esa-sceva/satcom-chunk-collection huggingface dataset:
# Data Fusion-Based Structural Damage Identification Approach Integrating Fractal and RCPN
## 4 Damage Identification of ASCE Benchmark Structure [41]  
### Numerical Model  
The American Society of Civil Engineers (ASCE) Benchmark structure is a four-storey, 2 \(\times\) 2 span steel frame structure. Its scaled structure model is shown in Figure 3a. The plane size of the model is 2.5 m \(\times\) 2.5 m and the height is 3.6m. Each floor and each span of the steel frame model are provided with a floor slab. The four floor slabs of the first floor have a mass of 800 kg, the four floor slabs of the second and third floors have a mass of 600 kg and the four floor slabs of the fourth floor have a mass of 400 kg. In this paper, a structural finite element model of 120 degrees of freedom is adopted, and the schematic diagram of the analysis model is shown in Figure 3b. In the figure, \(w_{1}\), \(w_{2}\) and \(w_{3}\) represent external excitation respectively and are located at the nodes of columns in each layer, \(\ddot{y}\) representing acceleration sensors. Two acceleration sensors are arranged in the \(x\) and \(y\) directions of each layer, with a total of 16 acceleration sensors. The sampling frequency was 1000 Hz, the sampling duration was 10 s, and the excitation mode was environmental excitation. Four damage modes were simulated, as shown in Table 1.
# Detecting Change at Archaeological Sites in North Africa Using Open-Source Satellite Imagery
## 3 Results
### Aswan and Kom Ombo
#### 3.1.1 True Positives
In areas where this is occurring, it can be identified at an early stage using change detection and sites could be recorded and potentially protected before they are further damaged/destroyed. Figure 9: Bulldozing to create a pit and surrounding spoil. The changed area within buffered zone of WK32 and direction of the photograph arrowed. Google image 2020 imported as an xyz tile to QGIS. Photograph January 2020. Figure 11: Changes including expansion of a modern cemetery and excavation of a pit (marked with arrows) around WT1 and WT7 are verifiable remotely using PlanetScope imagery. Figure 10: Trackway/material spread in front of modern cemetery, encroaching on the buffered area of WT1. The changed area within buffered zone of WT1 and direction of the photograph. Google image 2020 imported as an xyz tile to QGIS. Photograph January 2020. Figure 12: Lithic scatters in this area WK31, 35, 36, 37, 38 were recorded by AKAP in 2012. Although bulldozing and some ploughing to lay out the fields and their boundaries was done between 2009–2017, crops were not planted until 2018–2020. The planting fell into the analysis period and was detected. Google Earth images.
# Selected Applications of Terahertz Pulses in Medicine and Industry
## 4 Terahertz Imaging in a Pharmaceutical Context  
As most dielectric materials are semi-transparent in the terahertz region of the spectrum, it was soon realized that the terahertz pulses could be used in the analysis of the coating thickness on solid dosage forms [71; 72]. Unlike traditional microscopy measurements, the terahertz method provides the advantage of the non-destructive assessment of coating thickness and other properties, preserving the tablet for subsequent dissolution testing. The first study was undertaken by the FDA ([PERSON] et al. [73]), who measured the coating thickness on mesalamine tablets using terahertz pulsed imaging (TPI), and was then able to correlate this to tablet dissolution. [PERSON] et al. [74; 75; 76] showed in a series of papers that it was possible to correlate not only the coating thickness but also the density of the coating. In later work, [PERSON] et al. also used the raw terahertz pulse and chemometrics to predict the dissolution of sustained release pharmaceutical product [77]. An example of correlation between the thickness on an enteric coated tablet and the dissolution is shown.  
Tablet 7 is an outlier of the data set in Figure 3. The error bars shown in Figure 3 are the variation in the coating thickness across the whole tablet. The technique measures in the region of 2000 data points and the thickness can be derived at each point. The outlier, labelled tablet 7, from the straight line is due to imperfections in the tablet coating.  
In a recent paper by [PERSON] et al. [78], the authors showed a good correlation between the refractive index measured by terahertz pulsed imaging and the crushing force measured in  
Figure 3: The mean dissolution time in minutes against the thickness of the enteric coating as measured with terahertz pulsed imaging. The error bars are the variation in the enteric coating thickness as measured across the whole tablet.  
compression tests. The crushing force was found using a set of tablets that were compacted at various compression forces and this was found to be related to tablet hardness. The same group used a terahertz sensor inside a coating pan to follow the build-up of a pharmaceutical coating on a tablet [79; 80].  
The terahertz refractive index of the solid dosage form is not only dependent on the chemistry of the material but also on its porosity. In measuring the refractive index of a tablet, the porosity contributes to the effective index of the material; for example, the greater the porosity of the sample, the lower the effective refractive index of the material. Therefore, by measuring the effective refractive index of the material, an indication of the porosity is obtained [81; 82]. In the past, this was achieved by using high pressure mercury. Recent publications have demonstrated this technique, and the resultant development of high throughput instrumentation to be used within the pharmaceutical environment [83; 84; 85] is shown in Figure 4.
## 2 Research Methods and Technical Routes
### Research Process  
To fully consider the opinions of both experts and workers, we first collected expert opinions through a questionnaire survey and utilized an improved AHP method to determine the weight of each factor. Subsequently, we collected the opinions of workers through questionnaires and accordingly assigned them weights. The BackPropagation neural network was then employed to establish the prewarning model. The weight correction value could reflect the common opinions of experts and workers at this time. Therefore, the factor weight could be analyzed, and the model could be used for the safety assessment and early warning of individual workers. The specific research route is shown in Figure 1.
## 5 Conclusions  
In this paper, a 3-coil inductive WPT system with a small-sized implantable coil is designed and simulated by HFSS to investigate and compare the SAR distribution at 13.56 MHz and 40.68 MHz. The impacts of perfect alignment, lateral and/or angular misalignments on the PTE of the 3-coil inductive WPT system have also been analyzed. Additionally, the effects of different compositions of tissue,  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Ref.** & **Type of** & **Tech.** & **RX Coil** & **Operating** & **Distance** & **Medium** & **PTE (\%)** \\  & **Wireless Link** & & **Diameter (mm)** & **Frequency (MHz)** & **(mm)** & & \\ \hline
[32] & 2-coil & PSC & \(10\times 10\) & 13.56 & 10 & Air & 72.2 \\  & & & & & & Saline & 51.8 \\  & & & & & & Tissue & 30.8 \\ \hline
[33] & 2-coil & PCB & \(25\times 10\) & 13.56 & 10 & Air & 75 \\  & & & & & & Tissue & 58.2 \\ \hline
[34] & 4-coil & PCB & \(5\times 5\) & 13.56 & 10 & Air & 19.1 \\  & & & & & Tissue & 11.7 \\ \hline
[29] & 2-coil & - & \(10.8\times 10.5\) & 39.86 & 10 & Tissue & 47.2 \\ \hline This work & 3-coil & PCB & 19.9 & 13.56 & 10 & Air & 41.7 \\  & & & & & Tissue & 30.8 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison with the Conventional Works.  
input power, and coil size on the SAR is analyzed. For an input power of less than 2 W, the SAR produced is below the limit set by FCC at 13.56 MHz, but, at 40.68 MHz, the SAR exceeds the limit set by FCC for a low input power of 0.3 W. An analysis on the impact that various coil shapes have on SAR shows that irrespective of coil shapes, SAR is smaller at a low frequency than at a higher frequency. Furthermore, the simulated results obtained are experimentally validated through the air and boneless pork tissue mediums. The overall PTE and actual power delivered to the implantable coil are also measured. For the future work, a mm-sized implantable coil operating at 13.56 MHz will be designed and fabricated to achieve a high PTE and the wireless link will be tested on freely moving small animals.  
[PERSON] conceived, designed the study, gave the theoretical and simulation analysis; [PERSON] and [PERSON] designed the coils and carried out the experiments; [PERSON] and [PERSON] revised the whole manuscript; [PERSON] provided guidance and key suggestions for the system design and testing; [PERSON] wrote the paper.  
This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (Grant No. NRF-2016R1D1A3A03918084).  
The authors declare no conflict of interest.
## 4 Discussion
The decrease of the TDCP is not as expected in the case of Ni and it could be related to the formation of Al\({}_{3}\)Ni intermetallic compounds that are precipitated in the beginning of the solidification process of the alloy, at temperatures well above the TDCP and as described in Reference [26] because Ni provides significant changes in the sequence of post-eutectic reactions, promoting a substantial reduction in the alloy's freezing range. In both cases, the obtained results confirm the results obtained for the development of the Si equivalent method for obtaining the solidification temperatures, where Ni and Zn have a positive value, which means that they have an influence on decreasing the solidification temperatures [27; 28]. Ti is usually employed in the aluminum industry because it promotes the grain refinement of the aluminum alloys. If the grain is smaller, there are more dendrites in the solidification process, so their tips could touch one to another quicker, increasing the T\({}_{\text{DCP}}\) value, but without statistical relevance. The obtained results could also be correlated with the previous studies so that they show that an alloy refined with Ti has higher solidification temperatures than the unrefined alloys [15; 22]. Pb is usually precipitated in the grain boundary as isolated points and has a very restricted solid dissolution in the aluminum matrix. Because of this, Pb could tend to increase the T\({}_{\text{DCP}}\) value, but also without a statistical relevance. This result is also in concordance with a previous study [27; 29], where elements such as grain refiners (Ti and B) and silicon modifiers (Sr and Sb) or elements with a low melting point (Bi and Pb) have similar effects on the Si Equivalent value. The rest of the alloying elements also have a slight influence on the solidification temperature interval, but it is not very important and there is a complex interaction between them, obtaining a better adjustment of the results by adding all the alloying elements. The difference in the increase or decrease of the rest of the alloying elements can be related to the presence of intermetallic or eutectic compounds. If they precipitate before the DCP, they would decrease the T\({}_{\text{DCP}}\). Many of the alloying element could precipitate in different inter-metallics and eutectics (For example the Fe as Al\({}_{5}\)FeSi, Al\({}_{8}\)FeMg\({}_{3}\)Si\({}_{6}\), and others). By comparing the studied methods, the linear regression coefficient (r\({}^{2}\)) and the standard deviation (S\({}_{\text{ey}}\)) show that in all the cases, a good correlation between the developed formulae and the obtained results in r\({}^{2}\) values > 0.95 and Sey from 1.84 to 2.6 \({}^{\circ}\)C.  
More investigations with torque measurements should be done in order to define which one of the proposed methods is more exact in real DCP point determination and in the correlation between the different quantities of inter-metallics, types, and concentrations to have an estimation of the influence of the different inter-metallics over the T\({}_{\text{DCP}}\).
## 3 Proposed Methodology
### System Infrastructure  
#### 3.1.1 Network Model  
The system infrastructure comprises a single base station and many sensor nodes. There are two categories for each sensor node. The primary cluster node and the common node are the two different types. The public node's duties include monitoring the surroundings and providing perception data to the CH nodes. Common nodes are frequently selected as CH nodes, which gather, integrate, and send their data to the BS. Figure 1 depicts the design of the WSN architecture.
## Data and Methods
### Methods
The total value of GPP is obtained from the sum of the GPP values in each pixel multiplied by the pixel area in all research areas or in each land use type, while the average GPP value is obtained by dividing the total values of GPP by the number of image pixels. In this study, the speed of GPP changes was also calculated in all research areas and in each land use type. The analysis of land use change was performed for two periods (1992-2003 and 2003-2014) with the topographic data sources previously described. The land use change can be measured using Equation (8) in hectares and Equation (9) in percent, and the equations are defined as follows:\[LUC_{a}(ha) = (LU_{a,t}-LU_{a,t-1}) \tag{8}\] \[LUC_{a}(\%) = \frac{LUC_{a}(ha)}{LU_{a,t-1}}\times 100 \tag{9}\]  
where \(LUC_{a}\) (ha) is changes in land use type \(a\) in hectares from time \(t\) (current year) to time \(t-1\) (former year) and \(LUC_{a}\) (%) is land use change in percent. \(LU_{a,t}\) and \(LU_{a,t-1}\) are the total land area of land use type \(a\) in hectares at time \(t\) (current year) and time \(t-1\) (former year). In the current study, land use expansion in each type was also calculated and can be measured using a simple equation of the land use expansion index (LUEI). The LUEI can reflect the temporal patterns of expansion or reduction in each type of land use and is defined as follows:  
\[LUEI_{a}=\frac{LUC_{a}(ha)/LU_{a,t}}{N_{t}-N_{t-1}} \tag{10}\]  
where \(LUEI_{a}\) (ha year\({}^{-1}\)) is the annual expansion index for land use type \(a\) in hectares and can be converted to percent per year (% year\({}^{-1}\)) by multiplying by 100 and can also be converted to hectares per day (ha day\({}^{-1}\)) after dividing by the number of days in the year (356). \(N\) is the total number of years from time \(t\) (current year) to time \(t-1\) (former year). A positive LUEI value indicates expansion, and a negative value indicates reduction.
## 4 Results and Discussion
### Transfer Learning Setting  
The pre-trained TL network classifiers adopted for this research study, i.e., Inception-resnetv2, Inceptionv3, Xception, Resnet18, Resnet50, Resnet101, Shufflenet, Densenet201 and Mobilenetv2, can categorize images into 1000 different item classes and are trained on 1.28 million images of ImageNet database. The focus of this study is a three-class classification of brain tumors using the brain tumor classification (MRI) dataset. Furthermore, we employed a trial-and-error strategy. Experiments were carried out by assigning different values to the parameters to determine the optimum values for each parameter. We used stochastic gradient descent (SGD) to train pre-trained DL models through TL. We utilized a 0.01 learning rate and a 10-image minibatch size. In addition, each DL model was trained for 14 epochs to conduct the TL experiments for detecting and categorizing brain tumor types, accounting for the possibility of overfitting. We performed all experimentations on a machine equipped with Intel (R) Core (TM) i5-5200U CPU and 8 GB of RAM. For implementation, we used the R2020a version of MATLAB. The optimized parameters used for the classification experiment are shown in Table 2.
# Cooperative Spectrum Sensing Based on Convolutional Neural Networks
## 4 Main Contribution
### The Training of the CNNs  
The output layer of the LeNet network in this paper is activated by the sigmoid function. The Adam optimizer is used because the learning rate can be adjusted adaptively. The loss function uses a bivariate cross-entropy function. At the same time, to adapt to the shallow CNN and maintain the stable running of the model, the image size is reset to 36 \(\times\) 36 when reading the picture (two-dimensional data). Pictures are processed in batches, and the number of pictures in a batch is 100.  
Figure 4: The network architecture of AlexNet [39].  
Figure 5: The network architecture of VGG-16 [40].  
Batch normalization (BN) is used for the AlexNet network instead of LRN. Unlike the LeNet network, the random gradient descent optimizer (SGD) is selected for the AlexNet network. Like the LeNet network, the cross-entropy function is considered the loss function of the AlexNet network and the probability of random deactivation is 0.5. The algorithm resets the image size to 100 \(\times\) 100 when extracting the picture, and cuts it to 99 \(\times\) 99 in the center. The number of pictures is 100 in a batch.  
For VGG-16, the BN and Adam optimizer are used. The loss function is still the binary cross-entropy function, and the random deactivation is used in the full connection layer with the probability 0.2. The image size is reset to 100 \(\times\) 100 when extracting the picture. The input data are processed in batches, but when affected by GPU memory, the number of batches is reduced to 50.
# IoTBystander: A Non-Intrusive Dual-Channel-Based Smart Home Security Monitoring Framework
## 6 Dual-Channel Aggregation
### Event Verification and Fusion  
Note that TER cannot monitor dumb devices as they do not send event notification messages in the digital space. The absence of a certain event \(e\) in one channel cannot deny the presence of it in another one; on the contrary, merging the events recognized from both channels could yield a more comprehensive monitoring result if the recognition accuracy of each single channel is high enough. The two aligned output event sequences \(ES_{AER}\) and \(ES_{TER}\) are effectively aggregated into \(ES_{res}\) through a joint verification and fusion process of events.  
We build two prior-knowledge sets (\(ks_{AER}\) and \(ks_{TER}\)) for both audio and traffic channels, respectively, through an offline learning process: for each channel, we collect all the recognizable events and the corresponding recognition precision for each event and obtain a prior-knowledge set \(ks_{(\cdot)}=\{(e_{(\cdot),1},p_{(\cdot),1}),(e_{(\cdot),2},p_{(\cdot),2}), \cdots,(e_{(\cdot),m},p_{(\cdot),m})\}\), where \(p_{(\cdot),1}\) denotes the precision for recognizing the event \(e_{(\cdot),1}\) by \((\cdot)\) (AER or TER). Then, the two event sequences are traversed and merged following the algorithm below.  
* If an event \(e\) instance appears in both \(ES_{AER}\) and \(ES_{TER}\) with overlapping, it is verified to be correctly recognized. Thus, we merge the two time intervals of \(e\) in \(ES_{AER}\) and \(ES_{TER}\) by taking the wider side of each interval for the merged result and mark the time interval as event \(e\).
* If \(e\) appears within a time interval in one sequence (say \(ES_{AER}\)) but is not present in another sequence (say \(ES_{TER}\)) within the same time interval, we first check whether \(e\) is in \(ks_{TER}\). If so, a comparison \(ks_{AER}(e)>ks_{TER}(e)\) is evaluated: if true, \(e\) is determined as \"correctly recognized\" and will be added to the merged result; otherwise, \(e\) is a false positive and will not be merged. If \(e\) is not in \(ks_{TER}\), \(e\) will be added into \(ES_{res}\).  
The above algorithm, on one hand, combines the perception view of both channels and, on the other, excludes the recognized events with low confidence. The resultant event sequence \(ES_{res}\) will be output for further analysis.  
With the above dual-channel aggregation mechanism, IoTBystander is able to efficiently manage a wide variety of devices, including smart IoT appliances, traditional sensors, and non-smart devices. A key strength of our system is its ability to easily integrate additional monitoring channels as smart home ecosystems evolve. Users can seamlessly add new devices, such as cameras, power meters, or environmental sensors, without the need for significant changes to the underlying framework. This adaptability ensures that IoTBystander remains a highly scalable solution, capable of meeting the dynamic needs of the expanding smart home environments.
## 3 CE Methods and System Model
### System Model  
The architecture of the OFDM techniques and the OFDM baseband network is depicted in Figure 7. A serial-to-parallel converter is required at the transmitting edge to transfer the transmitted symbols and pilots into a parallel data flow. Inverse discrete Fourier transform (IDFT) converts frequency to time bands. Intersymbol interference (ISI) must be minimized using the cyclic prefix (CP). Channel delay spread should not exceed the CP duration. Complex stochastic variables determine the sampling space's multipath channel (MPC). \([b(n)]_{-}(n=0)^{*}(\mathrm{N}-1)\), can be used to describe the received message signal, as in [5]. Thus, the following equation expresses the received signal \(y(n)\):  
\[y(n)=x(n)\oplus b(n)+w(n) \tag{7}\]  
where \(x(n)\) is the transmission signal, \(w(n)\) is additive white Gaussian noise (AWGN), and \(\oplus\) is the periodic convolution. After implementing DFT and removing the CP, the receiving signal within the frequency band:  
\[Y(k)=X(k)H(k)+W(k) \tag{8}\]  
where correspondingly \(Y(k)\), \(X(k)\), \(H(k)\), and \(W(k)\) are the DFTs of \(y(n)\), \(x(n)\), \(b(n)\), and \(w(n)\).  
The initial OFDM block contains pilot symbols, while successive blocks contain transmitted data to create a frame. Although the channel changes between the different frames, it can be considered stationary over one frame. The proposed DL peephole LSTM-based CSE receives information and restores the sent information at its output.  
Figure 6: The architecture of GRU.  
Figure 7: Model of an OFDM system, orthogonal frequency division multiplexing.