esa-sceva/satcom-chunk-collection huggingface dataset:
# Traffic Offloading in Unlicensed Spectrum for 5G Cellular Network: A Two-Layer Game Approach
## 2 System Model  
### The System Model of the Whole Network  
As illustrated in Figure 1, we consider a coexisting network of one LTE-LAA macrocell and \(\mathcal{K}=\{1,\ldots,K\}\) WiFi APs. These APs are overlaid on the coverage of the macrocell. Both the macrocell and the APs share the same unlicensed spectrum around 5 GHz and there is no interference between the macrocell and the APs due to the use of CSAT mechanism. We assume that WiFi users are also equipped with LTE transceivers and the number of WiFi users in the \(k\)-th AP is \(N_{k}\). To facilitate the coexistence of WiFi and LAA, we further assume that there exists an inter-system coordinator, which performs the WiFi user transfer and unlicensed resource allocation, as in [24]. APs in a coalition can transfer stations from each other and enable devices to exchange information and perform actions without human intervention [22]. To implement this mechanism, a modified frame structure is used and managed by a centralized controller [25]. In addition, the APs may share the same channel with other internal/external APs and each AP covers a certain service area which may overlap with the service area of other APs. Each station can be associated only with one single AP at a time. The traffic arrival rate of a user is assumed to be known and the users have the same arrival rate. Furthermore, the time period in the unlicensed spectrum will be divided into several time slots, some for WiFi and the others for LAA. This can be accomplished by the CSAT method in [13; 14] as controlled by the inter-system coordinator. In addition, the ratio of \"on\" to \"off\" can be adjusted adaptively to meet the needs of the different operators' traffic load.
# Parallel Crossed Chaotic Encryption for Hyperspectral Images
## 3 Encryption for Hyperspectral Images  
One of the main disadvantages of using chaotic encryption is that we have to simulate the chaotic system until we have the same amount of information than the one to be encrypted. For this reason,  
Figure 2: MPLCM simulation.  
the chaotic systems are commonly used as a stream cipher. To overcome this problem, we propose to use a crossed scheme, where a \(p\)-dimensional chaotic signal is generated through the XOR of 1-dimensional systems, this is denoted in (7).  
\[C_{p}(i_{1},i_{2},\cdots,i_{p})=c_{1}(i_{1})\oplus c_{1}(i_{2})\oplus\cdots c_{p }(i_{p}) \tag{7}\]  
In Figure 3, we present how two chaotic systems can generate a chaotic two-dimensional signal.  
Therefore using crossed chaotic signals we can generate a multi-dimensional chaotic signal without simulating a chaotic system for the dimension of the data to be encrypted. Based in crossed chaotic signal, we propose the following encryption scheme in Figure 4.  
We propose to use four chaotic systems. The first three systems form a three-dimensional crossed chaotic signal, and they are used to introduce diffusion in the result. They are simulated for \(max(n,m,l)\) iterations. The forth chaotic system is used to creating a Substitution Box (S-Box) [31], we use the generation algorithm proposed in [32]. S-boxes are used for introducing confusion in the encryption and is a common technique applied in AES and other block ciphers [33].  
It is important to note that the proposed scheme is a transformation that is made pixel by pixel, this is because we are looking for a high parallelizable algorithm. Then the encryption of the pixel  
Figure 4: Chaotic Encryption Scheme.  
Figure 3: 2D Crossed Chaotic Signal.  
\((i,j,k)\) is applied with (8) and the decrypted pixel is recover with (9). In the Algorithm 1, we present the parallel version of this algorithm.  
\[I_{c}(i,j,k)=S_{box}\left[c_{3}(k)\oplus c_{2}(j)\oplus c_{1}(i) \oplus I(i,j,k)\right] \tag{8}\] \[I(i,j,k)=c_{1}(i)\oplus c_{2}(j)\oplus c_{3}(k)\oplus S_{box}^{-1 }\left[I_{c}(i,j,k)\right] \tag{9}\]  
```
Data: Hyperspectral Image \(I\) Result: Encrypted Image \(I_{c}\) Simulate all chaotic systems \(\{c_{1},c_{2},c_{3},c_{s}\}\) in parallel for \(max(n,m,I)\) iterations; Generate S-box with \(c_{s}\); Upload \(I\) to GPU memory; In parallel apply \(I_{c}(i,j,k)=S_{box}\left[c_{3}(k)\oplus c_{2}(j)\oplus c_{1}(i)\oplus I(i,j, k)\right]\); Download \(I_{c}\) to CPU memory;
```  
**Algorithm 1**Parallel Crossed Chaotic Encryption.
## 2 Proposed Seismic Damage Prediction Model for a Building Structure
### Generation Data Labels
#### 2.1.2 Definition of Structural Damage State  
The maximum inter-story displacement ratio (MIDR) is used as a basis for the damage classification of the target structure [48; 49; 50]. It is extracted from the results of the dynamic  
Figure 1: A four-storey RCMRF-framed office building: (**a**) plan view. (**b**) Elevation. (**c**) columns and beams for one floor. (**d**) columns and beams for floors 2–4.  
response analysis carried out on the structural model. In this paper, the damage state for a building is defined in five different levels: no damage (MIDR \(\leq 1/550\)), minor damage (\(1/550<\) MIDR \(\leq\) 1%), moderate damage (1% \(<\) MIDR \(\leq\) 2%), extensive damage (2% \(<\) MIDR \(\leq\) 4%), and complete damage (MIDR \(>\) 4%). Other damage level definitions can also be accommodated through the proposed model.
## 3 PV-Grid Tied Charging Stations for EVs and PHEVs
### Operating Modes
#### 3.2.2 Mode 2 (Grid to Battery)  
In this operation mode, the EV will be charged directly with the power from the AC grid only if the PV array cannot supply the power necessary to feed the battery pack of the EV. In this case, the irradiance must be extremely low or non-existent, thus cutting off the supply of power to the EV. In this way, the bidirectional inverter works as a rectifier, converting the AC power to DC directly to the  
Figure 5: Operating modes for the PV-grid-tied EV charging system. (**a**) PV to battery; (**b**) grid to battery; (**c**) PV and grid to battery; (**d**) PV to grid; (**e**) vehicle-to-grid (V2G) [13].  
common DC bus; then, this voltage is adjusted by the DC-DC charger to supply the required voltage for the EV. This mode of operation is shown in Figure 5b.
# Earthquake-Tolerant Energy-Aware Algorithm for WDM Backbone Network
## 1 Introduction
### Background  
IP over WDM networks consists of two layers: the IP layer and the optical layer, as shown in Figure 1. IP routers are responsible for routing at the IP layer, receiving aggregated traffic from peripheral routers, and interconnecting with the optical side of the network. The optical layer serves as the provider layer, offering services to the client layer through lightpaths. They consist of optical cross-connects (OXC), transponders, optical amplifiers, and multiplexers/demultiplexers [9]. The optical cross-connects (OXC) provide switching of optical signals between input and output ports. They can capture a snapshot of lightpaths and deactivate the paths dynamically. They can also support lightpath management and protection functions. They may also include wavelength conversion, multiplexing, and grooming capabilities internally [10]. Transponders are used when the format of the optical signal from the IP router needs to be adapted to another format. The transponder on the client side receives the signal from the IP router, converts it into an electronic signal, and then converts it back to the required WDM wavelength. The reverse procedure takes place on the receiving side of the network. This operation is called optical-electrical-optical (OEO) conversion. Optical amplifiers (EDFA) directly amplify optical signals without the need for converting them into electrical signals. They are placed along the fiber at intervals to compensate for the attenuation of the fiber. They are also used as amplifiers to increase the transmitting power of the transmitter or as pre-amplifiers to improve the sensitivity of the receiver [10]. Multiplexers and demultiplexers combine and separate multiple wavelengths carried through the fiber [10].
# Effects of Interference and Mitigation Using Notch Filter for the DVB-S2 Standard
## 4 Proposed Solution and Efficiency Analysis
### Discussion  
The results show that the notch filter improves the JSR, the SNR and the BER in the presence of a CWI. Those metrics depend on the value of the r parameter and interference power. As seen in Figures 26 and 27, the JSR and the BER are much improved (except for an r-value of 0.99 and JSR below \(-10\) dB). By attenuating the interference with the notch filter, the signal keeps most of its form and can be used for successful communication at low BER.  
We could argue that the notch filter and the CWI are too narrow, and if the detection of the center frequency is not good enough, not only the interference is still disturbing the signal, but some part of the signal is deleted as well. If the center frequency is moving, the algorithm must be able to follow the CWI. However, the benefits outweigh the negative since a single CW interference degrades much more the signal than the noise. Further research could focus on the precise detector itself which is as much important as the notch filter. Fast and accurate center frequency detection is necessary in order to reduce the negative impacts of the notch filter and maximize interference removal.
## 3 Current Model and Numerical Algorithm
### Model Description
The incident energy \(R\) should be computed by adding the contribution of all directions on the hemisphere above the fuel layer following the Equation (21), where the total radiation intensity \(I\) is given by the differential Equation (22). Each incident energy contribution depends on the flame temperature \(T_{f}\), which is approximated by Equation (28) once dimensionless, and the flame height \(F\), which is calculated depending on the wind, slope, and fuel type, according to Equation (25). The term \(q\) on the right-hand side of of Equation (31) stands for the random heat contribution due to fire-spotting, explained in Section 2.7. Equation (33) represents the loss of solid fuel due to combustion, where  
\[g(u)=\left\{ \begin{array}{ccc}0&if&u<u_{p},\\ \gamma&if&u=u_{p},\end{array} \right. \tag{38}\]  
i.e., there is no loss of solid fuel if the temperature is below the pyrolysis temperature, and it remains constant when the temperature of pyrolysis is reached. This constant value is inversely proportional to the solid fuel half-life time \(t_{1/2}\), of the combustion of each type of fuel:  
\[\gamma=\frac{\ln 2[t]}{t_{1/2}}. \tag{39}\]
## 1 Introduction
For instance, a 20% blend is used in the United States while at least a 10% blend is used in China [23]. Blends receive the name B5, B10 or B20 when the biodiesel volume content is 5%, 10% and 20%, respectively. Today, more than 78% of diesel vehicles coming off production lines are approved for up to B20 use [21]. For biodiesel to be blended with normal fossil diesel, it must comply in Europe with EN14214 from the European Committee of Standardization (ISO) and in the US with ASTM6751 from the American Society for Testing and Materials [5]. Biodiesel can be used in existing diesel engines without the need for substantial modification. Biodiesel has a higher oxygen content than conventional diesel and the carbon to hydrogen ratio is also lower. This explains the major advantages of biodiesel such as lower emission of particulate matter, but also a lesser content of sulfur, hydrocarbon and carbon monoxide [24; 25]. The major challenge nowadays is the production of environmentally and economically viable biodiesel [23] and the use of animal fat waste could contribute towards achieving this goal. This review is highlighting the latest advances in the available processes for biodiesel production from animal fat waste. To elaborate on the present review, the literature search was performed in Web of Science (WoS) database from 1 January 2010 to 28 February 2020. The terms \"biodiesel production,\" \"animal fat\" and \"transesterification\" were used for the survey of published papers. In total, 1602 publications were found, 1594 of them in English, with most found over the last three years. Of these, 1541 manuscripts were research articles, 141 were reviews, 210 corresponded to meetings, 27 corresponded to books, and the rest fell under another category. Eligibility criteria were established as: (a) full-text with English language article; (b) articles selected were published in scientific journals that apply a peer review system; (c) books, workshops, conference reports, theses and case reports were excluded due to lack of peer review; (d) articles with other feedstocks like vegetable oils; and (e) articles that only analyzed the overall performance of the process and did not provide specific parameters for comparison or evaluation were not included. Titles and abstracts of manuscripts were further evaluated for selection of manuscripts and those selected were used for the current review. Some relevant publications published before 2010 were also considered as well as websites of producers and international agencies with current data and updated information on industrial use of feedstocks, animal fats and biodiesel production.
## 3 Results and Discussion  
### The Oenological Parameters of Millet Huangjiu  
The present study investigated the effects of exogenous proteins on various quality parameters of millet Huangjiu. Table 1 summarizes the changes in total soluble solids,reducing sugar content, alcohol content, and amino acid nitrogen content in the produced Huangjiu samples. The control group showed the lowest total soluble solids content (\(11.8\pm 0.19\)\({}^{\circ}\)Bx), while the Glu-2 group, with the highest amount of protein addition, exhibited the highest total soluble solids content (\(15.6\pm 0.21\)\({}^{\circ}\)Bx). Notably, the amount of protein added before fermentation was also positively correlated with amino acid nitrogen concentrations (Table 1). However, the effect of protein components on reducing sugar content was found to be non-significant, though these values were still within the same range as concentrations reported in previous studies on traditional millet Huangjiu [10; 12]. Similar findings were also reported in rice Huangjiu and sake [8; 9].  
A notable correlation exists between the concentration of ethanol in millet Huangjiu and the glutelin content of its fermentation substrate (Table 1). Interestingly, the ethanol contents in Huangjiu samples were increased by 8.2%, 7.3%, and 17.7% in the Glu-1, Glu + Alb, and Glu-2 groups, respectively. On the contrary, no significant differences were observed between the control group and the albumin-added groups Alb-1 and Alb-2. During the production process, millet starch is hydrolyzed into dextrin by the enzyme \(\alpha\)-amylase and subsequently into glucose by glucoamylase [14]. Yeasts then ferment the glucose to produce ethanol, the primary alcohol in Huangjiu. A recent study investigated the role of glutelin, a protein found in millet, in the fermentation process of Huangjiu. The results showed that higher levels of glutelin in the fermentating materials resulted in a more rapid conversion of sugar to alcohol than in the control group [15]. This suggests that glutelin may promote yeast growth and enhance the utilization of sugar for ethanol production. The concentration of ethanol can be used as a reliable indicator of the fermentation status of Huangjiu during the production process. The findings of this study support the hypothesis that glutelin plays a significant role in the completion of millet Huangjiu fermentation and its quality.
# Research on Evaluation Methods of Black Soil Farmland Productivity Based on Field Block Scale
## 2 Materials and Methods
### Topsoil Productivity Evaluation Model
#### 2.2.1 CI Model and Its Revisions  
The CI model, namely the Comprehensive Index model, is a surface soil assessment method established on the foundation of soil productivity coefficients proposed by [PERSON] et al. [5]. This method comprehensively considers multiple key factors, including the content of organic matter, available nitrogen, available phosphorus, available potassium, soil acidity and alkalinity, soil texture, erosion status, and topography, encompassing eight aspects in total.  
The content of organic matter, available nitrogen, available phosphorus, and available potassium in the surface soil reflects the nutrient status of the soil, while soil acidity and alkalinity, soil texture, erosion status, and topography involve the physical properties and geographical environment of the soil. These eight factors are considered crucial in influencing land productivity. By comprehensively considering them, the CI model provides a holistic productivity assessment for the soil. The scores for each factor can be obtained from relevant literature and expert experiences. The summation of these scores constitutes the comprehensive index of the CI model, where higher scores indicate a greater contribution to soil productivity (Table 1).  
Surface soil productivity index is evaluated using the following indicators:  
\[\text{CI}=\frac{\text{a}\sum\limits_{i=1}^{8}X_{i}Y_{i}}{10} \tag{1}\]In the formula, CI represents the Soil Productivity Index, where i takes values from 1 to 8, representing the eight considered factors. \(\rm{X_{i}}\) denotes the weights assigned to the eight selected factors, and \(\rm{Y_{i}}\) represents the scores for these eight factors. The topographical factor 'a' is not considered in the formula due to the uniformity of maize soil sampling within the same farm.  
Taking into account the importance of soil physicochemical indicators in the production of maize crops in Northeast Black Soil and the accessibility of relevant data, we have chosen the indicators of Soil Alkaline Nitrogen (A), Available Phosphorus (B), Available Potassium (C), Suitability Index for pH value (D), Moisture Content (E), and Soil Bulk Density (F) for the original surface soil assessment model. The weights assigned to these indicators in the original model are 10%, 10%, 10%, 20%, 20%, and 20%, respectively.  
The analyses yielded a ration of A:B:C:D:E:F = 1:1:1:2:2:2 for these indicators, highlighting their equal importance in the overall soil productivity assessment. Considering that these assessments were carried out on the same farm, we ignored possible climatic influences from different regions. As a result, the resulting Modified Composite Index (MCI) model was obtained as follows:  
\[\rm{MCI}=\frac{\left(A+B+C+2\times D+2\times E+2\times F\right)}{9} \tag{2}\]  
This allocation strategy reflects the importance of each of the selected indicators, taking into account their joint influence in the overall soil productivity assessment of the black soil zone in northeastern China.
## 1 Introduction
The equations describing the coincidence experiments are given to provide insight in the performance gains and the role of the instrumental parameters in comparison to conventional EELS and EDX setups. These equations also provide a novel method for collection efficiency measurement omitting the need for a reference sample. Additionally, the influence on the coincidence measurement when varying the electron beam current is investigated and the performance of model-free background subtraction in EELS and EDX is demonstrated. Subsequently, the current limitations in time resolution of the setup are described and possible ways for improvement are discussed. Finally, a statistical analysis of the signal-to-noise ratio (SNR) demonstrates how coincidence counting provides superior results over conventional EELS and EDX for cases with low signal to background and/or low beam current, making the method ideal for extending the capabilities of analytical TEM for trace element analysis.
# Drone Deployment Algorithms for Effective Communication Establishment in Disaster Affected Areas
## 3 Proposed Methods
### Region Filling
#### 3.4.5 Inner Layer Filling
```
1:Input:\(d_{1},d_{2}\)**--two neighbouring drone objects Output:\(d\)--new drone object
2:if\(d_{1}\mid\mid d_{2}\) not\(defined\)then
3:\(d\gets not\)\(defined\)
4:else
5:\(r\gets Average(d_{1},r,d_{2}.r)\)
6:\((x_{1},y_{1})\leftarrow ArbitraryDistant(d_{1}.x,d_{1}.y,d_{2}.x,d_{2}.y,d_{1}.r,d_{ 2}.r,0)\)
7:\(h_{ref}\gets H_{x_{1},y_{1}}\)
8:\(d_{1}.h_{ref}\gets Maximum(d_{1}.h_{ref},h_{ref})\)
9:\((d_{1}.b,d_{1}.h,d_{1}.r,d_{1}.e)\gets FindDroneParameters(d_{1}.x,d_{1}.y,d_{ 1}.h_{ref})\)
10:\(d_{2}.h_{ref}\gets Maximum(d_{2}.h_{ref},h_{ref})\)
11:\((d_{2}.b,d_{2}.h,d_{2}.r,d_{2}.e)\gets FindDroneParameters(d_{2}.x,d_{2}.y,d_{ 2}.h_{ref})\)
12:\(Perform\) steps 4 and 5
13:\((x_{2},y_{2})\gets ArbitraryDistant(d_{1}.x,d_{1}.y,d_{2}.x,d_{2}.y,d_{1}.r,d_{ 2}.r,r)\)
14:if\(x_{2}=x_{1}\)then
15:\(\theta\gets 90^{\circ}\)
16:else
17:\(\theta\gets tan^{-1}\left(\frac{y_{2}-y_{1}}{x_{2}-x_{1}}\right)\)
18:endif
19:\(x\gets x_{1}+r\times cos\theta,y\gets y_{1}+r\times sin\theta\)
20:\((b,h,r,e)\gets FindDroneParameters(x,y,h_{ref})\)
21:\(Perform\) steps 12 to 18
22:\(coordinates\leftarrow\{(x,y)\}\)
23:while\(true\)do
24:\(Perform\) steps 12 to 19
25:if\((x,y)\)\(\epsilon\) coordinatesthen
26:\(b_{1},h_{1},r_{1},e_{1}\gets FindDroneParameters(x,y,h_{ref})\)
27:while\(h_{1}\ >\ h\)do
28:\((b,h,r,e)\gets b_{1},h_{1},r_{1},e_{1}\)
29:\(Perform\) steps 12 to 18
30:\((b_{1},h_{1},r_{1},e_{1})\gets FindDroneParameters(x,y,h_{ref})\)
31:endwhile
32:break
33:\(coordinates\gets coordinates\cup\{(x,y)\}\)
34:endwhile
35:\(d\gets Drone(x,y,r,h,b,e,h_{ref})\)
36:\(d_{1}.east\gets d_{2}.north\gets d\)
37:\(d.west\gets d_{1},d.south\gets d_{2}\)
38:endif
39:return\(d\)
```  
**Algorithm 3** TwoNeighbours--Calculating the position of a third drone object based on the positions of its two neighbours  
```
1:Input:\(d\)**--a drone object that represents the starting circle of the inner layer Output: updated \(D\)**--collection of drones, updated \(y_{ref}\)**--reference \(y\)**--coordinate for\(x\) direction filling.
# Explainable Artificial Intelligence Approach for Improving Head-Mounted Fault Display Systems
## 2 Explicative Head-Mounted Fault Display System Design
### Fault Diagnoser Model Computation  
As aforementioned, the problem addressed in this paper is to determine the occurrence of an anomaly and give a plausible cause (or explanation) so that human operators can quickly determine what action to perform. To determine if the system is in an abnormal situation over time, we use the LSTM model in this study. LSTM is particularly well suited for time series analysis due to its ability to capture long-term dependencies, handle varying sequence lengths, and adapt to the temporal dynamics of the data. Their flexible architecture and proven success in various applications make them a powerful tool for time series forecasting and related tasks [34; 35]. The LSTM concept improves over traditional recurrent neural networks (RNNs), and it was designed to address their limitations, such as the vanishing gradient problem. Hereafter, we have the principle of LSTM model formulation.