esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods  
### Overview of the Proposed Method  
This study introduces three innovative approaches to enhance computational efficiency and feature modeling in visual tasks. Faster-LTNet employs partial convolution (PConv) to minimize computational redundancy and memory usage, achieving high floating-point operations per second (FLOPs). Its hierarchical structure includes embedding layers for initial spatial downsampling and merging layers for further resolution reduction and channel expansion. By integrating inverted residual blocks with shortcut connections, Faster-LTNet ensures efficient feature reuse and central feature emphasis through a T-shaped convolution structure. The CG Attention Module addresses inefficiencies in multi-head self-attention (MHSA) by introducing grouped feature inputs, which reduce computational complexity, and cascading operations, which refine feature representation and improve attention diversity. This approach enhances model capacity and ensures lightweight computation while increasing the non-linear expressiveness of the network. Lastly, the RMT Spatial Prior Block introduces explicit spatial priors through a Manhattan distance-based spatial decay matrix and reduces the attention complexity from quadratic to linear via a novel linear decomposition strategy. This enables efficient global and local context modeling, especially for high-resolution feature maps, while balancing computational costs. Together, these methods provide a comprehensive and flexible framework for achieving high efficiency, advanced feature representation, and broad applicability across various visual tasks. The overall network framework of this study is illustrated in Figure 1.  
In addition, the modules proposed in this research can also be applied to other agricultural scenarios. For example, Faster-LTNet's lightweight and real-time performance make it well-suited for scenarios such as pest detection in large-scale crop fields, where high-speed inference is critical to enable timely intervention. CG Attention Module's ability to enhance feature extraction by focusing on critical regions can be beneficial for tasks such as fruit maturity classification, where subtle variations in texture or color need to be captured effectively. By leveraging spatial priors to refine detection, RMT Spatial Prior Block can be applied to livestock monitoring tasks, such as identifying specific health conditions or behaviors in animals within cluttered farm environments.
## Appendix B Review of Energy Management (ECO) Features
### ECO-Comfort Functionality  
Figure 12 shows how the ECO-comfort functionality dynamically alters the cabin setpoint temperature throughout the day. The dynamic temperature setpoint of the ECO-comfort depends on the passenger count inside the bus as well as the ambient temperature. The temperature setpoint is devised to save the energy required for climate control at the expense of slightly reduced passenger comfort. This means a little less cooling inside the bus during summers and a little less heating inside the bus during winters. As well as dynamic temperature setpoints, ECO-comfort also uses pre-conditioning to reduce the energy requirement needed for heating or cooling when the bus is in motion. Pre-conditioning means to utilize the thermal management system to track the setpoint temperature of the bus while it is connected to the grid for charging; thus appropriating the energy from thegrid instead of the battery. The energy reduction by ECO-comfort is highly dependent on the climate, e.g., for a hot climate, the maximum energy reduction due to ECO-comfort is achieved during mid-summer, while for colder climates, the maximum energy reduction is attained in mid-winter.  
### ECO-Comfort Functionality  
Figure 14 shows the ECO-charging functionality, which makes use of pulsed charging, instead of continuous charging. Since the charging is pulsed, the battery has a chance to cool down in between the charging pulses; this reduces the temperature increase during charging, and necessitates less cooling by the HVAC system. At the same time, this also results in low c-rate charging on average, thus improving battery longevity. The disadvantage of this charging method is that the battery will take longer to charge; to mitigate this, either the charging duration needs to be increased, which is not always possible due to bus scheduling constraints, or the battery size needs to be increased so that the battery can deliver the range required during its scheduled operational period. Thus, ECO-charging prevents excessive battery heating during charging, has minimal effect on the vehicle's energy requirements, and lowers the load on the electricity grid.
## 3 Methodology
### C-Factor (Crop Cover Factor)  
The C-factor ranges from 0 to 1 [11], and it characterizes the vegetation cover percentage and crop's influence on soil erosion. Vegetation coverage is considered a key factor in preventing water erosion. In order to quantify vegetation cover indices, the Normalized Difference Vegetation Index (NDVI) is used [11, 49]. NDVI establishes a relation between soil erosion on land with no vegetation and soil erosion under certain vegetation. It also highlights cover type and density's impacts on preserving soil. A C-factor map was generated by using Equation (9).  
\[\text{NVDI}=\frac{\text{NIR}-\text{Red}}{\text{NIR}+\text{Red}} \tag{9}\]  
Figure 4: (**a**) Maps of Soil type; (**b**) Soil erodibility (K-factor).  
NDVI stands for normalized difference vegetation index, NIR represents the near-infrared band, and Red represents the red band of the satellite image. Landsat 8-9 OLI/TIRS images with a spatial resolution of 30 m were utilized to estimate vegetative density. A Normalized Difference Vegetation Index (NDVI) was computed from Landsat images to approximate greenery. C factor was estimated by using Equations (10) and (11) [70,71], shown below.  
\[\text{C factor}=0.450-0.805\times\text{NDVI} \tag{10}\]  
\[\text{C factor}=\frac{(-\text{NDVI}+1)}{2} \tag{11}\]  
More than 60% of the watershed is covered by vegetation and trees, resulting in a lower C-factor. Conversely, the C-factor is higher in the northeastern parts of the area, which consists of clean land and glaciers. The land use and corresponding C-factor values, as shown in Figure 6a,b, illustrate the variation in vegetation cover and its influence on erosion across the study area.
# A pH Monitoring Algorithm for Orifice Plate Culture Medium
## 1 Introduction
Over the last 30 years, machine vision technology has developed rapidly, and it is increasingly used in transportation, agriculture, medicine, industry, and other fields [12; 13]. However, its application in the automated preparation of cells is rare in China. Currently, most automated cell-preparation equipment employs monitoring modalities. This is primarily based on manual empirical observations regarding the need for fluid changes or alkali supplementation [2; 14; 15; 16; 17]. None of the traditional methods consider non-invasive monitoring of pH changes in cell culture fluids, which can affect the quality of cell culture. In this paper, we propose an algorithm for pH monitoring of culture fluid in well plates to address the problem of accuracy in determining pH with traditional manual observation methods. The algorithm utilizes a machine vision technique to extract the color characteristics of cell fluids in culture plates. The images are then segmented under the RGB color space. Finally, the mathematical relationship between pH and HSV model is established to realize intelligent pH monitoring based on the HSV of the acquired images and the pH information of the experimental acquisition. In this paper, a method of pH monitoring based on cell fluid color is proposed for the first time, which provides a theoretical basis for follow-up operations on cell culture, such as accurate liquid change or alkali supplement, and promotes the improvement of cell culture quality.
# A Serious Gaming Approach for Crowdsensing in Urban Water Infrastructure with Blockchain Support
## 1 Introduction  
Cyber-Physical-Social systems (CPSS) are essential for sustainable developments such as smart grids, autonomous vehicles and health monitoring [1]. Mobile CrowdSensing (MCS) has untapped potential in water resources management for both data collection and raising awareness about water-related problems [2]. Citizen science is essential for this matter, to allow harnessing the potential of mobile devices in the scope of the community [3].  
During the last few decades, modern society has evolved around the emerging technologies that have shaped the way we interact with the environment. The video game industry has created one of the leading technologies in digital entertainment. Video games have been developed since the beginning of the digital age, based on the major hardware and software advancements over the last few decades. From arcade games to home consoles and PC (Personal Computer) games, there is now a variety of platforms, genres and communities built around this cultural medium with global implications [4].  
In this technology-driven world, Serious Gaming (SG) can harness the power of video games in transforming the way we interact with the environment and raising awareness about water-related problems. Such an example is represented by collaborative applications for increasing the level of participation of citizens in the community [5].  
There are many challenges associated with achieving a sustainable development of a modern society that is both resource-efficient and ethical, of which the level of trust between the stakeholders is of primary importance. Blockchain technology was created asa solution to the problem of mistrust and has the ability to reconfigure industries by ensuring transparency and trust between network members, reducing excessive bureaucracy, reducing costs, facilitating faster transactions and improving the flow of these transfers [6]. In this sense, efficient management of water resources can be achieved by using blockchain as a trusted storage ledger shared between stakeholders [7].  
Since considerable advancements have been made over the last few years in these emerging technologies, the scope of each piece of the puzzle must be carefully considered for the sustainable development of modern society [8; 9].  
The main objectives of this paper are: (1) to realize a scientific study of approaches related to serious games, focusing on applications for water resource planning and management; (2) to design a serious game in order to analyze and evaluate various scenarios in the context of water distribution systems; (3) to design and evaluate a secure storage layer based on blockchain technology.
# Support Vector Machine-Based Transmit Antenna Allocation for Multiuser Communication Systems
###### Abstract  
In this paper, a support vector machine (SVM) technique has been applied to an antenna allocation system with multiple antennas in multiuser downlink communications. Here, only the channel magnitude information is available at the transmitter. Thus, a subset of transmit antennas that can reduce multiuser interference is selected based on such partial channel state information to support multiple users. For training, we generate the feature vectors by fully utilizing the characteristics of the interference-limited setup in the multiuser downlink system and determine the corresponding class label by evaluating a key performance indicator, i.e., sum rate in multiuser communications. Using test channels, we evaluate the performance of our antenna allocation system invoking the SVM-based allocation and optimization-based allocation, in terms of sum-rate performance and computational complexity. Rigorous testing allowed for a comparison of a SVM algorithm design between one-vs-one (OVO) and one-vs-all (OVA) strategies and a kernel function: (i) OVA is preferable to OVO since OVA can achieve almost the same sum rate as OVO with significantly reduced computational complexity, (ii) a Gaussian function is a good choice as the kernel function for the SVM, and (iii) the variance (kernel scale) and penalty parameter (box constraint) of an SVM kernel function are determined by 21.56 and 7.67, respectively. Further simulation results revealed that the designed SVM-based approach can remarkably reduce the time complexity compared to a traditional optimization-based approach, at the cost of marginal sum rate degradation. Our proposed framework offers some important insights for intelligently combining machine learning techniques and multiuser wireless communications.  
ant  
research fields of communication and signal processing, different types of machine learning methods including (i) unsupervised learning, (ii) reinforcement learning, and (iii) supervised learning find suitable application areas in many areas [16; 17; 18; 19]. In this study, we attempt to explore the possibility of applying supervised learning techniques (switching the optimization-driven thinking to the data-driven thinking) to solve a specific antenna allocation problem in multiuser communication systems. Even compared to the optimization-driven antenna selection algorithms with reduced complexity [13; 20; 21; 22; 23; 24; 25], our proposed supervised learning-based approach is a promising alternative since it demonstrates almost optimal performance while greatly reducing complexity. Moreover, our supervised learning-based approach can also provide design flexibility based on a trade-off between computational complexity and sum-rate performance by adjusting the number of effective classes, i.e., computational complexity can be further reduced by using a smaller number of effective classes, with tolerable sum-rate performance degradation.
# Spherical Planting Inversion of GRAIL Data
## 2 Materials and Methods
### Model Studies
#### 2.2.2 Verification of the Validity of Spherical Planting Inversion  
As shown in Figure 4, we verify the proposed algorithm using the composite model following [PERSON] et al. [14] with two tesseroids at the same depth for easy comparison. One model occupied the volume from 32\({}^{\circ}\) to 34\({}^{\circ}\) longitude, 34\({}^{\circ}\) to 36\({}^{\circ}\) north latitude, and 1678 to 1718 km in depth, with a density contrast of 0.2 g/cm\({}^{3}\). The other model had a density contrast of 0.7 g/cm\({}^{3}\), and its geological setting ranged from 36\({}^{\circ}\) to 38\({}^{\circ}\) longitude, 34\({}^{\circ}\) to 36\({}^{\circ}\) latitude, and 1658 to 1698 km in depth. A total of 62,500 data points is calculated 10 km above the lunar surface with an interval of 0.8\({}^{\circ}\)\(\times\) 0.8\({}^{\circ}\) along latitude and longitude. The field source space is divided into 20 \(\times\) 20 \(\times\) 10 = 4000 tesseroids, and each tesseroid is 0.8\({}^{\circ}\)\(\times\) 0.8\({}^{\circ}\)\(\times\) 10 km along latitude and longitude, and radius direction, respectively. Figure 5 shows the data contaminated with pseudo-random Gaussian noise with zero means and five mGal standard deviations. Then, we invert the contaminated data with two seeds; one seed is located at [33\({}^{\circ}\), 35\({}^{\circ}\), 1698 km] with 0.2 g/cm\({}^{3}\) density contrast, and the coordinates of the other seed were 35\({}^{\circ}\), 37\({}^{\circ}\) and 1678 km along latitude, longitude and radius direction, respectively, with a density contrast 0.7 g/cm\({}^{3}\).  
Figure 3: Recovered density distribution of spherical planting inversion of \(g_{x}\) data. (**a**) With the horizontal weighting function \(w_{h}\). (**b**) With the novel seed weighting function \(\overline{w}_{h}\). The weight distributions as shown in the \(z\) = 120 km plane. \(r_{s}\) = 50, \(\beta_{h}\) = 1.2, \(\delta\) = 0.05, and \(\tau\) = 5.0 \(\times\) 10\({}^{-4}\).  
It is difficult for the Occam inversion to recover depth resolution from surface gravity data because there is no depth resolution in the data [21; 45; 46]. The interfaces of the two models in the spherical Occam inversion results are ambiguous, even though the depth weighting matrix is used. In contrast, the density slices of the spherical planting inversion are consistent with those of the forward model in Figure 6, which verifies the correctness of the algorithm presented in this paper.  
Figure 4: Composite model: (**a**) A horizontal slice of the composite model with a radial length of \(1698\) km. (**b**) A vertical profile of the composite model with a latitude of \(35^{\circ}\). \(\delta=2.0\times 10^{-5}\) and \(\tau=1.0\times 10^{-6}\).  
Figure 5: Gravity maps: (**a**) Forward modeling result. (**b**) Prediction data obtained by spherical planting inversion. (**c**) Residual data between forward modeling result (**a**) and prediction data (**b**). \(\delta=0.05\), and \(\tau=5.0\times 10^{-4}\).
## 4 The Results of Hypothesis Testing
### The Assessment of Validity and Reliability  
The assessment of validity and reliability of measures for multi-item constructs in models is an essential factor in ensuring the strength and precision of the constructs being evaluated. Table 2 provides the results of the constructs, and a visual presentation is provided in Figure 5. In the context of partial least squares structural equation modeling (PLS-SEM), the values are organized according to their indicator's reliability, ensuring that each latent variable within the model is reliably measured [105]. A Cronbach's alpha value exceeding the threshold of 0.7 is indicative of good internal consistency, ensuring that the items within the construct are well correlated and consistently measure the same characteristic [106; 107]. An AVE higher than 0.5 demonstrates high convergent validity, indicating that the constructs in the model are unidimensional and well measured [107] shown in Figure 6.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Construct.** & \begin{tabular}{l} **Cronbach’s** \\ **Alpha** \\ \end{tabular} & \
\begin{tabular}{l} **Composite** \\ **Reliability** \\ \end{tabular} &
\begin{tabular}{l} **Average Variance** \\ **Extracted (AVE)** \\ \end{tabular} \\ \hline Extraversion Characteristics (EXCs) & 0.928 & 0.952 & 0.873 \\ Agreeableness Features (AGFs) & 0.946 & 0.953 & 0.902 \\ Conscientiousness Attributes (COAs) & 0.988 & 0.989 & 0.977 \\ Neuroticism Factors (NEFs) & 0.982 & 0.983 & 0.982 \\ Openness Aspects (OPAs) & 0.988 & 0.993 & 0.976 \\ Self-Assessed Knowledge (SAK) & 0.889 & 0.940 & 0.898 \\ Test-Assessed Knowledge (TAK) & 0.971 & 0.975 & 0.972 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Construct measures of reliability and validity.
# Towards Explainable Quantum Machine Learning for Mobile Malware Detection and Classification +
## 6 Related Work
After extracting static and dynamic analysis features from the malware samples, they constructed the necessary vectors for the clustering algorithm and grouped the samples into families based on this information. Another study that used the call graph clustering was proposed in [49]. In [50], the authors extracted n-gram features from the binary code of the samples. These features were selected using the Sequential Floating Forward Selection (SFFS) method with three classifiers, and the accuracy of the method reached 96.64%. However, their methodology was different from the one presented in this paper, and they did not provide results specifically referring to ransomware. In [51], the authors made use of the PrefixSpan algorithm to create groups of malware families, based on sequence patterns. However, these patterns are related only to network traffic packets and not to the overall behavior of the malware. In [6] also, malware samples were assigned to a specific family by exploiting model checking. In [52], ARIGUMA was proposed, a malware analysis system for family classification. The authors considered the number of functions being called in a function and the number of functions that call a specific function. Furthermore, the local variables, the arguments, and the instructions were taken into account. Even though this method can also detect obfuscated APIs, the classification accuracy was only 61.6%. In [53], the authors presented a malware family classification system that was based on the instruction sequence. The researchers in [54] adopted supervised classification algorithms for ransomware family identification. Moreover, they considered the binary trees generated by these algorithms to infer the phylogenetic relationships. As emerged from the current state-of-the-art analysis, this paper represents the first attempt to introduce quantum Machine Learning in the malware detection research field. Moreover, this is the first paper devoted to introducing an explanation behind the decision made by a quantum Machine Learning classifier.
# An Improved Product Code-Based Data Hiding Scheme
## 6 Experimental Results
### Performance  
In Table 5, except the last solution, the embedding rate for each solution is equal to 33/49 (\(\approx\)0.6735) for fair comparison. In the original row-column scheme [5], after embedding in each row, only the first three columns are further used to embed data, whereas we adopt the first four columns in our implementation of [5] to enhance the embedding rate, but with larger distortion. The resultant algorithm is termed as Row-Column. Further, the one-off decision technique proposed in _Step 4_ of Appendix A.1.1 is used to reduce the distortion and the resultant algorithms are respectively termed as 1 DTS-1 and 1 DTS-2. In the original row-column scheme, \(R\) and \(D\) respectively equal 30/49 (\(\approx\)0.6122) and 0.212 for \(x=y=3\). When \(x=2\) and \(y=8\), \(R\) and \(D\) respectively equal about 0.6771 and 0.252. The performance in terms of embedding efficiency for this scheme and 1 DTS-2 are close to each other. For the former, they divided the pixels of cover image into disjoint matrix blocks of skew and large size 255 \(\times\) 3, whereas matrix block of small size 7 \(\times\) 7 is adopted in the latter one.  
From the results of Table 5, the embedding efficiency of the 2-D or hybrid 1-D/2-D Toggle Syndrome-based embedding scheme is much higher than that of the 1-D Toggle Syndrome-based embedding scheme. Hence, the former one is preferred. Our proposed scheme (2 DTS-1) reduces the average number of modification bits per embedding by 0.4938 when compared to [11]. That is, the average number of hidden bits per embedding modification is increased by 0.1149. Further, due to Property 3, the embedding efficiency can be significantly enhanced when combined with the strategy of Hamming+1.
## 3 The Proposed Downloading Scheme  
### Overview of Our Solution  
Our scheme for maximizing the satellite system throughput of downloading data is to transmit data between visible satellites via ISLs, so as to reduce the load of feeder links with insufficient bandwidth. In order to allocate the feeder link bandwidth resources, we must determine the amount of data rate this satellite can receive from others or need to transfer to others. To get the traffic demands, we calculate the sender and receiver of inter-satellite transmission. Then, a constrained MCMF routing algorithm is described to compute maximum-flow routing for all traffic demand needs inside a network's capacity area subject to routing cost limitations. Two linear programs (LPs) are required by the routing algorithm. The first LP finds a maximum flow between the visible satellites, and the second LP minimizes the routing cost given a fixed maximum flow. After one round of calculations, some feeder lines may have capacity surpluses, but others may have data waiting to be downloaded. Therefore, the above process needs to be iterated until all data from visible satellites can be downloaded to the ES or all feeder links have no spare capacity.