 respectively.  
It is observed that, similar to the simulation results, the proposed DRL algorithm obtained a much smaller mean error than the classic MDS-MAP algorithm on the real test environment.
# Research on Multi-Mode Braking Energy Recovery Control Strategy for Battery Electric Vehicles
## 2 Brake Energy Recovery Control Strategy
### Conventional Brake Energy Recovery Control Strategy
#### 2.3.2 Power Distribution Strategy for Fuzzy Controlled Motorized Mechanisms in Conventional Mode  
The braking energy recovery allocation ratio is related to the braking intensity z, vehicle initial speed v, and HV battery's SOC value. To ensure the safety and stability of vehicle braking, the braking energy recovery allocation ratio should be gradually reduced with a gradual increase in vehicle speed. When the battery SOC reaches the threshold value of 90%, the braking energy recovery function is turned off to prevent overcharging, and the braking intensity is proportional to the braking energy recovery allocation ratio.  
Among them, the domain of the input variable brake strength is [0, 1], the genus function S is selected as the ZMF (Z Membership Function) type, M is selected as the GAUSSMF (Gaussian Membership Function) type, L is selected as the SMF (S Membership Function) type, and the distribution is shown in Figure 5a. The domain of the battery's SOC is [0, 1], the genus function S (Small) is chosen as the ZMF type, M (Medium) is chosen as the GAUSSMF type, L (Large) is chosen as the SMF type, and the distribution is shown in Figure 5b. The speed domain is [0, 120], the genus function M is selected as the GAUSSMF type, the genus function H is selected as the SMF type, and the distribution is shown in Figure 5c. The domain of the output variable motorized power ratio k is [0, 1], and the affiliation function VS (Very small) is selected as the ZMF type. S, M, and L are selected as GAUSSMF types. VL (Very large) is chosen as the SMF type; the distribution is shown in Figure 5d.  
Figure 5: The plot of fuzzy control affiliation function: (**a**) brake strength affiliation function; (**b**) battery SOC relative function; (**c**) velocity dependence function; (**d**) motor braking proportional affiliation function.  
The fuzzy controller has three input variables, braking strength z, battery power SOC, and vehicle speed v; and one output variable, the proportion of motorized power k. The fuzzy subsets of the input variable, braking strength z, are defined as [S, M, L], which stand for {small, medium, and large}. The fuzzy defined subsets of the battery power SOC are defined as {S, M, L}, which stand for {low, medium, and high power}, respectively; The fuzzy subset of vehicle speed v is defined as [L, M, H], which represents {low, medium, high}; the output variable is the power distribution coefficient k of the motor mechanism, and the fuzzy subset is defined as {VS, S, M, L, VL}, which represents {very small, small, medium, large, and very large}. A total of 27 fuzzy rules are formulated, as shown in Table 2.
## 3 Results and Discussion
### Far-Field Results
In spite of such impairments, it can be noted that radiation patterns are still good enough for practical use.
# Safety Evaluation Method and Management Strategy for Aviation Flight Plans
## 1 Introduction  
According to International Air Transport Association forecasts, global passenger traffic will increase to 5.6 billion in 2030 [1]. However, global passenger traffic has declined over the past 2 years due to the impact of the recent COVID-19 pandemic. In the global aviation industry, 2.3 billion passengers were recorded in 2021, which is 49% of pre-pandemic (2019) levels. This value implies that there has been a recovery from the 60% drop in 2020, and the overall speed of this recovery is optimistic. The growing demand for air transport will lead to a continued increase in air traffic, resulting in more congestion of air routes. Flight delays have become serious, increasing the risk of aircraft conflict and threatening flight safety. The contradiction between the increasing air traffic flow and the limited amount of airspace resources is an urgent problem in the civil aviation industry that must be solved.  
Safety and efficiency are the core necessities of the air transportation system. Addressing aircraft conflict is the responsibility of air traffic controllers, who monitor planes' flight paths in real time, anticipate potential conflicts, and issue commands for their regulation. This tactical security scheme is implemented in real time. With the increase in air traffic flow, this scheme brings huge work pressure to air traffic controllers, and air traffic safety becomes difficult to effectively guarantee. Flight planning is an important part of aviation security. A reasonable flight plan can avoid the occurrence of conflicts to the greatest extent before the aircraft takes off, especially in the context of the four-dimensional-tracking-based operation that is proposed for next-generation air transport systems, in which the location and time of passing waypoints are predefined.
## 3 Resource Scheduling and Collision Resolution in Sidelink Mode 4
### Selection  
Suppose a UE \(V\) is at subframe \(n\). For a BSM transmission, \(V\) needs to reserve new subchannel(s) whose number depends on the packet size at a subframe in \([n+T1,n+T2]\), where \((T1\leq 4)\) and \(T2\,(20\leq T2\leq 100)\)[21]. The interval between \(n+T1\) and \(n+T2\) is called the _selection window_ (Figure 1). In this window, candidate single-subframe resources (CSRs) are selected after filtering out two unusable groups of resources. First, for the single-subframe resource \(m\) in the selection window, a UE marks it unavailable if \(V\) was transmitting in subframes \(m-RRI\cdot j\ \ (j\geq 1)\) in the sensing window. This is because the subframes at which \(V\) transmitted its packet could not be sensed by \(V\) due to half-duplex transmission. Because there might be other UEs that transmitted in these subframes, the subframe \(m\) is excluded to avoid possible collisions with these UEs. Second, we exclude the resources that will probably be used by other UEs. These are the single-subframe resources \(m^{\prime}\) in the same subchannel(s) where \(m^{\prime}-RRI\cdot j\ \ (j\geq 1)\) were used. Whether a resource was used or not is determined by a reference signal received power (RSRP) sensed higher than the threshold corresponding to the priority of \(V\)'s packet to be transmitted. The priority is provided by the higher layers [21; 36]. After excluding these two groups of resources, the remaining resources are called the set \(S_{A}\).  
In case the single-subframe resources in \(S_{A}\) is less than 20% of the entire selection window, then more candidate resources should be identified. For this, we raise the RSRP threshold by 3 dB, and repeat the filtering process for the second group. If \(S_{A}\) becomes larger than 20%, then we choose those with the smallest RSSI values, which we call \(S_{B}\). We report \(S_{B}\) to the higher layer that randomly selects one of them for the first transmission. The number of times that the subchannel(s) for the selected resource is used without the selection process is also randomly selected and is called SL_RESELECTION_COUNTER [20], or simply RC counter in this paper.
## 2 Previous Studies
### Waste Collection  
Past studies on waste collection delved into several approaches, namely, \"Smart bin system\", \"Route optimization\", \"Dynamic scheduling\", and \"Demand prediction\". Previous smart bin systems focused on household waste management systems using IoT [7], proposing waste removal systems for communities [8], identifying the fullness of litter bins, collecting data like litter bin utilization information [9], and so on. One of the previous studies on route optimization recommended optimal routes for waste collection potential and fuel cost [10], real-time optimization of collection routes considering traffic conditions [11], and cost-effective designs to enhance waste collection operations in specific areas [12]. Studies on dynamic scheduling covered AI applications in optimizing waste  
Figure 1: Results of the fire incident investigation. (**a**) Specific locations of fires and other incidents caused by secondary batteries; (**b**) items causing fires and other incidents related to secondary batteries.  
collection schedules based on residents' demand [13], and demand prediction studies ventured into forecasting waste generation rates [14].
## 3 DWS Measurement Error Suppression Strategy
### Design of Imaging System Parameters  
To achieve effective correction of the beam walk while minimizing the introduction of additional sources of error, starting from geometrical optics, employing the method of conjugate imaging can ensure object-image conjugation. Theoretically, this approach can avoid introducing additional aberrations, and for ideal optical systems, it can achieve symmetrical imaging while keeping the angular characteristics of beam jitter unchanged.  
As shown in Figure 7, this design utilizes two plano-convex lenses arranged in parallel with a separation of twice the focal length. The exit pupil and entrance pupil are positioned at the focal points of the front and rear lenses of the imaging system, respectively.  
In actual experiments, we employed the LA1433-C plano-convex lens (Thorlabs, Newton, NJ, USA) produced by Thorlabs, with a size of 1 inch and front and rear focal lengths of 149.5 mm and 147.5 mm, respectively. The anti-reflection coating wavelength range is 1050-1700 nm, making it effective for the subsequent experiments utilizing a 1064 nm laser.  
Simultaneously, we utilized a combination design of high-precision telescoping sleeves and clamp wrenches to ensure precise positioning of the optical elements. The telescoping sleeve allows for a travel range of 4 mm, with high-precision control of 0.5 mm per rotation, and prevents rotation of the optical elements during movement. During the optical path alignment process, it is consistently required to orient the convex surface towards the direction of beam incidence, as refraction through both surfaces ensures the outgoing beam achieves optimal focusing.  
Figure 6: Correlation between propagation distance and angle measurement precision. Note: Blue dots represent the magnitude of angle measurement errors that fall within the Taiji program’s acceptable linearity range across various propagation distances, while the orange line depicts the trend through linear fitting.