esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of Coastal Zone Data of Northern Yantai Collected by Remote Sensing from 1990 to 2018
###### Abstract  
Using remote sensing images of different time phases from 1990 to 2018, the surface coverage information of northern Yantai (coastline, 2 km from coastline to land) was extracted by means of average high tide line and visual interpretation. The end point change rate (EPR) and linear regression rate were used to study the coastline change rate, the fractal dimension of the coastline in the study area was analyzed, and the change of the type of coastal surface cover was analyzed by the transition matrix. The results show that: (1) Form 1990 to 2018, a significant trend of a continuous increase in the total length of coastline was observed with an increase of 181.08 km (43.18%). In the study area, the coastline of Laizhou had the greatest change rate with an EPR value of 33.67 m/a, whereas the coastline of Laishan had the smallest change rate with an EPR value of 0.30 m/a. (2) Over the past 30 years, with the rapid economic development of Yantai and the ensuant urbanization, the total surface area of the coastal zone in the study area has increased by 144.94 km\({}^{2}\), mainly in the areas covered by structures and forests/grasses, by 112.96 km\({}^{2}\) and 96.08 km\({}^{2}\), respectively, while the areas of desert/bare land and water have decreased by 92.26 km\({}^{2}\) and 12.32 km\({}^{2}\), respectively. (3) The changes among different types in the study area were clear, mainly from desert/bare land, cultivated land, and building areas to forests/grasses cover and structures. The change areas were mainly concentrated in Laizhou, Longkou, Zhifu, and Penglai. Frequent human activities are an important factor affecting the continuous expansion of the coastal areas of Jiaodong Peninsula to the sea. Aquaculture, coastal construction, construction of artificial islands, and expansion of port terminals have seriously affected the sustainability of ecological resources in the coastal areas. At the same time, the changes in the ecological environment in the coastal zone will have a greater impact on the health of the coastal zone.  
Keywords:coastline; coastal zone; remote sensing; Jiaodong Peninsula +
Footnote †: journal: Applied Sciences
## 4 Application
### Comparison between the Hybrid Model and the Simulation of Normal Random Variables  
The application of optimization algorithms to solve the economic dispatch problem requires test cases to evaluate the stability and quality of the solution found. An electrical reference system is used to test the algorithms, and the economic dispatch problem is solved based on historical information [12; 13; 14; 15; 16].  
Given the requirement of enough test cases, information for the validation of optimization algorithms, a widely used option is the simulation of demand data generating random values assuming a normal probability distribution. One of the main limitations of this method is that generated values are limited to the parameters of a normal distribution, their mean, and standard deviation \(N\sim(\mu,\,\sigma^{2})\). Since it is documented that the demand for electrical energy presents heavy tails in its distribution, further seasonality related to the time of day, day of the week, and month of the year analyzed [22; 23; 24; 25; 26; 27; 28].  
The proposed methodology addresses these aspects through the hybrid model that fitting of the electricity demand through the use of alpha stable distributions coupled with the modeling of seasonality and non-linear behavior through the use of GAM by integrating additive non-linear functions, such as spline functions or kernel functions, to variables such as the price of electrical energy, as well as the cost of the fuels, among others. Figure 9 shows the comparison between the simulation of normal random demands to simulated demands using the proposed hybrid model for a period of 52 weeks.  
It is observed that the generation of instances only using normal random numbers generates demands without any seasonal pattern besides presenting abrupt variations in demand, which are just random. Also, it is necessary to limit the generated values to only positive values, so that when the negative values of the distribution are generated, they take the value of zero. On the other hand, in the case of the hybrid model, the presence of peaks in demand, as well as seasonality, is observed. This lets generating instances more attached to reality and therefore subjecting the optimization models to more  
Figure 9: Comparison between instances generated by the hybrid model vs. normal distribution.  
realistic demands. Thus, contributing to generate more stable and higher quality models, tested in real-life scenarios.
## 5 Experimental Results
### Cascade Approach
#### 5.4.3 Classifier Selection  
The evaluated classifiers support multiple parameters, which directly affects their performance. The strategies applied in Section 5.3.2 to optimize the classifiers are applied here to select the best performing classifiers for the cascade approach. As shown in Table 8, the k-NN and the ANN classifiers offer the higher accuracy for most of the datasets of the cascade approach. Although the Naive Bayes classifier could be also considered, this classifier presents a relatively low accuracy for the _Root_ dataset, for which accuracy is critical for the cascade approach as discussed in the section below. For these reasons, only the K-NN and the ANN classifiers are selected to be optimized. Table 9 summarizes the default and optimized parameters for the k-NN and ANN classifiers.  
Figure 11 depicts the achieved accuracy when using the default and the optimized configuration of the classifiers using Set 3. As summarized in Table 9, each classifier is optimized to achieve the highest accuracy per data set. The optimization is evaluated using all the available features (Set 3). The classifier ANN presents a higher increment when optimized, offering the highest performance for most of the datasets.  
Figure 12 depicts the ratio between the average execution time when using the default and the optimized configuration of the classifiers. Again, an ANN classifier increments their execution time when optimized for the Root dataset. In general, the execution time does not present any significant variation when optimizing.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{3}{c}{**Optimized Parameters**} \\ \hline
**Datasets** & n\_neighbors & **hidden\_layer\_sizes** \\ \hline Root Dataset & 1 & 96 \\ Airport & 1 & 14 \\ Construction & 2 & 5 \\ Residence & 1 & 48 \\ Vehicles & 1 & 60 \\ Warning & 3 & 48 \\ Non-cascade & 1 & 74 \\ \hline \hline \end{tabular}
\end{table}
Table 9: The parameters for the optimization of the k-NN and ANN classifiers for the datasets of the cascade approach and for the non-cascade approach.  
Figure 11: Achieved accuracy of the classifiers with their default and optimized configuration.
# Game-Theoretic Power and Rate Control in IEEE 802.11\(p\) Wireless Networks
## 3 System Model
### Link Quality Estimator  
To characterise the link quality, we utilise the Packet Reception Ratio (PRR) metric, which in general describes the ratio of the number of packets successfully received by node \(j\) over the number of packets sent by node \(i\) for a wireless link \((i,j)\). It is known that the relation between PRR and Signal-to-Interference-plus-Noise Ratio (SINR) is given by:  
\[PRR_{i,j}=(1-e^{-\gamma_{i,j}})^{l}, \tag{1}\]  
Figure 1: VANET Scenario.  
where \(\gamma_{i,j}\) is the SINR of the transmission from node \(i\) to node \(j\), and \(l\) is the packet length in bits. The term \(\gamma_{i,j}\) is given by  
\[\gamma_{i,j}=\frac{H_{i,j}p_{i}}{\sum_{t\
eq i,l\
eq j}I_{t}+N_{0}}, \tag{2}\]  
where \(N_{0}\) is variance of the white Gaussian noise, \(H_{i,j}\) is the channel gain of node \(i\) with node \(j\), \(p_{i}\) is the transmission power of node \(i\) and \(I_{t}\) is the interference signal from node \(t\), which can be calculated as \(H_{t,k}p_{t}\), namely using the channel gain between nodes \(t\) and \(k\) and the transmission power of node \(t\). for the node \(i\).  
By increasing the the transmission power, it is expected that the SINR will also be increased. However, when the transmission power is set to a significantly high level, interference from the other users, \(\sum_{t\
eq i,l\
eq j}I_{t}\), makes the PRR decrease. When we have no interference, the PRR goes rapidly to maximum 1. In Figure 2, we see the increase of the PRR with the increase of the SINR without the presence of interference, where it follows the sigmoidal function. Note that for \(PRR(0)=0\) and \(PRR(\infty)=1\), which is true for a packet with reasonably large number of bits. Thus, SINR values that belong in the grey area do not exhibit stable PRR estimation. For this reason, our simulation results will be based on more practical definitions of PRR.
# On Vulnerability of Selected IoT Systems to Radio Jamming--A Proposal of Deployment Practices
## 6 The Measurement of the Weightless and Sigfox Susceptibility to Interference
### The Measurement Set-Up
In each individual measurement a setting consisting of a unique combination of the aforementioned Weightless operational parameters, namely: _BW_; \(R\); \(R\); _MOD_), was used, yielding eight combinations, the two extreme of which were discussed in Section 6.2, namely: {12.5 kHz; 0.625 kb/s; 0.5; OQPSK} and {100 kHz; 100 kb/s; 1.0; GMSK}. The SigFox equipment, i.e., the end-device and the BS, were both Digi-Key devices, whereas the operational mode was set to the only one available in SigFox, i.e., DBPSK with automatic triple retransmissions of each packet. Since in SigFox each packet transmission actually takes place three times on different randomly selected physical channels, the need for repeating the PER test series in order to achieve the measurement stability, was thereby eliminated because repetitions were already ensured by the SigFox communication protocol. This random selection of the three physical channels for transmitting a given packet made the PER value obtained for a single test sequence equivalent to the tests performed for three different sequences of 100 packets in each. A full compilation of operational parameters used in the measurements can be found in Table 2.  
Figure 4: A schematic of the set-up used for measuring the IoT Low-Power Wide Area Network (LPWAN)/ultra-narrowband (UNB) systems’ performance in the Laboratory of Electromagnetic Compatibility (LEC) (Wroclaw University of Science and Technology (WUST)) anechoic chamber.
# A Distributed Mix-Context-Based Method for Location Privacy in Road Networks
## 6 Experimental Setup
### Evaluation Criteria
#### 6.2.1 Anonymity Set Size  
Anonymity is one of the significant metrics for evaluation of location privacy. It is the process of the grouping of users of similar status in a vicinity that hides the identity of a user in the group. Anonymity Set Size (ASS) is defined is the set of users/vehicles, including target users that are indistinguishable among the group of users. ASS achieves the anonymisation of the vehicle in a group of vehicles of similar status. In the road network, ASS is preferred, which hides the actual identities of a target vehicle during communication. Its value affects the location privacy of vehicles. The higher the ASS, the higher will be the level of vehicle privacy. We consider the arrival of vehicles at a particular point at the road is the Poisson process with rate \(\lambda\) and \(X\) is a random variable that denotes the number of vehicles gathered at the congested area for an interval of time \(T\); then, the probability is calculated as follows [12]:  
\[P(X=x)=\frac{(\lambda T)^{x}}{x!}e^{-(\lambda T)}, \tag{21}\]  
where \(\lambda T\) is the expected number of vehicles that change pseudonyms cooperatively. This can improve the anonymity of vehicles in the vicinity by updating pseudonyms at the same time. Hence, the expected number of vehicles during the time \(T\) is denoted as:  
\[E(X=x)=\sum_{x=1}^{\infty}x\frac{(\lambda T)^{x}}{x!}e^{-(\lambda T)}=\lambda T. \tag{22}\]  
After the computation of probability and the expected number of vehicles at a certain time, the anonymity set size is calculated as follows:  
\[|ASS|=\sum_{i,j=1}^{n}V_{i}PC_{j}=\sum_{i=1}^{n}E(X_{i}=x)=\sum_{j=1}^{n} \lambda_{j}T. \tag{23}\]  
where \(V_{i}PC_{j}\) is the number of vehicle update pseudonyms that anonymize the target vehicle in the region.
## 2 Novel Phase Shifter Design
The calculated phase shift values for unit and cascaded P-CRLH phase shifters at the design frequency of 5.5 GHz are \(-92.3^{\circ}\), \(-184.6^{\circ}\), \(-276.9^{\circ}\) and \(-369.2^{\circ}\), respectively. The corresponding values of the simulated phase shifts achieved are \(-92.3^{\circ}\), \(-184^{\circ}\), \(-275.5^{\circ}\) and \(-367.4^{\circ}\), respectively, as shown in \[\Phi=\beta L, \tag{2}\]1 The range of phase error between the calculated and simulated phase shifts is \([0.6^{\circ}\)-\(1.8^{\circ}]\). The effects of the phase error on the phased array antenna will be discussed in Section 3. \[\Phi=\beta L, \tag{2}\]2 Simulated phase response results of the proposed unit cell and cascaded unit cells P-CRLH phase shifter with MRSs embedded on the symmetrical stubs for \(-45^{\circ}\) phase shift.
# Fire Video Recognition Based on Local and Global Adaptive Enhancement
## 4 Proposed LGAE
### GAME Block  
To depict the global motion characteristics, the GAME block was designed. As shown in Figure 2, GAME is mainly composed of an adaptive weight layer and two linear layers without additive bias.  
First, an adaptive weight layer is devised to learn knowledge of the global motion, which is formulated as below.  
\[\mathbf{E}_{\text{G}}=\sum_{k=1}^{T^{\prime}}\sum_{i=1}^{n^{\prime}}\sum_{j=1} ^{w^{\prime}}\text{Sigmoid}(\mathbf{W}_{\text{G}}\mathbf{X}^{\prime})[\text{ k},\text{i},\text{j}], \tag{4}\]  
where \(\mathbf{E}_{\text{G}}\in\mathbb{R}^{C^{\prime}}\) is the output tensor, \(\mathbf{X}^{\prime}\) is calculated by Equation (3), \(\mathbf{W}_{\text{G}}\) represents the adaptive learnable tensor, and \(\text{Sigmoid}(\cdot)\) is the Sigmoid function.  
Second, the quantity of channels of tensor \(\mathbf{E}_{\text{G}}\) is reduced to \(\frac{\mathbb{C}^{\prime}}{R}\) using Tensor \(\mathbf{W_{1}}\). Then, the quantity of channels is restored using Tensor \(\mathbf{W_{2}}\). In our experiment, \(R\) was fixed at 4. Therefore, the global motion information was further enhanced using \(\mathbf{W_{1}}\) and \(\mathbf{W_{2}}\). The calculation of this process can be formulated as below.  
\[\mathbf{S}_{\text{G}}=\text{Sigmoid}(\mathbf{W}_{2}\text{ReLU}(\mathbf{W_{1} }\mathbf{E}_{\text{G}})), \tag{5}\]  
where \(\mathbf{S}_{\text{G}}\in\mathbb{R}^{C^{\prime}}\) represents the global weight tensor, \(\text{ReLU}(\cdot)\) denotes the function of rectified linear unit, and \(\mathbf{W}_{1}\in\mathbb{R}^{C^{\prime}}\times\frac{\mathbb{C}^{\prime}}{R}\) and \(\mathbf{W}_{2}\in\mathbb{R}^{\frac{C^{\prime}}{R}\times C^{\prime}}\) are the two parameters of the two linear layers.  
Third, Tensor \(\mathbf{S}_{\text{G}}\) is used to enhance Tensor \(\mathbf{X}^{\prime}\), and a residual connection is also used to improve the generalization ability, which is formulated as follows:  
\[\text{GAME}(\mathbf{X}^{\prime})=\mathbf{X}^{\prime}+\mathbf{S}_{\text{G}} \otimes\mathbf{X}^{\prime}, \tag{6}\]  
where \(\mathbf{S}_{\text{G}}\) is calculated using Equation (5), and \(\otimes\) is the Hadamard product with a broadcast mechanism. This block adaptively learns the global distribution information, thus enhancing the ability to depict the global motion information.
## 4 Mycotoxins in Food Commodities in Cameroon
### Cassava and Derived Products  
The Cassava root contributes to food security, income, and employment opportunities in rural areas of sub-Saharan Africa. In Cameroon, cassava roots are processed into fufu, gari, tapioca, starch, and flour. Fresh cassava and cassava products are perceived as potential sources of mycotoxins. Some authors assessed the AFs content in cassava chips, a cassava-derived product (obtained after fermentation and drying) which is widely consumed locally [40]. Aflatoxins and penicillin acid were found at a concentration of 194 ug/kg and 184 ug/kg, respectively, in cassava products (flakes and chips) [31]. The presence of OTA was detected in fresh cassava and ZEN in dried cassava in the center and north of Cameroon at concentrations below the EU limits [34]. The Sudano Sahelian zone (AEZ 1) and the western highlands (AEZ 3) exhibit the highest concentrations for AFB1 and penicillin acid.
## 5 Discussion
### The Relationship of Freezing Duration and Pore Distribution Peak Value  
There is a relationship between the NMR peak value and freezing duration. The curves of Figure 12 link the first peak value intimately with freezing duration. During the first four hours of freezing in Figure 15a, the free water detected as small pores increased sharply, then the growth rate of the peak value declined. The relationship of peak value \(V_{F}\) and freezing duration time \(t\) is established in:  
\[V_{F}=\frac{7.45509t}{0.25617+t}, \tag{3}\]  
Similar to the freezing process, the peak value changed most rapidly in the first four hours of freezing after thawing. Small pores decreased significantly after freezing-thawing action. The curve of first peak value \(V_{F\text{-}T}\) and freezing duration \(t\) is fitted as:  
\[V_{F\text{-}T}=5.3225+\frac{1.8422}{t}, \tag{4}\]  
Figure 14: PSD changes after different freezing durations: (**a**,**b**) show the post-test results changes to pore distribution initially and after a 24 h rest period.  
Equations (3) and (4) both reflect that the freezing speed goes down with the freezing duration increase. For the samples in this paper, 80% of the freezing process was concentrated in the first four hours. Considering the energy required to freeze water, the set temperature could hardly freeze the rest of the water after four hours of freezing duration. There was a turning point in the actual construction. That is, when the freezing time exceeded a certain value, the soil strength basically did not increase, while the freezing damage continued to increase. Therefore, it was important to find a turning point for freezing duration in AGF construction to shorten the working period.
###### Abstract  
A heterostructure of Sn-doped In\({}_{2}\)O\({}_{3}\) (ITO)/Al-doped ZnO (AZO)/n-Si was proposed and studied for photovoltaics. The top ITO worked as a transparent conducting layer for excellent optical transparency and current collection. The AZO/n-Si served as the active junction and provided the built-in potential (\(qV_{bi}\)) for the photovoltaic devices. To achieve a higher open circuit voltage (V\({}_{\rm oc}\)), which is the main challenge for AZO/Si heterojunctions due to the junction interfacial defects, the AZO and AZO/Si junction properties were systematically investigated. By modulating the Al doping in the AZO thin films via a dual beam co-sputtering technique, the AZO/n-Si junction quality was significantly improved with \(qV_{bi}\) increased from 0.21 eV to 0.74 eV. As a result, the V\({}_{\rm oc}\) of our best device was enhanced from 0.14 V to 0.42 V, with a short circuit current (\(\rm{{}_{\rm sc}}\)) of 26.04 mA/cm\({}^{2}\) and a conversion efficiency (E\({}_{\rm fl}\)) of 5.03%. To our best knowledge, this is the highest V\({}_{\rm oc}\) reported for ZnO/Si heterojunctions prepared by the sputtering method. The results confirmed the validity of our proposed structure and junction engineering approach and provided new insights and opportunities for ZnO/Si heterojunction optoelectronics.  
Al-ZnO/Si heterojunction photovoltaic; junction engineering; built-in potential; thin film stress release +
Footnote †: journal: Applied Physics Letters
# Assessment of the Impact of Electric Vehicle Batteries in the Non-Linear Control of DC Microgrids
## 2 Background: Non-Linear Control of DC Microgrid
### Assumed Microgrid Configuration  
For this study, the proposed microgrid includes DG: photo-voltaic (PV) panel; and ESSs: a battery, and a supercapacitor; and DC/DC converters, as can be seen in Figure 1. Moreover, the microgrid circuit configuration is illustrated in Figure 2. The supercapacitor and electric battery work as storage for the generation from the PV that is not consumed, but they also allow for mitigating disturbances due to transients. The electric battery could provide a low-frequency current and the supercapacitor, a high-frequency one. A boost converter was selected for the DC/DC converter, which is composed of three phases and it includes six switches connected in parallel. The switches could be independently controlled by three different duty cycles.  
Later in this study, the simulation of the charging of an EV battery will be simulated as a new load for the DC microgrid. Note that, bidirectional flow, i.e., Vehicle-to-grid (V2G), is not considered.  
With this configuration, various power electronics equations could be obtained, described as follows:  
\[\frac{dV_{C_{1}}}{dt}=\frac{V_{PV}}{R_{1}C_{1}}-\frac{V_{C_{1}}}{R_{1}C_{1}}- \frac{I_{L_{1}}}{C_{1}}, \tag{1}\]  
\[\frac{dV_{C_{2}}}{dt}=\frac{V_{BAT}}{R_{2}C_{2}}-\frac{V_{C_{2}}}{R_{2}C_{2}}- \frac{I_{L_{2}}}{C_{2}}, \tag{2}\]  
\[\frac{dV_{C_{3}}}{dt}=\frac{V_{SC}}{R_{3}C_{3}}-\frac{V_{C_{3}}}{R_{3}C_{3}}- \frac{I_{L_{3}}}{C_{3}}, \tag{3}\]  
\[\frac{dI_{L_{1}}}{dt}=\frac{V_{C_{1}}}{L_{1}}-\frac{[(R_{01}-R_{02})u_{1}+R_{0 2}]I_{L_{1}}}{L_{1}}-\frac{(1-u_{1})V_{DC}}{L_{1}}, \tag{4}\]  
\[\frac{dI_{L_{2}}}{dt}=\frac{V_{C_{2}}}{L_{2}}-\frac{[(R_{03}-R_{04})u_{2}+R_{0 4}]I_{L_{2}}}{L_{2}}-\frac{(1-u_{2})V_{DC}}{L_{2}}, \tag{5}\]  
\[\frac{dI_{L_{3}}}{dt}=\frac{V_{C_{3}}}{L_{3}}-\frac{[(R_{05}-R_{06})u_{1}+R_{0 6}]I_{L_{3}}}{L_{3}}-\frac{(1-u_{3})V_{DC}}{L_{3}}, \tag{6}\]  
Figure 1: Proposed DC Microgrid blocks diagram.  
Figure 2: Proposed Circuit of the Microgrid.  
\[\frac{dV_{DC}}{dt}=\frac{1}{C_{DC}}[(1-u_{1})I_{L_{1}}+I_{L_{2}}+I_{L_{3}}-I_{LOAD }], \tag{7}\]  
\[I_{LOAD}=I_{L_{4}}+I_{L_{5}}+I_{L_{6}}, \tag{8}\]  
where \(V_{C_{1}}\), \(V_{C_{2}}\), and \(V_{C_{3}}\) are the voltages of capacitors \(C_{1}\),\(C_{2}\), and \(C_{3}\). \(V_{PV}\), \(V_{BAT}\), \(V_{SC}\), \(V_{DC}\) are, respectively, the voltages of PV panel, battery, supercapacitor, and DC microgrid. \(I_{L_{1}}\), \(I_{L_{2}}\), \(I_{L_{3}}\), \(I_{L_{4}}\), \(I_{L_{5}}\), and \(I_{L_{6}}\) are the inductances \(L_{1}\), \(L_{2}\), \(I_{3}\), \(I_{4}\),\(L_{5}\), and \(L_{6}\) currents. \(R_{01}\), \(R_{02}\), \(R_{03}\), \(R_{04}\), \(R_{05}\), and \(R_{06}\) are the internal resistances of the converter's switches. \(u_{1}\), \(u_{2}\), and \(u_{3}\) are the duty cycles of the converters. \(I_{LOAD}\) is the total load current.