esa-sceva/satcom-chunk-collection huggingface dataset:
# A New Low-Cost Compact Antenna for the 2.45 and 5.8 GHz ISM Bands
## 2 Dual-Band Antenna Design
### Proposed Antenna Configuration  
First, it should be noted that all the simulation results presented were obtained using the high-frequency simulation software HFSS-version 13.0.  
The proposed antenna is rectangular in shape and fed by a centered microstrip line. The advantage of choosing a rectangular shape is that rectangular patch antennas can be sized according to theoretical equations [31]. The radiating element and ground plane have a thickness of 0.035 mm, which represents the most economical standard PCB (printed circuit board) thickness. The substrate used for the antenna design (FR-4 epoxy) has a relative permittivity of 4.4, a loss tangent of 0.02, and a thickness of 1.6 mm. For simplicity's sake, the feed line is a microstrip placed in the same plane as the radiating element.  
The proposed antenna configuration after optimization steps is shown in Figure 1. Figure 1,b show a top view of the antenna, with the radiating element and feed line coplanar and positioned on the dielectric substrate. Figure 1 shows the bottom view of the antenna. The antenna features circular and rectangular slots.  
Figure 1: Proposed dual-band antenna optimized design: (**a**) top view of the feed line; (**b**) top view and (**c**) bottom view of the patch antenna.  
Inspired by the works in [31], the antenna feed line shown in Figure 1a features a pair of rectangular notches in its upper part (at the lower limit of the radiating element), followed by another pair of square notches inserted on the sides. Considering Figure 1b, on the radiating element, the designed antenna has three ring-shaped slots. Inside the central ring with radii R1 and R2 are three slots, two of which are rectangular and identical, symmetrical to the third circular slot. The ground plane shown in Figure 1c has a partial length with respect to the dielectric substrate. The upper part of the ground plane is crenellated due to the insertion of square slots.  
Analytical calculations [31] for sizing patch antennas are best suited to single-band antennas and do not guarantee the achievement of other desired matching frequencies in the case of a multiband antenna because sizing is performed at a single frequency (the lowest in the case of a multiband antenna). Thus, the purpose of the slots created on the antenna is, on the one hand, to obtain two frequencies adapted to ISM bands and, on the other, to improve the antenna's reflection parameters and performances. The dimensions and position of the slots in the occurrence of those of the central ring are chosen to favor frequency shifting or the obtaining of the second frequency band. The other slots of the radiating element are inserted to adjust the antenna to its central matching frequencies.  
Table 1 presents the dimensions of each element of the antenna (slots, patch, feed line, substrate, and ground plane). The optimized dimensions of the patch (Lpa \(\times\) Wpa) and substrate (Lsu \(\times\) Wsu) are 33 mm \(\times\) 18.5 mm and 40 mm \(\times\) 24 mm, respectively.
# Discrete Environment-Driven GPU-Based Ray Launching: Validation and Applications
## 5 Conclusions  
In this work, a GPU-parallelized ray-launching algorithm previously proposed in [21] is tested against a very large set of measurements to assess its accuracy and efficiency. The validation has been carried out for over 500 base station sites in dense urban areas, including the very challenging scenario of San Francisco, showing that the overall accuracy is very similar to that of a standard image ray tracing algorithm, in spite of a much lower computation time (more than three orders of magnitude). Moreover, by evaluating the results for different tile sizes, it is shown that the accuracy of the proposed algorithm has little dependence on the discretization of the environment.  
Further results are presented in the Atlanta and San Jose scenarios, showing a generally good performance, also in terms of temporal accuracy.  
An outlook with some potential applications of the proposed algorithm is finally provided, with particular reference to UAV-aided wireless networks and localization based on fingerprinting.  
Conceptualization, E.M.V., J.S.L. and V.D.-E.; Methodology, E.M.V. and V.D.-E.; Software, E.M.V.; Validation, E.M.V., J.S.L. and J.J.Z.; Resources, J.J.Z. and S.G; Data curation, J.J.Z.; Writing--original draft preparation, E.M.V.; Writing--review and editing, E.M.V., J.S.L., S.G. and V.D.-E.; Supervision, E.M.V. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
**Data Availability Statement:** Network and cell site data are protected by Non-Disclosure Agreement between the cellular operator and Polaris Wireless Inc. Cell site identifier used in this paper have no relation with actual identifiers used by the cellular operator.  
This work has been carried out in the framework of a research collaboration between Polaris Wireless Inc. and the CIRI-ICT Industrial Knowledge Transfer Center, University of Bologna, Italy.  
The authors declare no conflict of interest.
## 5 Analytical Modelling
### Description of the ACK and the Stochastic Cracking Models
Because of the non-deterministic nature of the matrix cracking stress, it is necessary to underline the difference between the mean saturated crack distance at the end of Stage II (\(X\)) and the mean actual crack spacing (\(x\)). While the \(X\) value can be evaluated experimentally, observing the crack pattern at the end of a tensile test, both the values of the transmission length \(\delta_{0}\) and the actual crack distance \(x\) are functions of the applied stress. Depending on the value of the stress \(\sigma_{c}\) applied to the composite, \(x\) may be smaller than, equal to or larger than \(2\delta_{0}\). In particular, \(x\) tends to \(X\) according to the following formula:  
\[x=X\bigg{\{}1\ -\ \exp\left[-\left(\frac{\sigma_{c}}{\sigma_{Rc}}\right)^{m} \right]\bigg{\}}^{-1}, \tag{13}\]  
where \(\sigma_{Rc}\) is the reference average cracking composite stress, computed according to Equation (10). The stress \(\sigma_{mu}\) is taken as the average tensile strength \(f_{ctm}\) of Table 3 and \(m\) is the width of the strength distribution (respectively, the first and the second parameter of the Weibull function, see Table 3). The analytical stress-strain curve of the composite (see Figure 14) is described by means of the following non-linear functions:  
\[\varepsilon_{c}=\left\{ \begin{array}{l}\frac{\sigma_{c}}{E_{c1}}\Big{(}1+ \frac{\beta\cdot\delta^{*}}{x}\Big{)}\,\ x\ \geq\ 2\delta_{0}\\ \sigma_{c}\Big{(}\frac{1}{E_{f}V_{f}^{*}}-\frac{\beta\cdot x}{4\delta^{*}} \cdot E_{c1}\Big{)}\,\ x\ <\ 2\delta_{0}\end{array} \right. \tag{14}\]with  
\[\beta=\frac{E_{m}V_{m}^{*}}{E_{f}V_{f}^{*}}\, \tag{15}\]  
and where \(\delta^{*}\) is evaluated according to Equation (11), with the evolving matrix stress in place of \(\sigma_{mu}\).
# Water Supply Systems for Settlements with Arsenic-Contaminated Groundwater--Making the Right Choice
## 3 Results and Discussion
### Drinking Water Treatment Options Based on Source Water Quality
#### 3.2.1 Treatment Options for the Municipality of Kanjiza Using the Current Source  
The largest water consumer in the municipality is the town of Kanjiza, with almost 10,000 inhabitants (40% of the total population in the municipality). Unfortunately, the groundwater source has elevated concentrations of CH\({}_{4}\), CO\({}_{2}\), NH\({}_{3}\), Fe, Mn, organic matter \(<\) 20 mg KMnO\({}_{4}\)/L, and As \(<\) 50 mg/L (see Table 3). As such, this water requires a relatively complex treatment processes (Scheme D in Table 1), which is depicted in Figure 5, so it is advisable to consider the possibility of bringing better quality water from a distant source.  
For adequate treatment, this water requires aeration, oxidation (using, for example, chlorine), sand, granulated activated carbon filtration, and a specific adsorber for arsenic removal. The elevated concentrations of arsenic are removed using this scheme in two steps: (i) by addition of chemical oxidant, all As(III) is converted to As(V), increasing the proportion of arsenic removed with the deferrisation process, thus relieving (ii) the arsenic adsorber and extending the replacement of the specific adsorbent used for arsenic, improving the overall economics of the water treatment process.
## 4 Experiment and Results
### Training Results
#### 4.6.2 Comparison Analysis Under Different Proportions of Electric Vehicles  
The proportion of electric vehicles was set to 30%, 60%, and 90% to explore the impact on the model's optimization performance. The corresponding results for average vehicle waiting time and average CO2 emissions are shown in Figure 11.  
As shown in Figure 11, the average waiting time for vehicles under different optimization schemes fluctuates as the proportion of electric vehicles increases. At 30% and 60% electric vehicle penetration, the Rainbow DQN_AM-CO2 Reduction scheme reduces the average waiting time by more than 15% compared with the actuated signal timing. However, as the proportion of electric vehicles rises to 90%, the optimization effect of the proposed reward function weakens. The Rainbow DQN_AM-Time scheme, which focuses solely on the waiting time, maintains stable optimization performance. Additionally, due to the faster acceleration of electric vehicles, waiting times are reduced to some extent. Despite this, under the same reward function, the Rainbow DQN_AM-CO2 Reduction method still achieves a more than 10% reduction in average waiting time compared with the DQN and D3 QN models. As shown in Figure 11, the increase in the proportion of electric vehicles leads to a natural decline in CO2 emissions at the intersection. When the proportion of electric vehicles is 30%, 60%, and 90%, the corresponding average CO2 emissions for the Rainbow DQN_AM-CO2 Reduction scheme are 188.1 g, 151.44 g, and 118.3 g, respectively. As the proportion of electric vehicles increases, the optimization effect of the proposed scheme gradually weakens. At medium and low proportions, the scheme  
Figure 11: Model results for different proportions of electric vehicles: (**a**) average vehicle waiting time; (**b**) average vehicle CO2 emissions.  
Figure 10: Model results under different CO2 emission factors for electric vehicles: (**a**) average vehicle waiting time; (**b**) average vehicle CO2 emissions.  
can reduce average CO\({}_{2}\) emissions by 4% to 7% compared with the actuated signal timing, but at a 90% electric vehicle proportion, the reduction is only about 2%. It is noteworthy that when the proportion of electric vehicles reaches 90%, the average CO\({}_{2}\) emissions from the D3 QN-CO2 Reduction and DQN-CO2 Reduction optimization schemes show no significant difference compared with the actuated signal timing. This is because electric vehicles release much lower CO\({}_{2}\) emissions during idling, acceleration, and deceleration than conventional gasoline vehicles. At high electric vehicle proportions, the CO\({}_{2}\) emissions at the intersection are already at a low level, so the potential for further optimization is reduced.
# A Novel Hybrid Edge Detection and LBP Code-Based Robust Image Steganography Method
## 2 Related Works
### Hybrid Edge Detection Based Image Steganography Method  
In 2021, [PERSON] et al. [25] proposed a hybrid edge detection-based image steganography method. They applied an n-number of edge detectors on m-bits cleared images and hybridizes those edge images using AND operator. Then they classified the cover pixels as edge and non-edge pixels. At the same time, they also encrypt the secret message using the chaotic method. They implant x-bits into edge pixels and y-bits into non-edge pixels, where x \(>\) y, and generate a stego image.
## 2 Literature Review
### Discussion
#### 2.2.4 Satellite Surveillance
The Landsat-8 was launched in 2013 and provides data in visible, SWIR, NIR and thermal IR spectrums (Table 2). The Operational Land Imagers (OLI) channels 1, 5, 6 and 7 are typically used for fire detection [92]. Using the [PERSON] dataset [91] for training, [PERSON] et al., developed a convolutional neural network (CNN)-based architecture called \"MultiScale-Net\" for finding fires [93]. To accommodate for fires of different sizes and patterns, the CNN used multiple sized kernels for feature extraction and changing dilation rates for the dilated convolution layer. SWIR bands and blue channel data were used as inputs to the CNN. As an attempt to make satellite technology accessible to the general academic community, the CubeSat project was started in 1999 by a collaborative effort between California Polytechnic state University (San Luis Obispo) and Stanford University. The objective of this project was to boost research exploration and invention at affordable budgets. CubeSats have a standardized design and are launched as part of the payload on other satellite launches [94]. [PERSON] et al., used the images from 1U AeroCube-4 equipped with three 2 MP cameras to demonstrate CubeSats as a viable option for wildfire detection [95]. However, downloading the images to a ground station usually takes hours. To mitigate this challenge, [PERSON] et al., proposed k-nearest and CNN deep-learning (DL) models for image classification implemented on a Raspberry Pi [96]. The runtime for this RPi unit was optimized to minimize the power consumption and was integrated on to a KITSUNE 6U CubeSAT. The KITSUNE uses a Sony IMX342 color sensor that generates the images fed into the DL model for classification. Using this setup, the group was able to achieve classification accuracy of above 95% using networks like ShallowNet and LeNet [96; 97]. Another recent example is Orora Technologies, which specifically designs 3U CubeSats furnished with IR cameras for finding and monitoring wildfires from space [98]. They claim to be currently protecting 302,588,843 hectares of forests spread over six continents. The Orora CubeSats have onboard GPUs to assist with wildfire detection without needing to download the data to the ground station for analysis.
# Layer-Wise Query Selection to Eliminate Redundant Queries in DETR
## 2 Related Work
### Introduction to DETR and Its Computational Challenges  
DETR [1] was the first end-to-end object detection model based on the Transformer architecture. Unlike conventional CNN-based object detectors [12; 13; 14; 15; 16; 17; 18; 19], which rely on complex post-processing steps such as anchor generation and non-maximum suppression (NMS), DETR directly predicts objects using a Transformer-based encoder-decoder structure [20]. The model assigns object queries to final predictions through a one-to-one bipartite matching mechanism, enabling a streamlined detection pipeline without heuristic components. This approach provides several advantages, including end-to-end learning, a global attention mechanism for feature extraction, and an optimized assignment process that eliminates redundancy in object matching.  
Despite these advantages, DETR suffers from significant computational inefficiencies, primarily due to the excessive number of object queries processed in the decoder. The original DETR model used 100 object queries, but subsequent studies have increased this number to over 900 to improve prediction stability [2; 3]. However, in real-world applications, images rarely contain more than 50 objects, meaning that a large portion of these queries are redundant. This inefficiency increases computational costs, prolongs both training and inference times, and imposes a heavy burden on GPU memory, making DETR less suitable for resource-constrained environments. To address these challenges, researchers have proposed various modifications aimed at either improving DETR's performance or enhancing its efficiency, which are discussed in the following sections.
# Covert Channel Communication as an Emerging Security Threat in 2.5D/3D Integrated Systems
## 3 Types of Physical Covert Channels
### Electromagnetic Radiation-Based Channels  
Electromagnetic (EM) signals can be used as a medium for physical covert channels, with the unique ability to travel through many physical obstacles (i.e., concrete walls) with negligible energy loss. BitJabber is a high bandwidth covert channel that uses the spectra of EM waves to transmit data between air-gapped devices [31]. The sender creates the covert channel through memory accesses to modulate the electromagnetic signal generated by the clock signal of the DRAM chip. It was determined earlier in [55] that accessing memory results in unwanted side-channel information leakage, specifically with the same frequency as the memory accesses. For example, memory accesses with an execution time of approximately 350 ns result in an EM spectra with raised energy at multiples of that frequency (2.86 MHz). After measuring the EM signal on the receiver side, the data is extracted by observing the spectra at these known frequencies, which correspond to either a bit 0 or 1 being transmitted. The major contribution of Bitjabber is its potential for such a high bandwidth, while still being able to penetrate thick concrete walls to an adjacent room. It was shown that BitJabber was implemented with two desktop computers. A log-periodic type of antenna was used to collect the EM signals around the DRAM clock frequency (400 MHz to 1 GHz) [31]. The data was collected in a typical office environment, which had multiple sources of background noise (such as radio stations, cell towers, other components within the desktop, and wires inside the walls). Experiments were performed with two scenarios: (1) the antenna was placed adjacent to the computer to receive the strongest EM waves from the DRAM clock signal and (2) the antenna was placed in a neighboring office that shared a 15 cm thick concrete wall. For the experimental setup with the antenna adjacent to the computer, with OOK modulation at a bandwidth of 100,000 bps, there was only a 0.4% bit error rate [31]. Utilizing BFSK modulation decreased the bit error rate to 0.25% at the same bandwidth of 100,000 bps [31]. Bitjabber is a practical covert channel because of its high bandwidth. Additionally, it can be relatively difficult to detect because the memory accesses required to encode the data can look like normal operation. The capability of EM waves to pass through walls means that it is less obvious to observant users, as compared to optical or acoustic covert channel attacks.
# Determination of Cyanotoxins and Prymnesins in Water, Fish Tissue, and Other Matrices: A Review
## 4 Sample Preparation and Analytical Methods for Cyanotoxins and Prymnesins Detection
### Sample Preparation  
Sample preparation procedures differ according to the toxin analyzed and the nature of the tissue, organ, or biomass analyzed. Typically, this includes extraction, clean-up, and any post-extraction modifications (e.g., derivatization and solvent exchange) prior to analysis. Table 3 lists a variety of sample extraction and preparation procedures from a survey of the literature. The typical workflow being followed is summarized in Figure 1.  
Procedures for extracting toxins from complex matrices vary widely (Table 3). In most cases, heterogeneous samples, including tissues, organs, and food supplements, are mechanically homogenized or lyophilized to break down any solid structures and produce a suspension or emulsion amenable to extraction by solvents containing various percentages of aqueous and/or organic solvents (acidified methanol, acidified water, acetone, acetonitrile, butanol, etc.). In samples containing intact cyanobacterial cells, lysis to release intracellular toxins is often facilitated by ultrasonication, extended mixing, or incubation  
Figure 1: Common sample extraction and preparation procedures for measuring cyanotoxins in tissue.  
at a specific temperature and application of heat [48]. Following lysis/homogenization and extraction, a variety of clean-up techniques are available (Figure 1), including centrifugation, solid-phase extraction (SPE), protein precipitation, hexane washes, filtration, and immunoaffinity columns [49]. The clean-up steps attempt to remove matrix components, after which the toxin is eluted/extracted with a solvent and reduced in volume by evaporation prior to analysis. The efficiency of extraction techniques and the clean-up steps in the recovery of cyanotoxins from complex matrices vary widely based on the toxin physicochemical properties, matrix effects, holding times, collection methods, and other site-specific variables. In addition, each of these extraction techniques and clean-up steps may result in some loss of the target toxins during sample processing.  
Extraction of cyanotoxins from complex matrices requires use of appropriate solvent mixtures to remove them, ideally without also extracting interferences to the analytical techniques being employed. The references cited in Table 3 are, broadly, either attempting to extract only individual types of toxins or attempting to quantify multiple classes with a single extraction workflow. The former can potentially allow for optimal conditions to be obtained, while in the case of more general extractions, compromises may result from attempting to measure chemicals less compatible with the extraction conditions. The primary differentiating step is the extraction procedure, solvents, and conditions used, which are elaborated upon below.