esa-sceva/satcom-chunk-collection huggingface dataset:
# Cluster-Based Data Aggregation in Flying Sensor Networks Enabled Internet of Things
## 3 Flying Sensor Network Cluster Optimization  
In cluster-based flying networks, the selection of CHs and cluster formation requires special attention to decrease the re-affiliation rate and save FSNet resources [33]. To select CHs, essential parameters such as the remaining energy, mobility, and flying nodes degree are considered to obtain optimal clusters [34; 35; 36]. These parameters are optimized to distribute load among clusters. We use the clustering approach to balance and select CHs in accordance with [37], as shown in Figure 1. Data aggregation is initiated once clusters are formed.  
As shown in Figure 1, the proposed approach begins with the selection of a CH-UAV. The selection process determines which UAV will act as the CH. If the node is the CH, it proceeds to the next step. Then, the CH-UAV broadcasts the time division multiple access (TDMA) schedules to all member nodes within its cluster. TDMA is a channel access method that allows multiple nodes to share the same communication channel by dividing it into different time slots. The CH-UAV receives data from all neighboring nodes within its cluster. Data aggregation is the process of combining or summarizing data from multiple sources. The CH-UAV performs data aggregation on the received data, reducing  
Figure 1: Flowchart of proposed system.  
redundancy and improving efficiency. After data aggregation, the CH-UAV sends the aggregated data to the BS for further processing. If the current node is not CH, it proceeds to the next step. The non-CH node waits for the TDMA schedule broadcast by the CH-UAV. This schedule determines when the node can transmit or receive data. The algorithm checks the energy level of nodes. If the energy is still available, it returns to the CH selection step, indicating that the next round of the process will begin. When the node's energy reaches zero or depletes, the flowchart terminates, and the process ends.  
In cluster-based routing for flying sensor networks, CHs may receive multiple copies of the same dataset from different sensors located in the vicinity. The communication of data requires more resources as compared to computation. Hence, a method is required to discard duplicated packets when sending data to the base station at the cluster level. Thus, network resources such as batteries and bandwidth can be utilized for other purposes. The network lifetime will increase. Our proposed algorithm works in two phases: cluster setup and data aggregation.
# A Novel Technosol Formulation for Sustainable Landfill Top Covers Using Non-Hazardous Wastes
###### Abstract  
This study explores the potential of non-hazardous wastes for crafting an engineered soil-like material (Technosol) suitable for landfill capping applications. Three distinct materials--waste foundry sand (WFS), washing aggregate sludge (WAS), and composted biosolids (CBS)--were strategically combined to develop this innovative Technosol. The formulation process involved a comprehensive analysis of their physical-chemical properties, mineral composition, leachate quality, and a series of geotechnical assessments to ensure compliance with landfill top cover construction standards. The blend 90 WFS/10 WAS showed optimal geotechnical properties for constructing a protective layer, including maximum dry density (1.77 g cm\({}^{-3}\)), void ratio (0.4), CBR index (23.2), cohesive strength (40 kPa), internal friction (\(\phi\) = 30\({}^{\circ}\)), and permeability coefficient (k = 1.48 \(\times\) 10\({}^{-6}\) cm s\({}^{-1}\)). Further enhancement was achieved by adding 10% CBS, resulting in the development of a functional organo-mineral topsoil horizon (81 WFS/9 WAS/10 CBS). Importantly, leachate analysis confirmed the negligible environmental footprint of this Technosol. Moreover, a pot-based experiment with _Brussica junce_ planting validated its capacity to support plant growth and establish a vegetative cover on the landfill surface.  
landfill closure; soil top cover; Technosol; waste management; circular economy; geotechnical testing
# Adaptive Solar Power Forecasting based on Machine Learning Methods
## 1 Introduction
However, the least angular regression (LARS) [24], which is particularly suitable for situations where the feature dimension is higher than the sample number, and during the calculation it keeps all variables selected into the regression model, and provides a simple structure for computing. We therefore choose LARS to select the most important factors in each group. In fact, the predicting methods with only key factors are not sufficient to have very accurate results [22], because the environment is highly complicated. Therefore, an adaptive revising method on the original forecasting is highly desirable. Motivated by this, we propose an adaptive solar power forecasting (ASPF) method in this paper, which can revise the original forecastings adaptively according to the historical forecasting errors and the weather data. It combines data clustering, variable selection and artificial neural network (ANN) to achieve highly accurate revision on PV power predictions by learning the weather data and day-ahead forecasting errors intelligently and adaptively. The function of the ASPF is shown in Figure 1. Through ANN, ASPF learns the characteristics of forecasting errors between the predicted and actual PV power. By clustering the data, the capture of the forecasting characteristics could be highly improved. In addition, ASPF classifies the predicted day into similar day or non-similar (defined in Section 2), based on which the original forecastings are revised accordingly. The details are shown in Section 2. Also, ANN is adaptively upgraded so that ASPF improves as more data collected. Please note that ASPF actually captures the characteristics of forecasting errors and revises the predictions accordingly, and thus it does not require any specific original forecasting method, which can be a machine learning method or a statistical one. This makes ASPF a quite general method which can be used in many cases. Figure 1: The function of ASPF. The remainder of this paper is organized as follows. We present the principle of ASPF and its framework in Section 2. The algorithms to realize ASPF are proposed in Section 3. The adaptive solar power forecasting is presented in Section 4. We perform the simulation studies in Section 5. Section 6 concludes this paper.
## 2 Related Work
### Indoor Real-Time Location System
Figure 2: Comparison of major real-time location system (RTLS) technologies (where the size of each circle reflects how widely the technology has been adopted, the vertical axis indicates the range of accuracy, and the horizontal axis indicates the ease of implementation). For buildings, such as the floor of the building shown in Figure 3, Bluetooth sensors can be placed in multiple locations on each floor, with a layout that depends on the room layout for each floor, as a single Bluetooth sensor will generally cover an area with a radius of 5-10 m. The Bluetooth sensors are the size of a coin and cost about US$12 each, can be used for 6-12 months before the batteries need to be replaced, and are small enough that they will not have an obvious or unappealing appearance. Batteries should be checked at six-month intervals to ensure that they are working properly. A mobile phone can be used to establish a communication link between a Bluetooth-enabled device and the surrounding sensors, and the location of the smartphone can be determined using the received signal strength (RSS) method.
## 4 Results
### Operations and Approaches
#### 4.2.3 Implementation Issues and Functional Requirements  
The implementation of the system involves addressing various use cases and functional requirements that dictate the primary operations the system must support to respond to different scenarios. These requirements are as follows:  
1. Sending charging requests to the CSCC. The CS must send the charging request to the CSCC (only in UC1). After the user has specified their user preferences (Ups), the CS sends a charging request (CR) to the CSCC. A charging request (CR) consists of the following data: * xRef: the desired level of charge. * S: the start time. * E: the preferred end time. * \(\Delta\)P: the maximum allowed power consumption by the CS during charging (depending on the selected plug). * X(S): the initial level of charge.
2. Obtaining the current battery charge level. The charging station (CS) must send the current battery charge level to the CSCC at each sampling interval (all UC). When an event triggers the CSCC, it needs to access the up-to-date charge levels of the electric vehicles (EVs) that are currently being charged. In the detailed approach, this information must be collected at every sampling point.
3. Implementing the control signal. The CS needs to implement the control signal received from the CSCC (all UC). The control signal C[k] is a sequence defined in discrete time, where at each time step k, it can take the following values: * c > 0: if the electric vehicle (EV) needs to draw power c\(\cdot\Delta\)P during the k-th sampling interval. * 0: for standby mode during the k-th sampling interval. * s < 0: if the EV needs to return power s\(\cdot\Delta\)P during the k-th sampling interval.  
The control is applied at the next possible sampling interval.
4. Storing charging requests. The CSCC is responsible for storing CRs (UC1 and UC2). Whenever the CSCC receives a CR, it must save the request in its database. This stored data will later be used by the CSCC to formulate the MPC problem.
5. Calculating control signals. The CSCC must generate control signals based on the information it has (all UC). Every time the CSCC is activated by a CR, it must calculate  
Figure 3: Use case 1: UML sequence diagram (refined approach).  
and update the control signals for all electric vehicles (EVs) currently charging. The CSCC performs a near-real-time optimization using the stored data. The result of this optimization is the optimal control signal values C[k]. In the refined method, this calculation occurs at each secondary sampling interval.
6. transmitting control signals. Once the optimization problem is solved, the CSCC sends the control signals to the CSs (all UC). Whenever new control signals are generated, the CSCC promptly sends them to the CSs.
# Statistical Modeling of Cultural Differences in Adopting Autonomous Vehicles
## 2 Models for Public Opinion about AVs  
### Background  
#### 2.1.1 Public Opinion on AVs  
To develop a statistical model for adapting AVs, we collected literature on public opinion, especially concerns about AVs and willingness to pay for them. Since the purpose of this paper is to investigate the influence of cultural differences, we first investigated the literature that was surveyed in various countries. A number of studies have confirmed public opinion through online surveys [14; 15; 16; 18; 20]. However, those papers were based on surveys conducted within a city or a country, and direct comparison was difficult because the scale and questions of the survey were different. In contrast, [PERSON] et al. [17] and [PERSON] and [PERSON] [19] were representative papers that surveyed drivers from various countries. These surveys contained various questions including \"Concerns about AVs\" and \"Willingness to pay for AVs\" (WTP). Concerns about AVs included safety, legal liability, security, and data privacy concerns. Furthermore, Willingness to pay indicated how much additional cost one was willing to pay for the purchase of an automated vehicle.  
In [PERSON] et al. [17], 5000 drivers from 109 countries responded to a questionnaire about public opinion on AVs. In particular, the authors examined cross national differences in public opinion. However, the differences were the result of economic differences among countries, and differences according to cultural values were not examined.  
Next, [PERSON] and [PERSON] [19] surveyed public opinion on AVs in China, India, Japan, the U.S., the U.K., and Australia. Respondents to the survey also responded to questions on concerns about AVs and WTP. Issues and degrees of concern differed from country to country. Willingness to pay for AVs was also different. Most respondents in China and India said they would pay extra money for AVs, while less than half of the respondents in the remaining countries said they would pay extra money. However, they did not provide an explanation of which factors caused the cross national differences.
## 4 Sensors Calibration Tests
### MS Calibration  
To calibrate the MS sensor, only the normal set of the cap/aluminum cylindrical specimen was used due to the sensor shape and gauge placement, as shown in Figure 11a. The indirect effect of the confinement pressure on the sensor (called mechanical effect) was measured by using a solid aluminum sample. Only the outer side of the system was subjected to the confining pressure, as illustrated in Figure 11b. The experimental tests details are gathered in Table 4.  
Figure 12 depicts the results of the calibration tests where the applied pressure is plotted versus the strain sensor in its central part. The experimental results were also compared with the numerical simulations shown before. First, we observed that the effect of the confining pressure caused a negative strain in the sensor, which started from the beginning pressure of the test being dissimilar to the numerical simulation curves. This means that the sealing joint filled the gap between the sensor  
\begin{table}
\begin{tabular}{l c c c c} \hline
**Test Name** & **Purpose** & **Position** & **p (MPa)** & **Slope K (GPa)** \\ \hline MS-calib & Calibration & normal & 200 & \(K_{p}=73.5\) \\ MS-mech & Mechanical effect & normal & 200 & \(K_{d}=-573.1\) \\ \hline \end{tabular}
\end{table}
Table 4: Description of the MS calibration test condition, pressure applied and curve slope value.  
Figure 10: Results of the hydrostatic calibration tests on the HS: \(p\) vs. \(\epsilon_{v}>0\) in compression.
# Trends in Performance Limits of the HOT Infrared Photodetectors
###### Abstract  
The cryogenic cooling of infrared (IR) photon detectors optimized for the mid- (MWIR, 3-5 \(\upmu\)m) and long wavelength (LWIR, 8-14 \(\upmu\)m) range is required to reach high performance. This is a major obstacle for more extensive use of IR technology. Focal plane arrays (FPAs) based on thermal detectors are presently used in staring thermal imagers operating at room temperature. However, their performance is modest; thermal detectors exhibit slow response, and the multispectral detection is difficult to reach. Initial efforts to develop high operating temperature (HOT) photodetectors were focused on HgCdTe photoconductors and photoelectromagnetic detectors. The technological efforts have been lately directed on advanced heterojunction photovoltaic HgCdTe detectors. This paper presents the several approaches to increase the photon-detectors room-temperature performance. Various kinds of materials are considered: HgCdTe, type-II A\({}^{\text{III}}\)B\({}^{\text{V}}\) superlattices, two-dimensional materials and colloidal quantum dots.  
HOT IR detectors; HgCdTe; P:-i-N; BLIP condition; 2D material photodetectors; colloidal quantum dot photodetectors +
Footnote †: journal: Applied Physics Letters
## 5 Recognition of Biologically Active Compounds
### Behavioral Experiments
#### 5.3.2 Two-Path Olfactometry  
The following method is among the most popular configurations in olfactometry: two independent reservoirs, one containing the VOC source and the other containing the control, are connected to a convergent point by employing an accessory in a T or Y shape using the two closest entrances to each other, and the insect is placed in the remaining point and then allowed to make a choice. One individual is deployed at time, and the path of its election (VOC source or control) is recorded; this operation is repeated several times to achieve statistically significant data, and this is used for assessing both attractive and repulsive effects of VOCs on insects. This approach has been employed in the identification of either attractive or repulsive VOCs from certain plants to the coffee berry borer (_Hypothenemus hampei_) [163] and for evaluation of the influence of VOCs emitted by plants in different states on aphids [72], pollinators [111] or parasitoids [113]. Additionally, this has been used in the study of the effect of plant-derived VOCs on female _Grapholita molesta_ behavior [164].  
Apart from olfactory using a T- or Y-shaped apparatus, there are some other two-path structures that have been referenced in the literature that also involve the placement of test insects in a center point equidistant to both control and VOC-containing reservoirs. One such configuration separates the insect from the reservoirs by using conduits, and the vapors from each reservoir are pumped to the insect placement point. This has been cited in insect attraction assays regarding whole plants [68; 165] or fruits [42], among others. Another interesting experiment with a nonconventional configuration is described in Ref. [166], in which the VOC reservoirs were underground sources, specifically pots containing soil that were planted with _Picea abies_ L., and their influence on the attraction of _Otiorhuchus sulatus_ larvae was evaluated.  
Another method is well olfactometry, in which the insect is placed over a platform that has two wells, and again, one well houses the VOC-emitting source and the other one houses the control. In this assay, the insect is released and then allowed to walk on the platform, and whether the insect approaches or moves away from a well is observed. It must be considered that in this assay, the VOC diffusion is passive, i.e., no pump is used for transporting the VOC to the insect. In this olfactometry method, the well can be below the platform level [67] or at the same height [39].
## 2 Materials and Methods
### Processing  
The CST Microwave Studio was used to simulate the microwave properties. The sizes and material parameters for simulations are in agreement with the experimental values. The metamaterials absorber was designed by simulation through adjusting the structure, size, and materials parameters.  
A calcium titanate sample was synthesized by the solid-state reaction method. The mixer of stoichiometric quantities of calcium carbonate and titanium dioxide were put into a polymeric amide bottle to ball-mill for 10 h with ethanol and zirconium oxide balls. Then, an oven was used to dry the obtained slurry at 120 \({}^{\circ}\)C, followed by calcining at 1150 \({}^{\circ}\)C for 120 min in an alumina crucible. After ball-milling a second time, the obtained powders were pressed into dimensions of 30 mm \(\times\) 20 mm \(\times\) 3 mm under a pressure of 5 MPa. A muffle furnace (KSL-1700X, KMT, China) was used to sinter the compacted powders at 1350 \({}^{\circ}\)C for 2 h. The permittivity of the obtained ceramics was 150 + 0.002 i. To prepare dielectric particles, the ceramics were cut into 2.50 mm cubes which were stick to the center of the crossbar.  
The proportions of the concrete are demonstrated in Table 1. The fresh mortar was cast into 150 mm \(\times\) 150 mm \(\times\) 150 mm plate molds, flattened, and compacted on a vibrating table for 10 s. Then the metamaterials absorber was put into the center of the concrete; after standing for 24 h, the molds were removed, and the samples were put into a curing room for 28 days of standard curing. Finally, the obtained concrete was cut into 125 mm \(\times\) 125 mm \(\times\) 125 mm to manufacture the designed EMW absorbing concrete sample, as shown in Figure 1.  
Figure 1: Wave-absorbing concrete samples: (**a**) unit cell of the metamaterial absorber sample; (**b**) optical image of EMW absorbing concrete sample.
# A Multi-Channel Uncertainty-Aware Multi-Resolution Network for MR to CT Synthesis
## 2 Methods
### Experiments  
We quantitatively and qualitatively compare the synthesis results of the MultiRes network trained with \(T_{1}\)- and \(T_{2}\)-weighted MR input images against three baselines: HighRes3 DNet trained with patches of size \(96\times 96\times 96\), Deep Boosted Regression (DBR) trained with patches of size \(80\times 80\times 80\) and U-Net trained with 3D, \(160\times 160\times 160\), patches with batch size one. An additional four convolutional layers with kernel size three were added prior to the final \(1\times 1\times 1\) convolutional layer in the standard U-Net architecture as this was found to increase stability during training. All models were trained on the same 22 images while the remaining 10 images were equally split into validation and testing data. We further compare against the results that we reported in our preliminary work, where MultiRes could only be trained with single-channel \(T_{1}\)-weighted input images.  
We perform a similar experiment on the brain dataset comparing to an additional baselines, a multi-atlas propagation method, in order to evaluate the model's extrapolation properties to other parts of the body. Similar to the whole-body dataset 30% of the brain data was excluded from training and split into validation and testing data.
## 5 Subjective QoE Evaluation of Cloud Gaming
### Gaming Platform and User Profile  
Following careful consideration of several gaming platforms, it was determined that the Parsec streaming platform would serve as the application for the subjective experiments. The choice was to either use the Steam game platform or Parsec out of several other options. We chose Parsec because it enabled streaming via LAN connections, which was necessary for a controlled network environment without uncontrolled network degradation over the internet. Secondly, it is extremely popular in the gaming community, due to the proprietary Better-UDP (BUD) networking protocol utilised by Parsec [36]. BUD combines the dependability of TCP with the low latency of UDP to give the user one of the best possible remote gaming experiences. It also provides control over a number of important streaming KQIs', including frame rate, bandwidth, video encoding, and resolution.  
Subjective assessment included 24 human individuals, 6 females and 18 males. The tests were conducted in a laboratory designed specifically for QoE trials in accordance with BT500 and P809 specifications [37; 38]. The Parsec application was installed on both the server and client PCs via the emulator PC's LAN connection. Before the QoE experiment, each subject played all three games for 10 min to familiarise themselves with the controls and ideal conditions. All the subjects had experience of playing PC games before, but played less than 7 h a week, so would be classified as recreational gamers and not as experienced gamers, according to the definition in a report by Limelight Networks [39].  
As indicated previously, each game featured fourteen scenarios, each lasting 45 s. After each scenario, participants were asked to score video-QoE and game-QoE on a scale from 1 to 7, with 1 representing the worst experience and 7 representing the best. As indicated by ITU 809, the ranking was based on a 7-scale ACR. This allowed for greater versatility and representation of user experience than a 5-point scale. The participants also had the option to provide a reasoning for each rating, describing why they selected a certain value for video and game QoE. The subject required around 90 min to complete the test, with pauses after every scenario to lessen the impact of fatigue on the QoE rating.