esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Description of Instances  
For the computational experimentation, 25 instances were created using real data from Montevideo. Various sources of information were utilized to build these instances. Details about the route of bus lines, the schedules, and the location of bus stops were obtained from the National Open Catalog of Uruguay. The information about transfers was provided by the City Hall of Montevideo and was processed using the urban data analysis methodology proposed in [21]. The planning horizon was set to the rush hour at midday in Montevideo, which is from 12:00 to 14:00 [3].  
To construct the instances, the demand function was derived from the information of the smart cards ticket sales' database. The transfer zones were selected based on demand, i.e., pairs of bus stops with the highest number of registered transfers during the specified period. Then, the considered bus lines are those that pass through these synchronization points. Each instance included information from 30, 70, and 100 transfer zones, chosen randomly from the 170 most demanded transfer zones in the city. The time traveling function, denoted as \(TT\), for each bus line was empirically computed using GPS data. The walking time between bus stops was estimated by considering a walking speed of 6 km/h and the distance between bus stops within each transfer zone, which was obtained using geospatial information about the stops.  
For each instance, different levels of Quality of Service (QoS) provided to the citizens were used. The different levels of QoS were modeled with different values of the parameter \(Tol_{b}^{\mathit{\text{\emph{lg}}}}\), which represents the maximum threshold time that users are willing to wait for a transfer from bus line \(h\in H\) to bus line \(g\in H\). This parameter was set as \(\lambda Hd_{g}^{\mathit{max}}\), where \(\lambda\) took values from the range [0.5, 0.7, 0.9, 1] and \(Hd_{h}^{\mathit{\text{\emph{max}}}}\) is the maximum bound for the headway of the bus line \(g\in H\). This allowed for the consideration of different thresholds of user tolerance regarding the time they are willing to wait. The problem instances were named in the format NL\(\lambda\).id, where \(NL\) represented the number of bus lines, \(\lambda\) was the percentage coefficient applied to the waiting time of the line (representing different thresholds of tolerance), and \(id\) is an identifier for differentiating instances with the same \(NL\) and \(\lambda\).
## 4 Implementation  
A simulation environment was created to demonstrate the proposed solution using Paragon Virtual ATM-XFS Simulation and ATM Testing Solution. Client software was developed using C# to demonstrate the ATM, utilizing Visual Studio 2019 as the IDE. On the other hand, the authenticator Rest API was built using ASP.NET Web API utilizing Visual Studio 2019 as the IDE. Further, the authenticator mobile application was developed and implemented using Android Studio IDE utilizing Firebase as a real-time database. Upon completing the development of all the modules, the simulation was run on a laptop computer with Intel Core i7 2.4 GHz and 16 GB RAM.  
Having finalized all the features included in the proposed solution, the proposed solution was developed primarily via an agile methodology. Most agile methods attempt to minimize risk by developing software in short time boxes, called iterations, which typically last one to four weeks. Each iteration is like a miniature software project of its own and includes all the tasks necessary to release the mini-increment of new functionality: planning, requirements analysis, design, coding, testing, and documentation. While an iteration may not add enough functionality to warrant releasing the solution, an agile software project is capable of releasing new software at the end of every iteration. At the end of each iteration, the team re-evaluated project priorities. Overall, it took two months to complete the development tasks.  
In the following, we briefly discuss the implementation of the prototype with its essential functions.
## 3 Results
### SP/SW Measurement Accuracy  
In the majority of studies covered by this survey (53%), SPs and SWs were used to monitor environmental factors affecting sleep. In particular, noise and ALAN were analyzed in eight studies, ambient temperature was analyzed in seven studies, humidity in seven studies, and CO\({}_{2}\) levels were analyzed in four studies (see Figure 4D). In six studies, the data obtained from SPs and SWs were compared with records from conventional monitoring tools [1; 26; 82; 83; 84; 26].  
Overall, the measurements obtained from SPs and SWs were found to be relatively accurate in assessing sleep and ALAN, with reported precision ranging from 85% to 99% [5; 15; 48; 83]. However, the reliability of low-cost technology tools for monitoring noise was found to be slightly lower, ranging from 76% to 94% [15; 48; 82]. Characteristically, none of the studies identified and reviewed in this survey demonstrated the reliability of SPs and SWs for monitoring ambient temperature, humidity, and air pressure. Limited validation of these specific parameters highlights a critical gap in current research, suggesting the need for further studies to assess the potential of SPs and SWs in capturing a broader spectrum of environmental data.
## 2 Materials and Methods  
### Studied Sites  
Three typical transects in China were selected: the Tibetan Plateau (TP), Mongolian Plateau (MP), and Loess Plateau (LP) (Figure 1). The transects were selected as natural grasslands free from human activities (Table 1). The MP spans about 1000 km at 141-1249 m above sea level, with a mean annual temperature (MAT) of about 2.64 \({}^{\circ}\)C. The MP has a mean annual precipitation (MAP) from 168.29 to 448.47 mm. MP soil is mainly chestnut soil. The TP spans about 1600 km at 4037-4592 m above sea level. The MAT is about \(-\)2.91 \({}^{\circ}\)C and the MAP spans from 191.71 to 619.86 mm. TP soil is mainly brown soil. The LP spans about 800 km at an altitude of 792-1684 m above sea level. The MAT is about 8.70 \({}^{\circ}\)C and the MAP spans from 215.50 to 591.18 mm. LP soil is mainly cinnamon soil. Each transect covers meadow steppe (MS), typical steppe (TS), and desert steppe (DS) gradients with increased precipitation from west to east. The microorganism data (bacteria and fungi) are from Yang [38; 39].  
Ten representative sample areas were selected for each grassland transect. Eight sample quadrats (20 m \(\times\) 20 m) were set up in each sample area. For every sampling quadrat, four randomly chosen plots (1 m \(\times\) 1 m) were taken. A soil auger was used to gather soil samples ranging from 0 to 10 cm, which were then merged to create a composite sample. Soil samples were preserved after a 2 mm sieve.
# Quark Powder Valorisation in White Self-Compacting Concrete: Mortar Level Study
## 3 Results and Discussion  
### Raw Powder Materials Characterisation  
As expected, the main oxides of QP revealed 99% of SiO\({}_{2}\) and other minor constituents, namely, P\({}_{2}\)O\({}_{5}\) and Al\({}_{2}\)O\({}_{3}\) (see Table 1). The pozzolanic test results are depicted in Figure 7. According to EN 196-5, the sample satisfies the pozzolanicity test if the point is below the calcium ion saturation concentration curve (expressed in calcium oxide) shown in Figure 7 (adapted from EN 196-5). As such, after 8 and 15 of testing, the samples constituted by cement and QP did not satisfy the pozzolanicity test since the point (test results) lies below the calcium ion saturation concentration curve. Those results corroborate [PERSON] and [PERSON]'s findings, which revealed that only quartz particles with a diameter d\({}_{50}\) below 5 \(\upmu\)m were pozzolanic [43].  
PSD of cement, QP and limestones used in the current work are depicted in Figure 8, and the physical properties are summarised in Table 4. The fineness material, proved by PSD and specific surface, is limestone filler BL, followed by BC, cement, BL and QP. The QP PSD is between cement and sand and has a wide particle size range of 0.5 to 100 \(\upmu\)m. Characteristic diameters d\({}_{10}\), d\({}_{50}\) and d\({}_{90}\) proved to be similar for BC and BL. The density of cement was the highest, as expected, and QP is less dense among all powdered materials with a specific gravity of 2660 kg/m\({}^{3}\).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **CEM II/A-L 52.5 N** & **QP** & **BC** & **BL** & **EC** \\ \hline Density (kg/m\({}^{3}\)) & 3020 & 2660 & 2710 & 2700 & 2720 \\ \hline Specific surface—Blaine Method (m\({}^{2}\)/kg) & 485.4 & 132.0 & 512.0 & 540.0 & 421.0 \\ \hline D\({}_{10}\  
SEM images of cement and limestone are presented in Figure 9, and QP in Figure 10. Cement and limestone particles are polyhedric and with different sizes, below QP. QP particles are irregular and angular-shaped with sharp, clear edges, as observed by [44; 49]. The PSD observed by SEM corroborates the results presented in Figure 8. As also expected, QP particles are mainly silica, as shown by the EDS spectrum (Figure 10d).  
Figure 8: PSD of LFs and QP under study.  
Figure 9: Secondary electron mode SEM image of particles of (**a**) cement (\(\times\)5000); (**b**) Limestone EC (\(\times\)5000); (**c**) Limestone BC (\(\times\)5000); (**d**) Limestone BL (\(\times\)5000).
## 4 Discussion
### Impact of LST and Air Temperature on Photosynthesis  
Previous studies indicated that temperature (an external stress factor) directly affects the vegetation phenology, which played a significant role in carbon accumulation. It is observed that there is a good correlation between LST and air temperature at each site with different land cover types (r \(>\) 0.8, P \(<\) 0.01). Most models used in this study (except GR) have considered the temperature effects, and the model performances indicate that temperature plays an important role in GPP estimations (Table 6). Meanwhile, it is discovered that using measured air temperature at the flux towers instead of LST can give a better GPP estimation for the TG model. Similar findings have also been reported by [PERSON] _et al._[11], as uncertainties in the satellite LST are excluded. Since MODIS LST provides a proxy of an average temperature across pixels, the TG model will be based entirely on remotely sensed variables without any ground meteorological input. According to the original TG model, LST is a useful measure of physiological activity in the top canopy leaves, which provide leaf cover great enough to avoid LST being significantly affected by soil surface temperature [8]. So the TG model shows a greater potential for GPP estimation in space.  
Meanwhile, both LST and air temperature show good correlations with PAR (r \(>\) 0.5) at various land cover types, especially croplands, grasslands, and ENF sites, where r values reach 0.7, which indicates that temperature can provide the best estimation of PAR. Thus, the AVM and TG models can still produce satisfactory GPP estimates without considering the PAR in the model construction.
# A Novel Channel Calibration Method for Bistatic ISAR Imaging System
###### Abstract  
In practical bistatic inverse synthetic aperture radar (ISAR) imaging systems, the echo signals are modulated by non-ideal amplitude and phase characteristics of the transmitting and receiving channels, which seriously distorts image quality. However, the conventional channel calibration method based on a transponder is not applicable to bistatic ISAR imaging systems, since the baseline of the system is up to hundreds of kilometers. A channel calibration method only using calibration satellite echo information is proposed for the system, with a linear frequency modulation (LFM) waveform. Firstly, echoes of the calibration satellite are collected by tracking the satellite and multi-period echoes are aligned in the time domain, according to the pulse compression result. Then, the signal to noise ratio (SNR) is improved by accumulating multi-period echoes coherently in the time domain and the calibration coefficient is constructed based on the accumulated signal. Finally, spectrum of the echo signal is multiplied with the calibration coefficient to compensate the influence of channel characteristics. The effectiveness of the proposed method is verified by the simulation experiment with real satellite echoes.  
b 2018; 8, 2160; doi:10.3390/app8112160
## 2 Materials and Methods
### Preparation Process  
In accordance with the findings of our group's prior research, the optimal ratio for EPRC particle preparation is determined as follows: fly ash/steel slag/cement = 12:2:1. Notably, in terms of particle size selection, EPRC particles with a 5 mm diameter have demonstrated the most effective phosphorus removal. As such, the experimental materials in this study adhere to the previously established optimal ratio and are prepared with a particle size of 5 mm. Furthermore, based on our past investigations, the plant-based foaming agent has exhibited the most favorable results, with an optimal dosage of 6 mL per 750 g of material. Consequently, the dosage of the plant-based foaming agent remains consistent in this experiment at 6 mL per 750 g of material.  
The initial step involves mechanically grinding the steel slag into a powdered form. This powder is then mixed with an appropriate quantity of fly ash, binder, and the plant-based foaming agent. Following this mixing, a modest amount of tap water is added to facilitate thorough blending. Subsequently, granulation is achieved utilizing a drum granularator. Once granulation is completed, the composite particles are carefully arranged on a level surface and allowed to stand for a duration of 6-8 h. Following this resting period, the particles are regularly moistened by sprinkling with water over several days to maintain a consistent surface humidity level. The granulation process is visually depicted in Figure 1.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Name of Material** & **SiO\({}_{\mathbf{2}}\)** & **CaO** & **Al\({}_{\mathbf{2}}\)O\({}_{\mathbf{3}}\)** & **Fe\({}_{\mathbf{2}}\)O\({}_{\mathbf{3}}\)** & **MgO** & **SO\({}_{\mathbf{3}}\)** \\ \hline
**Clinker** & 20.86 & 56.77 & 5.90 & 3.61 & 3.50 & 2.43 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Main components of 525R ordinary silicate cement.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Steel slag particle size range/mesh** & \textless{}12 & 12–80 & 80–320 & \textgreater{}320 & \\ \hline
**Proportion/per cent** & 2.49 & 7.68 & 32.40 & 57.36 & \\ \hline
**Fly ash particle size range/mesh** & \textless{}6 & 6–18 & 18–45 & 45–70 & \textgreater{}70 \\ \hline
**Proportion/per cent** & 7.45 & 12.76 & 42.93 & 23.55 & 11.87 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The size distributions of the substrate.
## 3 Recent Developments and Progress
### GNSS Positioning Performances
The MOFC model with different reference-station densities was further evaluated in GPS and Galileo kinematic PPP-AR solutions. Compared with the PPP-AR solution without tropospheric delay enhancement, the positioning accuracy levels of the MOFC and OFC schemes with a 100 km inter-station distance were improved by 25.7% and 17.8%, respectively, while the corresponding times-to-first-fix (TTFFs) in high-altitude areas were improved by 36.9% and 33.0%. Figure 4: The positioning-error sequence of dynamic PPP by using the ambiguity floating point solution and the ambiguity-fixed solution of real-time products (Reprinted from Ref. [32]). Figure 5: The positioning accuracy of the GPS + Galileo combined solution is improved in kinematic PPP when compared to the GPS-only and Galileo-only solutions (Reprinted from Ref. [33]). [PERSON] et al. [34] proposed a PPP-AR based on factor graph optimization, in which the pseudorange and carrier-phase factors are constructed from the error equations of the raw observations, while the ambiguity resolution factor is constructed from the ambiguity resolution process. Validation results from most MGEX stations showed that the average precision of static PPP was improved from 1.25 cm, 0.61 cm, and 2.29 cm to 0.81 cm, 0.5 cm, and 2.1 cm, corresponding to improvements of 35.1%, 18.7%, and 8.7% in the eastward, northward, and upward directions, respectively (Figure 6).
# Multi-Objective Task Scheduling Optimization in Spatial Crowdsourcing
## 3 The MOTSO Model in SC
### The Ranking Strategy Algorithm
#### 3.1.2 Task Entropy (TE) and Ranked Task Entropy (RTE)  
Considering the popularity of location entropy is useful for maximizing task assignment [12]. Location entropy takes into consideration the number of visitors (workers in our case) to a particular location. In other words, if the location is visited by few workers, then each task within this location should be given a higher priority to be assigned first, because any task with a little entropy has a lower chance to be completed in the next iteration. In our study, the space was represented as 2D cells called a grid; the entropy of the cells was computed and stored in historical data in order to allow for real-time task scheduling, i.e., to reduce the time for recalculating the location entropy every time.  
The TE table was structured as a \(i\times j\) matrix, where \(i\) is the number of tasks and \(j\) is the number of workers (Table 4). Thus, to compute the entropy of the task, we divided the 2D map into several cells; then, we calculated the entropy for each cell based on worker occurrence, as in Equation (3). Then, the incoming tasks took the cell's entropy value in which they appeared.  
\[TE\ (t)=-\sum_{w\in W_{t}}P_{t}(w)logP_{t}(w) \tag{3}\]  
where \(W_{t}\) is a set of distinct users that visited location of task \(t\), \(P_{t}(w)\) is the probability of visits to a task location \(t\) that belongs to user \(w\), and \(P_{t}(w)=\frac{|O_{w}|}{|O_{t}|}\). Meanwhile, \(|O_{t}|\)  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **w1** & **w2** & **w3** & **w4** & **w5** \\ \hline
**t1** & 137.5286391016494 & 140.77934794265565 & 140.83953070394318 & 132.77599556103092 & 156.88627234146358 \\ \hline
**t2** & 47.12959736369906 & 85.88725867729572 & 75.65173447900676 & 77.63439324242962 & 66.6175097791553 \\ \hline
**t3** & 92.55306850854014 & 130.09070678927753 & 92.66485742716874 & 101.97479734391655 & 88.3209190565524 \\ \hline \end{tabular}
\end{table}
Table 2: Task execution duration (TED).  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **w1** & **w2** & **w3** & **w4** & **w5** \\ \hline
**t1** & 2 & 3 & 4 & 1 & 5 \\ \hline
**t2** & 1 & 5 & 3 & 4 & 2 \\ \hline
**t3** & 2 & 5 & 3 & 4 & 1 \\ \hline
**t4** & 1 & 2 & 3 & 5 & 4 \\ \hline \end{tabular}
\end{table}
Table 3: Ranked task execution duration (RTED).  
indicates the number of the visits to a task location in the \(O_{t}\) set, while \(|O_{w,t}|\) indicates the number of visits of the same worker to a task location in the \(O_{w,t}\) set.  
After that, we ranked the TE table according to the task's entropy value by sorting the tasks in ascending order so as to first select the task with the smallest entropy, as shown in Table 5. The reason behind sorting the tasks in ascending order was to correspond to first objective, i.e., maximizing the number of completed tasks; therefore, we give the highest priority to the task with the smallest entropy to help maximize the number of completed tasks.
## 3 Design
### Overall Architecture
**Slave:** Each slave performs streaming encryption of its assigned file into the offset range and asynchronously returns encrypted subsets to the master. Each slave consists of the following components: _Cipher Reader_, _AES Encryptor_, and _Cipher Sender_. _Cipher Reader_ sequentially reads the assigned file region in units of a predefined subset size (e.g., 4, 8, or 16 MB). For each subset, _AES Encryptor_ reconstructs the AES key using the received ciphertext and the QKD-unmasked secret key, then encrypts the subsets using AES in CTR mode. _Cipher Sender_ batches the resulting encrypted subsets and asynchronously  
Figure 2: Overall architecture of our proposed scheme. transmits them back to the master using MPI's non-blocking isend() interface. The three components operate as a pipeline, overlapping disk I/O, encryption, and communication to hide latency and maximize throughput during distributed encryption.
## 3 Measurement Results
### Radiation Patterns  
The far-fields were measured in the anechoic chamber at the UTP's antenna measurement facility, as shown in Figure 9. The meander line antenna was placed on a rotating stand and worked as a receiving antenna, whereas the horn antenna moved in the azimuth and elevation planes and worked as a transmitting antenna. Thus, the radiation patterns were measured on-body inside an anechoic chamber using a Keysight PNA-X N5242A VNA. To minimize the computational resources, in the simulations, only the chest and arm parts of the body were examined. In measurements, the antenna performance was tested on a 3D flat phantom that had dielectric properties similar to human muscle tissues: relative permittivity (\(\varepsilon_{\mathrm{r}}\)) of 52.7 and 48.2, and conductivity (\(\sigma\)) of 1.95 and 6 S/m, at the lower and upper bands, respectively [29]. Figure 10 demonstrates the simulated and measured radiation patterns in various scenarios for the lower and upper bands, respectively. Usually, the body-arm is a suitable location for a wearable antenna because the radiation in the horizontal positions from the arm may not be easily affected, due to it being less surrounded by the lossy tissues than the chest position. It can be seen that the wearable antenna demonstrates omnidirectional radiation at the lower band, which can be used for on-body communication.  
Similarly, omnidirectional radiation was also achieved at the upper band, which may be suited for off-body communications. For on-body S\({}_{11}\) measurements, the subjects were chosen such that their body sizes were similar to the sizes of the chest and arm body models we used in the anechoic chamber. In practical situations, the performance of textile antennas may be affected by the fabrication tolerances and irregularities of the materials. It can be seen that the design was tested in different scenarios and maintained a wide BW with good stability in operating frequency and radiation patterns. Additionally, the measured gain, as reported in Table 3, showed stable values in both bands.
# Detection of Forged Images Using a Combination of Passive Methods Based on Neural Networks
## 3 Materials and Methods  
In this section, we present the methodology adopted for image manipulation detection, which integrates Error-Level Analysis (ELA) and Discrete Wavelet Transform (DWT) alongside a novel multi-stream neural network architecture.  
Our decision to adopt a multi-stream neural network architecture was driven by the recognition that traditional passive methods could offer valuable insights to guide the learning process of a Convolutional Neural Network (CNN) for image manipulation detection. Rather than aiming to identify the optimal method outright, we viewed this choice as an initial exploration of the potential capabilities such an architecture may offer. In line with this perspective, we selected two streams based on methodologies outlined in previous literature, with the intention of extracting different facets of image data. This approach was motivated by our desire to integrate diverse aspects of passive image analysis, serving as a foundational step in our investigation into the effectiveness of multi-stream architectures for detecting image manipulation.  
The multi-stream architecture comprises three distinct CNNs, each operating on a unique data stream extracted from the original image. Two of these streams were selected based on methodologies outlined in prior literature, which will be explained in detail in the subsequent sections. Each stream is designed to analyze specific data subsets, while the third stream processes the unaltered image itself. By adopting this multi-stream framework, our aim is to leverage the strengths of traditional passive methods and integrate them into a unified detection system.