esa-sceva/satcom-chunk-collection huggingface dataset:
# Algorithm for Generating S-Boxes with Prescribed Differential Properties
## 1 Introduction
Note that in our search algorithm we focus on a single S-box criterion, the maximum value of the so-called Difference Distribution Table (DDT). We define the Difference Distribution Table (sometimes called the XOR table) of S-box \(S\) as a matrix \(\mathbf{D}^{(S)}\) over \(\mathbb{Z}\), with dimension \(2^{m}\times 2^{m}\). When \(S\) is known from the context, we simply write \(\mathbf{D}\). Rows of \(\mathbf{D}\) are indexed by vectors \(\mathbf{a}\in\mathbb{Z}_{2}^{n}\), while columns are indexed by vectors \(\mathbf{b}\in\mathbb{Z}_{2}^{m}\). An element at position \((\mathbf{a},\mathbf{b})\) has a value  
\[\mathbf{D}_{\mathbf{a},\mathbf{b}}^{(S)}=|\{\mathbf{x}\in\mathbb{Z}_{2}^{n}; S(\mathbf{x}\oplus\mathbf{a})\oplus S(\mathbf{x})=\mathbf{b}\}|.\]  
The differential profile of an S-box \(S\) is a multiset of values from \(\mathbf{D}^{(S)}\) for \(\mathbf{a}\
eq 0\). S-box \(S\) is differentially \(\delta\)-uniform if \(\delta=\max_{\mathbf{a}\
eq 0}\{\mathbf{D}_{\mathbf{a},\mathbf{b}}^{(S)}\}\), that is, \(\delta\) is the maximum value in the whole DDT excluding the case \(\mathbf{a}=0\). Our goal is to construct S-boxes with low differential uniformity, which are important in constructing ciphers resistant to differential cryptanalysis. It might be possible to generalize our algorithm for multiple criteria; however, we wanted to keep this research focused. We suspect that it would be too difficult to understand and analyze the method properly if we wanted to include multiple criteria. By investigating a single criterion, we can evaluate the complexity of the algorithm analytically (Section 4). We provide experimental results for small S-boxes in Section 5. Finally, we discuss our results and open research questions in Section 6.
## 2 Dataset and Methodology
### Dataset and Criteria
#### 2.3.6 Distance to Faults  
Seismic activity can significantly affect the stability and long-term viability of energy infrastructure. In regions like Gaziantep, which are located near active fault lines, the risk of earthquakes and ground movement must be accounted for when assessing land suitability for renewable energy projects. Gaziantep lies in a region that is susceptible to seismic activity due to its proximity to the East Anatolian Fault, which is one of the major active fault lines in Turkey. The fault line data from the GMRE's (General Directorate of Mineral Research and Exploration) active fault maps were used to determine the suitability of land for PV farms. Areas located more than 35 km away from fault lines were classified as very suitable for solar installations, while areas within 10 km of fault lines were considered unsuitable due to the increased seismic risk [52]. The distance to a fault was calculated using Euclidean distance analysis based on data. According to the calculations, areas located farther than 10 km from the faults were classified as suitable, with suitability increasing progressively as the distance increased.
## 3 Results  
### Characteristics of Spatiotemporal Change of Urbanization at 30 m Spatial Resolution  
According to the dataset of land use in 2005, 2010, 2015, and 2020, the urban land in Harbin was mostly distributed on the south side of the Songhua River (Figure 4). The change in urban expansion of Harbin city over the past 15 years showed that the built-up area of Harbin expanded relatively quickly between 2005 and 2010, and even faster between 2015 and 2020, which demonstrates the rapid economic growth.  
When the Sankey diagram (Figure 5) is coupled with the spatial distribution of LUCC (Figure 4), the built-up area expands by 66.96 km\({}^{2}\) at an average rate of 13.39 km\({}^{2}\) in 2005-2010. The cropland (106.71 km\({}^{2}\)) and waterbody (11.96 km\({}^{2}\)) have the highest fraction for land conversion and are the major land resources that urbanization has encroached upon. The urban converted from farmland areas and waters constituted about 84.71% and 9.5% of the total urban gain, respectively. The main expansion was in the form of westward expansion from the center, expanding in a radiating way, which mainly occurred in the Songbei and Dongli Districts (33.71 km\({}^{2}\), 30.02 km\({}^{2}\)). From 2010 to 2015, the pace of urban expansion keeps gradually stable, and the area of Harbin city expands by 12.39 km\({}^{2}\) at an average rate of 2.49 km\({}^{2}\). Only a total of 22.85 km\({}^{2}\) of land experienced changes, an increase of 15.46 km\({}^{2}\) in urban areas accounting for 67.64% of the total change in land use, of which, 70.69% and 19.60% resulted from cropland and waterbody, respectively. In 2015-2020, the urban experience remarkably increased on a rapid scale. A total of 192.74 km\({}^{2}\) of land was encroached upon by built-up land; the result from cropland was 156.22 km\({}^{2}\) (81.05%) and grassland was 16.27 km\({}^{2}\) (8.44%).  
Figure 4: Spatial distribution of built-up land changes in Harbin from 2005 to 2020.
# AgriSecure: A Fog Computing-Based Security Framework for Agriculture 4.0 via Blockchain
## 2 Overview of Agriculture 4.0
### Research Contributions  
The research contributions of the paper are as follows:  
* We go over the advantages of using IoT in the farming sector and outline some of the potential uses.
* We provide a layered approach for smart agriculture that can be applied to any precision agriculture application.
* We suggest an agricultural sensor data management system that can gather, analyze, visualize, and manage sensing data in real-time.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Layers in Agriculture 4.0** & **Recourses Used in Layers** & **Description of Layers** \\ \hline \multirow{4}{*}{Physical layer} & Sensors and Cameras & Collecting data from the environment \\ \cline{2-3}  & Actuators & Changing the state of the environment \\ \cline{2-3}  & RIFD & Storing the data \\ \cline{2-3}  & GPS & Tracking the location of the machinery \\ \hline Network Layer & Connecting resources such as Routers & Connecting remote devices to transform data \\ \hline \multirow{4}{*}{Edge Layer} & \multirow{4}{*}{Security, interfaces, gateway} & Uses Security protocols for ensuring data \\  & & integrity, confidentiality, etc., gateways \\ \cline{1-1}  & & connecting devices with the cloud to store a \\ \cline{1-1}  & & small amount of data \\ \hline \multirow{4}{*}{Application Layer} & \multirow{4}{*}{Database, End users, Web tools, etc.} & Storing data, exchanging information between \\ \cline{1-1}  & & the applications, and providing data accessing \\ \cline{1-1}  & & to the end users. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Resources used in the multi-layered architecture of IoT in Agriculture 4.0.  
* We present a blockchain-based authenticity monitoring technique to prevent erroneous control and information delivery.
* To improve network management, we proposed a simulated switch that supports SDN technologies.
* We present experimental results with different case studies from an open-source IoT platform integrating Ethereum blockchain and SDN technologies, demonstrating the efficacy of the suggested security architecture.
## 3 Results and Discussion
### Mineral Content

Table 2: Contents for minerals and trace elements for the dried seaweeds analyzed (_Palmaria palmata_ and _Ulva_ sp.) and for the SHGCs * after 30 days of cheese ripening. Phosphorus (P) levels ranged from 12.81 to 14.67 g\(\cdot\)kg\({}^{-1}\), with no significant differences observed among the SHGCs (\(p\) > 0.05), likely due to the low phosphorus content in the seaweeds used. Regarding trace elements, iron (Fe) levels ranged from 3.34 to 21.84 mg\(\cdot\)kg\({}^{-1}\), with significant differences between SHGC-C and SHGC-U, as well as SHGC-PP and SHGC-U (\(p\) = 0.0495). The higher Fe content in _Ulva_ sp. (331.81 mg\(\cdot\)kg\({}^{-1}\)) was reflected in SHGC-U. Iron deficiency is one of the most common nutritional deficits in developed countries [78], but seaweeds are an excellent source of iron, essential for health maintenance [77]. While dairy products like cheese are typically poor sources of iron [75], the inclusion of _Ulva_ sp. in this study significantly enriched the Fe content in the goat's cheese, with levels in _Ulva_ sp. being much higher than those found in _U. intestinalis_ (89 mg\(\cdot\)kg\({}^{-1}\)) and _U. lactuca_ (120 mg\(\cdot\)kg\({}^{-1}\)) [79]. Iodine (I) level was below the quantification limit in all SHGCs, despite _P. palmata_ presenting a moderate iodine concentration (48.38 mg\(\cdot\)kg\({}^{-1}\)). The low iodine content in the supplemented cheeses may be attributed to the small percentage (2%) of seaweed used in supplementation. Manganese (Mn) levels ranged from 0.83 to 2.50 mg\(\cdot\)kg\({}^{-1}\), with significant differences observed among all samples (\(p\) = 0.0495). _P. palmata_ (5.72 mg\(\cdot\)kg\({}^{-1}\)) and _Ulva_ sp. (37.94 mg\(\cdot\)kg\({}^{-1}\)) contributed to the Mn content of SHGC-PP (1.21 mg\(\cdot\)kg\({}^{-1}\)) and SHGC-U (2.50 mg\(\cdot\)kg\({}^{-1}\)), respectively. Mn is essential for the metabolism of proteins, lipids, and carbohydrates [80], making its presence in the cheese nutritionally important. Selenium (Se) levels ranged from 1.93 to 2.17 mg\(\cdot\)kg\({}^{-1}\), with no significant differences among the samples (\(p\) > 0.05), likely due to the low selenium content in both seaweeds (1.20-1.74 mg\(\cdot\)kg\({}^{-1}\)). Zinc (Zn) concentrations ranged from 22.77 to 26.89 mg\(\cdot\)kg\({}^{-1}\), showing significant differences between SHGC-C and SHGC-PP, as well as between SHGC-PP and SHGC-U (\(p\) = 0.0495). Seaweeds, particularly brown (class _Phaeophyllceane_) and red (phylum _Rhodophyta_) species, are known to be rich in Zn, with concentrations reaching up to 700 mg\(\cdot\)kg\({}^{-1}\), depending on the species and geographic region [80]. Although _Ulva_ sp. contained a lower Zn level (23.28 mg\(\cdot\)kg\({}^{-1}\)) compared to _P. palmata_ (24.77 mg\(\cdot\)kg\({}^{-1}\)), SHGC-U (26.89 mg\(\cdot\)kg\({}^{-1}\)) had a higher Zn level than SHGC-PP (22.77 mg\(\cdot\)kg\({}^{-1}\)). A slight increase in Zn content was observed in the SHGCs supplemented with seaweeds, with a significant increase in SHGC-PP.
## 5 Challenges of CV in Smart Agriculture
### Socio-Technical and Economic Barriers  
Beyond technical hurdles, the widespread adoption of CV and GenAI in agriculture is profoundly constrained by socio-technical and economic barriers. The assumption that technological availability leads to adoption overlooks the complex on-farm reality, particularly in smallholder contexts, which represent over 90% of the world's farms [310]. These barriers are often more decisive than purely computational challenges.  
A primary concern is data sovereignty and ownership. As farms become data sources, questions arise over who controls this information--the farmer, the technology provider, or corporate aggregators. For smallholder farmers, this is especially critical. There is a significant risk of data exploitation, where aggregated data on soil quality, yield, and practices could be used to their disadvantage in market negotiations or for land speculation. Without equitable data governance models, such as farmer-led data cooperatives, the benefits of AI may flow away from the farmers who generate the foundational data, exacerbating existing power imbalances.  
Furthermore, significant economic and infrastructural gaps limit accessibility. The high upfront costs of hardware like drones and sensors and the recurring expenses for software and data processing present formidable barriers for farmers with low profit margins. The return on investment is often uncertain and long-term, making it a risky proposition. This is compounded by the digital divide, including a lack of reliable Internet connectivity and the technical literacy required to operate and maintain these sophisticated systems in many rural areas. Consequently, there is a risk of creating a two-tiered agricultural system, where large, well-capitalised farms advance while smallholders are left behind. Addressing these socio-technical challenges through inclusive design, fair data policies, and affordable, context-appropriate technology is therefore as crucial as algorithmic innovation for the equitable and sustainable future of smart agriculture.
# A Graph Convolutional Network-Based Method for Congested Link Identification
## 4 Performance Evaluation
### Performance Evaluation Results
We systematically varied the network size by adjusting the number of nodes, denoted as \(N\), which varied from 2 to 16, and observed the impact on the DRs and FPRs of the different methods. For each network size, we conducted multiple simulations by iteratively changing \(\delta_{l}\) and \(\delta_{n}\) from 0.0 to 0.3 with a step size of 0.1. The results, summarized in Figures 16 and 17, reveal several interesting insights. First, the mean DR values achieved by the GIC method remained relatively stable across different network sizes, indicating its robustness to changes in topology and scale. In  
Figure 16: DR versus different network size. Figure 15: Schematic diagram of binary tree network topology. contrast, the DR values of SCFS, CLINK, and RIC decreased as the network size increased, with SCFS showing the most significant decline. This finding aligns with previous experimental results reported in [35], which also observed a reduction in the DR values as the network size expanded. The stability of the GIC method can be attributed to its ability to effectively identify and isolate faults in complex network topologies, regardless of their size. This suggests that the GIC method may be more suitable for large-scale networks, where the impacts of the topology and size on diagnosis performance are more pronounced.
# Real-Time Anomaly Detection in Cold Chain Transportation Using IoT Technology
## 3 Materials and Methods
### The Network Layer: Communication Protocol
That is, the solution employed should not rely on a piece of hardware, or gateway, that provided network coverage in a limited area around its deployment location but rather a national-wide network coverage. Unfortunately, neither Sigfox or LoRa have the required national-wide coverage and thus would not be appropriate for the communication stack for the IoT solution being designed. Instead, a cellular telecommunication-based protocol would be required. The other two popular LPWAN options that were considered were the Narrowband Internet of Things (NB-IoT) and Long-Term Evolution Machine (LTE-M). NB-IoT is part of the 3 rd generation partnership project (3 GPP) LTE specifications and shares several technical components with LTE. It can coexist with GSM and LTE under licensed frequency bands. Its main advantages are low power consumption, extended coverage and deep penetration [79]. The LTE-M network is derived from the 3 GPP 4G LTE standard. Likewise, it also offers a low power consumption, long range and deep penetration. In fact, both NB-IoT and LTE-M share considerable similarities, with their main difference residing in the operational bands they use. These technologies were considered suitable candidates for the validation stage of the system as they were both available in northern Ireland. While both protocols offer a high degree of compatibility with standard 4G LTE and 5G networks, LTE-M was finally chosen as the communication protocol as it offered the added advantages of a range of bandwidth, latency, and mobility [80].
# The Bimodal Neutron and X-ray Imaging Driven by a Single Electron Linear Accelerator
## 4 Discussion
### The Influence of Counting Statistics on Identifying the Material  
Figure 10 presents the bivariate histograms of the inspected turbine blade with various measurement times. As the turbine blade made of nickel is free of a gadolinium tracer, in principle the slope (Appendix A.6), which is the ratio between \(\mu_{m,n}t_{m}\) and \(\mu_{m,X}t_{m}\) in each histogram, should be a certain value. However, the statistical error contaminates the results  
Figure 9: (**a**) Fused image of the turbine blade; (**b**) the distributions of \(\mu_{m,n}/\mu_{m,X}\) for turbine blades with or without gadolinium tracer, for six different positions of the turbine blade (selected region is 40 \(\times\) 40 pixels); (**c**)\({}_{\text{(1)}}\) the bivariate histograms for turbine blades with gadolinium tracer and (**c**)\({}_{\text{(2)}}\) without gadolinium tracer. Pixel density is the number of pixels in the area of 0.0075 \(\upmu\times\) 0.0075 \(\upmu\)t.  
and spreads the two-dimensional distributions in the histograms. The value of the slope in the bivariate diagram is as follows:  
\[slope=\frac{Att_{n}}{Att_{X}}=\frac{\ln\left(\frac{I_{0,n}}{I_{n}}\right)}{\ln \left(\frac{I_{0,n}}{I_{X}}\right)}=\frac{\mu_{m,n}\cdot t_{m}}{\mu_{m,X}\cdot t _{m}}=\frac{\mu_{m,n}}{\mu_{m,X}} \tag{1}\]  
where \(Att_{n}\) and \(Att_{X}\) are the logarithm of the attenuation of neutrons and X-rays, respectively; \(I_{0,n}\) and \(I_{n}\) are the numbers of incident and transmitted neutrons, respectively; and \(I_{0,X}\) and \(I_{X}\) are that for X-rays, respectively. As the mass attenuation coefficient is the ratio of the microscopic cross section with the mass of the nucleus (for neutron) or the atom (for X-ray), this equation can be further expressed as follows:  
\[slope=\frac{Att_{n}}{Att_{X}}=\frac{\mu_{m,n}}{\mu_{m,X}}=\frac{\frac{\sigma_{ n}}{m_{nucleus}}}{\frac{\sigma_{X}}{m_{atom}}}\approx\frac{\sigma_{n}}{ \sigma_{X}} \tag{2}\]  
where \(\sigma_{n}\) and \  
A 60-minute measurement then leads to a fractional error of 4.91% for the 5-millimeter-thick nickel. This error can be further reduced when more than one pixel can be involved to realize even better counting statistics, which is possible when the ROI (region of interest) to be identified consists of many pixels.
# Semi-Supervised Learning for Robust Emotional Speech Synthesis with Limited Data
## 4 Experiment and Results
### Objective Evaluation  
The objective evaluation of this paper selects the Mel-Cepstral distortion (MCD) method. The calculation formula of MCD is shown in Equation (10), and the calculation result reflects the distance between the spectral features of the experimentally synthesized speech and the real speech of the target, and the MCD defined as an extension of the simple Euclidean parametrization. The smaller the MCD value, the higher the spectral similarity between the two, and the better the effect of the synthesized speech.  
\[\text{M}CD[db]=\frac{10\sqrt{2}}{\ln 10}\frac{1}{M}\sqrt{\sum_{m=1}^{M}(y_{m}-y_{ m}^{\prime})^{2}}. \tag{10}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Data Size** & \(\mathbf{\theta}\) & **MOS (Neutral)** & **WER(\%)** \\ \hline
30 & 0 & 2.65 \(\pm\) 0.20 & 2.5 \\
100 & 0 & 3.23 \(\pm\) 0.18 & 2.0 \\
300 & 0 & 3.85 \(\pm\) 0.07 & 1.5 \\ \hline
30 & 0.1 (BL) & 3.20 \(\pm\) 0.28 & 1.5 \\
100 & 0.1 (BL) & 3.49 \(\pm\) 0.22 & 0.5 \\
300 & 0.1 (BL) & 4.14 \(\pm\) 0.10 & 0.2 \\ \hline
30 & 0.3 & 3.14 \(\pm\) 0.16 & 2.0 \\
100 & 0.3 & 3.93 \(\pm\) 0.35 & 1.0 \\
300 & 0.3 & 3.96 \(\pm\) 0.09 & 0.3 \\ \hline \hline \end{tabular} Note: \(\pm\) is the standard deviation symbol.  
\end{table}
Table 4: Numerical comparison of models under different dataset sizes.  
Figure 4: EMOS of EGST, ETaco2, and SMAL-ET2.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Emotion** & **EGST** & **ETaco2** & **SMAL-ET2** & **Ground Truth** \\ \hline Neutral & 3.15 \(\pm\) 0.31 & 3.52 \(\pm\) 0.23 & 3.97 \(\pm\) 0.28 & 4.81 \(\pm\) 0.16 \\ \hline Sad & 3.32 \(\pm\) 0.29 & 3.55 \(\pm\) 0.21 & 3.77 \(\pm\) 0.15 & 4.83 \(\pm\) 0.19 \\ \hline Happy & 3.47 \(\pm\) 0.16 & 3.61 \(\pm\) 0.09 & 4.15 \(\pm\) 0.19 & 4.94 \(\pm\) 0.09 \\ \hline Surprise & 3.19 \(\pm\) 0.27 & 3.43 \(\pm\) 0.30 & 3.96 \(\pm\) 0.24 & 4.86 \(\pm\) 0.07 \\ \hline Angry & 2.92 \(\pm\) 0.20 & 3.34 \(\pm\) 0.17 & 3.88 \(\pm\) 0.25 & 4.81 \(\pm\) 0.13 \\ \hline \hline \end{tabular} Note: \(\pm\) is the standard deviation symbol.  
\end{table}
Table 5: MOS Results with 95% Confidence Intervals.  
where \(y_{m}\) and \(y^{\prime}_{m}\) are the Mel-Cepstral coefficients (MCEPs) of real speech and synthesized speech, respectively. \(M\) indicating the dimension of MCEPs.  
From Figure 6, we can see that the MCD value shows a decreasing trend, which indicates that the emotional speech synthesized by the method proposed in this paper is also closer to the real target GT.
# Aerostat-Based Observation of Space Objects in the Stratosphere
## 2 Astronomical Observation Conditions in Stratosphere
### Aerosols  
Aerosols are the dominant factor of sunlight/moonlight scattering, whose influence on the sky background also needs to be considered. The distribution of aerosols in the atmosphere with height is different from the trend of atmospheric density, which decreases whilst height increases. Figure 4 gives the vertical distribution [10] of the measured aerosol quantity density and mass concentration. These data were obtained from the in situ vertical distribution measurement experiment by the Institute of Atmospheric Sciences, Chinese Academy of Sciences (CAS) in 2018 in Golmud, Qinghai province, by carrying a portable optical particle spectrometer (Portable Optical Particle Spectrometer, POPS) on high-altitude balloons. This figure shows that there is a clear peak volume of the aerosol number density from 14 km to 18 km. At 18 km and 20 km, the aerosol concentration was about 0.05 \(\upmu\)g m\({}^{-3}\) and 0.02 \(\upmu\)g m\({}^{-3}\), respectively, which are very low levels and no  
Figure 3: The magnitude of the sky background in visible and near-infrared bands on the ground and in the stratosphere (20 km) at 02:00:00 (UTC) around the summer solstice with LOWTRAN parameters: the visibility is set to 23 km; the sun is located at the azimuth of 109\({}^{\circ}\); and the attitude is 56\({}^{\circ}\).  
longer change significantly when height increases. Therefore, the influence of aerosol on astronomical observations in the stratosphere can be neglected.
# Modeling and Calculation of Limit Magnitude Detection of Orbital Optoelectric Tracking System
## 4 Calculation and Experiment Analysis
### Computation Analysis
Therefore, under strong light conditions, a light filter is needed to attenuate the influence of the strong background light on the limit magnitude detection in order to improve the tracking performance. For Equation (29), taking \(N_{x}=N_{y}=256\) and \(L=1.49597892\times 10^{8}\) km, the changing curve between the limit magnitude detection and the sunlight incident direction under illuminated conditions is obtained, as shown in Figure 4:  
As can be seen from Figure 4, since the sunlight is relatively strong, just a little sunlight incident greatly reduces the limit magnitude detection of the system. Therefore, the system  
Figure 4: The relation between the limit magnitude detection and the angle between the optical axis of the system and the sunlight incident direction. Figure 3: The relationship between the limit magnitude detection and the influence factors: **(a)** The relationship of the limit magnitude detection and the SNR threshold \(K_{\rm min}\); **(b)** the relationship of the limited magnitude detection and the exposure time; **(c)** the relationship of the limited magnitude detection and the dark current; and **(d)** the relationship between the limited magnitude detection and the background illumination. basically can only adopt the backlight working mode or work in the ground shadow. Especially when the background light is enhanced, the limit magnitude detection decreases significantly. Therefore, the system, when under strong illumination conditions, needs to take measures to increase the light filter to weaken the influence of the strong background light on the limit magnitude detection in order to improve the tracking performance.