esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Applications of Cold Atmospheric Pressure Plasma
### Food Industry and Agriculture
Newcastle disease and avian influenza are the two most common devastating diseases among poultry (chicken, turkey and duck). It has been already reported that CAPP can be used as an effective and safe agent for inactivated vaccine preparation against viruses [121]. The study of [PERSON] et al. indicates that virulent Newcastle disease can be inactivated using plasma-activated solutions (H\({}_{2}\)O, NaCl, H\({}_{2}\)O\({}_{2}\)). The efficiency of the inactivation was confirmed using embryo lethality assay and hemagglutination tests. This disease affects bird species and has a large economic impact on the domestic animal and poultry industries; furthermore, it is transmissible to humans [122]. Plasma-based inactivation of prions (proteinaceous infectious particles), which belong to one of the most resistant pathogens, is also under research. Most prion diseases affect the nervous system of humans (Creutzfeldt-Jakob disease) and animals (bovine spongiform encephalopathy in cattle, chronic wasting disease in cervids). Prion diseases have long and silent incubation periods, once the symptoms emerge, these disorders are rapidly progressive and usually fatal [16]. Key benefits of utilizing CAPP in the food and agriculture sectors include minimal water and power usage, operation at ambient temperature, and short treatment duration (from seconds to minutes). Furthermore, its use is free of hazardous solvents, it reduces preservative use and it is applicable for both solid as well as liquid phases [10,16]. Currently available data confirm that CAPP technology could be utilized in the agriculture and food industry [22,99]. To achieve broad applicability, an efficient open-air device suitable for the treatment of both large areas and high number of samples is necessary [16]. An ideal industrial scale device allows homogenous disinfection of various products during the sorting process on rolling electrodes [23]. It is important to avoid quality degradation or any undesired effects of plasma treatment. In addition, more intense optimization studies are needed to fully understand the mechanisms behind the pathogen-plasma interactions. There is a need for standardization of the plasma dose (treatment duration is not an appropriate standardized unit because of the diversity of food products and plasma reactors) [11; 25]. CAPP treatment limitations are also necessary to account for before consumer acceptance can be achieved [10; 11; 109].
## 6 Strengths, Limitations, and Future Research Directions
### Limitations
#### 6.2.4 Evaluation  
As mentioned above, CNNs are primarily designed for local feature extraction and may struggle to capture long-range contextual dependencies, which can limit the evaluation performance in tasks that require understanding of global context or long-term dependencies. ViTs are relatively newer than CNNs, and as such, may lack well-established benchmarks or evaluation metrics for specific tasks or datasets, which can make performance evaluation difficult and less standardized.
# HRU-Net: High-Resolution Remote Sensing Image Road Extraction Based on Multi-Scale Fusion
## 5 Conclusions  
In summary, this article proposes a road extraction model called the HRU-Net, which is based on the deep learning convolutional neural network model and adopts a \"U\" network architecture. The model combines the upsampling multi-scale feature fusion module (UMR module) and the multi-feature fusion module (MRF module) to enhance its ability to extract roads from high-resolution remote sensing images. Also, we implemented a new loss function to decrease the problem of class imbalance in our datasets and improved the result of road segmentation.  
The multi-scale fusion module in the HRU-Net performs upsampling on inputs with different resolutions in the encoding stage and fuses feature maps of various scales for convolution in the decoding stage. By maintaining multi-scale information in parallel and preserving the parallelism of low-resolution and high-resolution feature maps, the network repeatedly exchanges information among different resolutions throughout its operation. This process enables the better fusion of multi-scale semantic information, the extraction of global multi-scale features, and the improvement of the network model's expression ability.  
The proposed HRU-Net model demonstrated significant performance improvements when tested on the Massachusetts Road Dataset and the DeepGlobe Road Dataset. Its ability to effectively extract roads from high-resolution remote sensing images makes it a valuable tool for various road extraction tasks and applications.  
Conceptualization, Z.Y.; Methodology, C.R. and X.X.; Validation, W.Y.; Formal analysis, X.Z.; Investigation, X.L.; Writing--original draft, A.Y.; Visualization, Z.W.; Project administration, J.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (Grant No. 42064003). The author is located at the Guilin University of Technology.  
Not applicable.  
The authors declare no conflict of interest.
## 3 Results and Discussion  
This section is essentially devoted to present and discuss an experimental study of the sub-THz response of strained-Si MODFETs under front and back illumination conditions. It is found that at 0.3 THz the response of the transistor has almost no influence of the bonding wires and, at this frequency, in contrast with the response under radiation of 0.15 THz, back illumination produces a stronger response than front illumination (the one conventionally used).
# Model Test on the Behaviors of Deep Excavation with Lateral Confined Water
## 3 Results
### Analysis of Strut Axial Force  
The diachronic curves of strut axial force for each strut are shown in Figure 11. The axial force sign convention is defined such that compression is positive and tension is negative. Overall, the second level of strut experiences the highest axial force throughout the test (600-1000 N), followed by the third and fourth levels of struts (300-500 N), while the first level of strut experiences the smallest axial force, fluctuating around 100 N. After the first level of strut was installed (Z1), its axial force reached 80 N, which increased to 120 N following the excavation of the second soil layer (K2). After the second level of strut was installed (Z2), the axial force in the first level of strut decreased to 66 N, while the axial force in the second level of strut increased to 740 N. Based on the principles of force transmission and load distribution, it is evident that the second level of strut effectively shared the lateral pressure. Following the excavation of the third soil layer (K3), the axial forces in the first and second levels of struts increased to 129 N and 784 N, respectively, as the diaphragm wall experienced inward horizontal displacement due to unbalanced soil and water pressures. After the installation of the third level of strut (Z3), the axial forces in the first and second levels of struts significantly decreased to 58 N and 587 N, respectively, indicating that the third level of strut played a crucial role in sharing the lateral pressure  
Figure 10: Pore water pressure in each test stage: (**a**) left side; (**b**) right side.  
of the diaphragm wall, and its axial force was 317 N. After the excavation of the fourth soil layer (K4), as the excavation reached the depth of the lateral confined aquifer, extreme pressure imbalance occurred between the inside and outside of the excavation, causing a substantial increase in axial forces in the first three struts, with increases of 80 N, 351 N, and 158 N, respectively. Following the installation of the fourth level of strut (Z4), its axial force increased from 292 N to 349 N, but the changes in the axial forces of the other struts were relatively minor. When the excavation reached the bottom and the lateral confined water level was raised to 432 mm (F), the axial force in the fourth strut experienced a noticeable change, increasing from 349 N to 528 N, while the axial forces in the other struts showed no significant change.  
In conclusion, the magnitude of the axial forces in the struts is closely related to whether the excavation has reached the depth of the lateral confined aquifer. After the excavation reaches this depth, the axial forces in all struts increase significantly. Additionally, the increase in lateral confined water level has the most pronounced effect on the struts within the depth range of the confined aquifer, significantly increasing the strut axial force.
## 2 Experimental Methods
### Characterization Methods  
The topography of the silicon template was imaged by an optical microscope (NX30-HK830, AOSV, Shenzhen, China). The micropillar arrays and cross-sectional porous microstructures were characterized by a scanning electron microscope (SEM, Ultra 60 VP, Zeiss, Germany). The cross-section of the foil was obtained through the brittle fracture of foil by liquid nitrogen, as described in [35]. Pore size distribution were analyzed by the software ImageJ in SEM images. Three samples fabricated at different foaming conditions were imaged, and on each sample, five arbitrary positions in the central region of the cross-section were investigated. The pore dimensions in each image were accordingly measured and statistically analyzed, resulting in an average pore size for each experiment. From these averages, a mean value was determined to characterize the pore size obtained at a specific foaming condition. The porosity was calculated as the ratio of the pore volume to the volume of the foamed sample from micro-computed tomography (Xm-Tracer-uCT-90, IHEP, Beingjing, China). The diameter and depth of the post holes in the silicon template were determined by a laser microscope (Olympus LEXT OLS5100, Olympus Co., Ltd., Tokyo, Japan). The water or oil contact angle on the foil was measured by a SINDIN contact angle instrument (SDC2111071, Shengding Precision Instrument Co., Ltd., Dongguan, China). The droplet volume used in the contact angle measurements was 1 uL.
# Reversible Data Hiding Scheme in Homomorphic Encrypted Image Based on EC-EG
## 3 Proposed Scheme
### Data Extraction  
#### 3.6.1 Extract the Embedded Data from Encrypted Image  
As shown in Figure 5h,i, when having the shared parameter \(n\), \(A_{r,map}\), the private key \(sk\) and the hidden key \(k_{2}\), the receiver extracts additional data from the received encrypted image with hidden data by using homomorphic subtraction in ciphertexts. The steps are as follows:  
Step 1. The received image is divided into blocks in the same way as the process of room reserving. As shown in Figure 5e, the legal receiver extracts the square grid with hidden data.  
Step 2. In the MCC, the encrypted target pixels with hidden data are \(M(c_{t}^{{}^{\prime\prime}})=M(c_{r})+M(c_{S_{w}})\). \(M(C_{S_{w}})\) is extracted by the homomorphic subtraction, which is \(M(c_{S_{w}})=M(c_{t}^{{}^{\prime}})-M(c_{r})\).  
Step 3. Use \(M(S_{w})=M(c_{S_{w}})+r_{S_{w}}pk-skr_{S_{w}}G\) to decrypt \(M(C_{S_{w}})\) to \(M(S_{w})\) and decode to get \(S_{w}\).  
Step 4. The receiver can use \(w_{i}=\left\lfloor\frac{S_{w}}{2^{i}}\right\rfloor,i=0,1,\cdots,n-1\) to get \(w_{0},w_{1},\cdots,w_{n-1}\), and then use the hidden key \(k_{2}\) to perform scrambling recovery. Finally, we can extract additional data.
## 2 System Working Principle
### Working Principle of the APT System
\tag{5}\]  
According to the Formula (5), the scanning trajectory of the two-dimensional mirror is obtained by simulation, as shown in Figure 6:  
The system uses the position of the light spot on the four-quadrant detector as feedback information to adjust the pitch angle and azimuth angle of the two-dimensional mirror. When the light spot reaches different quadrants, the position information of the light spot is calculated according to the voltage value of each quadrant on the surface of the detector, that is, the offset of the light spot in the \(x\)-\(y\) direction \(D_{x}\), \(D_{y}\), and the calculation formula of the light spot position is shown in Formula (6) [18]:  
\[\left\{ \begin{array}{l}D_{x}=\lambda_{x}\dfrac{(V_{A}+V_{B})-(V_{C}+V_{D})}{ V_{A}+V_{B}+V_{C}+V_{D}}\\ D_{y}=\lambda_{y}\dfrac{(V_{A}+V_{C})-(V_{B}+V_{D})}{V_{A}+V_{B}+V_{C}+V_{D}} \end{array} \right. \tag{6}\]  
In the formula, \(\lambda\) is a proportional coefficient, and its size is related to the energy distribution and size of the spot. Its function is to convert the value in Formula (6) into the distance between the spot and the center of the four-quadrant detector, and \(V\) is the voltage value of each quadrant. After obtaining the position information of the spot, calculate the offset of the spot from the center of the four-quadrant detector as \(\Delta D_{x}\), \(\Delta D_{y}\), and calculate the deflection angle of the beam, as shown in Formula (7) [19; 20]:  
\[\left\{ \begin{array}{l}\theta=\lambda_{x}\cdot\arctan\dfrac{\sqrt{\Delta D _{x}^{2}+\Delta D_{y}^{2}}}{d}\\ \phi=\lambda_{y}\cdot\arctan\dfrac{\Delta D_{x}}{\Delta D_{y}}\end{array} \right. \tag{7}\]  
Figure 6: Scanning trajectory of the beam emitted from a two-dimensional mirror. In the formula, \(\theta\) the pitch angle \(\phi\) is the azimuth angle, and \(d\) is the distance from the receiving surface to the photosensitive surface of the detector. This formula expresses the corresponding relationship between the experimental measurement value and the actual value. Assuming that the error angles corresponding to the azimuth direction and the pitch direction are \(x_{i}\) and \(y_{i}\) respectively, for \(n\) measurements, the radial angle deviation \(r_{i}\) and its average value \(\overline{r}\) can be expressed as:  
\[r_{i}=\sqrt{x_{i}^{2}+y_{i}^{2}},\ \overline{r}=\frac{1}{n}\underset{i=1}{\overset{n} {\sum}}r_{i} \tag{8}\]  
Then the \(3\sigma\) alignment accuracy can be expressed as:  
\[\epsilon=\overline{r}+3\sqrt{\frac{1}{n-1}\underset{i=1}{\overset{n}{\sum}}(r _{i}-\overline{r})^{2}} \tag{9}\]
# Micro-Location Temperature Prediction Leveraging Deep Learning Approaches
###### Abstract  
Nowadays, technological progress has promoted the integration of artificial intelligence into modern human lives rapidly. On the other hand, extreme weather events in recent years have started to influence human well-being. As a result, these events have been addressed by artificial intelligence methods more and more frequently. In line with this, the paper focuses on searching for predicting the air temperature in a particular Slovenian micro-location by using a weather prediction model Maximus based on a long-short term memory neural network learned by the long-term, lower-resolution dataset CERRA. During this huge experimental study, the Maximus prediction model was tested with the ICON-D2 general-purpose weather prediction model and validated with real data from the mobile weather station positioned at a specific micro-location. The weather station employs Internet of Things sensors for measuring temperature, humidity, wind speed and direction, and rain, while it is powered by solar cells. The results of comparing the Maximus proposed prediction model for predicting the air temperature in micro-locations with the general-purpose weather prediction model ICON-D2 has encouraged the authors to continue searching for an air temperature prediction model at the micro-location in the future.  
Keywords:long short-term memory neural networks; air temperature; micro-location; prediction; weather; Internet of Things +
Footnote †: journal: Applied sciences
###### Abstract
Organic PCMs are poor thermal conductors, so thermal conductivity improvement is fundamental for fast charging and discharging rates, which are critical issues when PCMs are applied for the thermal management of electronic devices and batteries [24]. Thermal conductivity in organic PCMs can be enhanced by adding carbon substances such as graphene [25], graphene oxide [26], and expanded graphite [27; 28; 29]. They can rely on their excellent porous structure, which allows for the efficient absorption of PCMs, making possible the creation of a highly stable PCM-based composite. Graphene and graphene oxides, notwithstanding their excellent properties, have been often discarded due to the high cost, in favor of expanded graphite (EG). EG has become of huge interest in the realization of PCM-based composites due to its low density and high specific surface area, which creates a thermal conductive network, which additionally provides shape stabilization and limited leakage [30; 31; 32]. This stabilizing effect of EG is crucial to restrict the main technological limitation in the use of PCMs, which is the need to confine them once molten. Many solutions have been proposed, like macro- [33] or micro-encapsulation [34]. These are valid strategies but they do not increase the thermal conductivity and macro-encapsulation has to deal with the large volumetric expansion of PCMs upon melting, while micro-encapsulation is a fairly expensive process. Moreover, the micro-encapsulated PCM must be dispersed into a polymeric matrix, such as one made up of thermoplastics [35; 36], acrylic resins [37], or elastomers [38], but this process can be performed also with neat PCMs, obtaining higher melting enthalpy composites. For these reasons, carbon-based structures are preferred when a high PCM content is needed. In a previous study [39], for the first time, we impregnated EG with a fatty acid mixture under vacuum conditions and the obtained powder was cold-compacted to realize shape-stabilized panels. We found that a 14 EG parts per hundred ratio (phr) of PCM is the optimum amount for leaking prevention and thermal conductivity enhancement, obtaining isotropic thermal conductivity values not reached before in the literature. However, the considered PCM melted at 50-55 \({}^{\circ}\)C and we verified that, to maximize the thermal management efficacy in PV systems at a north-Italian latitude, a lower melting point (around 40-45 \({}^{\circ}\)C) is preferable. For this reason, in the present study, we focus on lower-melting-point PCMs, in order to maximize the thermal management action, intending to obtain EG-based passive cooling systems to be coupled with crystalline silicon PV cells, which can be applied to either new plants or existent ones.
## 1 Introduction
* We propose a novel covert communication method by utilizing MQTT messages to build a storage channel for secure data exfiltration. * We provide an analysis of the overhead introduced by our solution both in terms of time and bytes sent. Overall, the overhead required appears to be acceptable. * We provide a security analysis of the proposed approach. This analysis shows that our solution offers security guarantees in scenarios with network monitoring and censorship without raising suspicion due to the common usage of the MQTT protocol in IoT environments. The remainder of this document is structured as follows. In Section 2, we review the relevant literature on covert communication methods. In Section 3, we provide background information on MQTT. Section 4 describes the motivations and potential use cases for our approach. Section 5 presents our approach. Then, we illustrate our approach by referring to a numerical example in Section 6, while in Section 7, we provide an analysis of the overhead required by our solution. In Section 8, we analyze the security of the proposed approach. Finally, Section 9 concludes the paper.
## 2 Materials and Methods
### Sample Collection and Determination  
The sampling time points were at 60 days, 90 days, 120 days, and 150 days after seeding. Decay sample bags and soil samples were collected for each treatment. Each sampling event involved taking a single sample bag from each straw strip in every plot, totaling 36 sample bags each time. These samples were then sorted into plastic bags based on their labels and transported to the laboratory. The straw samples were cleaned and dried in an oven at 65 \({}^{\circ}\)C until a constant weight was achieved. Soil samples were collected from the 0-20 cm soil layer beneath the straw strips in each plot using a five-point sampling method. The soil samples were sealed, stored, and later brought back to the laboratory. Some soil samples were kept fresh in a refrigerator at 4 \({}^{\circ}\)C, while others were air-dried naturally.  
The extraction and determination of humus-like substances in soil were carried out using a modified method based on soil humus composition [28]. The sample humus was categorized into water-soluble substances (WSSs), humin-like (HML), humic-like acid (HLA), and fulvic-like acids (FLAs). The carbon content of each component was analyzed using the potassium dichromatic oxidation method, with the carbon content of FLA calculated using a subtraction method as per the following formula:  
\[\text{FLA}=\text{HLE}-\text{HLA} \tag{1}\]  
\[\text{PQ}=\text{HLA}/(\text{HLA}+\text{FLA}) \tag{2}\]  
Soil humus composition was altered by various components including humic acid (HAC), fulvic acid (FAC), humic matter (HE), water-soluble matter (WSS, this parameter is a water-soluble substance in soil, which is different from WSS in humus-like substances), and humin (HM). The carbon content of WSS, HE, and HA was determined using the external heating method of potassium dichromate, while the carbon content of FAC was calculated using the difference subtraction method:  
\[\text{PQ}=\text{HAC}/\text{HE} \tag{3}\]  
\[\text{\Delta logK}=\text{logK400}-\text{logK600} \tag{4}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Organic Materials** & **Total N (g/kg)** & **Total P (g/kg)** & **Total K (g/kg)** & **Organic Carbon (g/kg)** \\ \hline Chicken manure & 17.40 & 8.56 & 14.23 & 261.30 \\ Cattle manure & 10.20 & 3.40 & 8.22 & 304.60 \\ Maize straw & 8.70 & 1.23 & 12.49 & 485.96 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Nutrient content of organic materials.  
Soil organic carbon was heated externally using potassium dichromate [29]. Easily oxidized organic carbon in the soil was measured using the 333 mmol/L potassium permanganate oxidation method [30]. Soil microbial biomass carbon was assessed using the chloroform fumigation-K\({}_{2}\)SO\({}_{4}\) extraction method [31]. Soluble organic carbon in the soil was determined through the 0.5 mol/L K\({}_{2}\)SO\({}_{4}\) extraction method [29]. Particulate organic carbon in the soil was analyzed using the sodium hexametaphosphate dispersion method [32].