esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### CART Model
Following the results, the boosted CART model demonstrated the best performance in determining the long-term unemployed category of jobseekers (those unemployed for more than 12 months), with a sensitivity of 75.8%. This implies that the model correctly predicted 3/4 of individuals who were unemployed for longer than 12 months to belong to the long-term unemployed category based on their input values. Furthermore, this category had the highest accuracy of 91.7%, indicating that almost 92% of jobseekers were correctly classified as either unemployed for more than 12 months or not. Additionally, the precision for this category was 94%; thus, for those individuals predicted by the model to have an unemployment duration of more than 12 months, over 94% experienced such a period of unemployment. Regarding the macro- and micro-average of the CART ensemble model evaluation statistics, we can say that model has a very good prediction accuracy of over 77%. This means that, on average, the predictions of the model on jobseekers' unemployment duration into one of four categories were correct in more than 77% cases. The sensitivity of the model in correctly identifying the right category of unemployment duration averaged over 53% (according to the macro-average) or nearly 55% (according to the micro-average). Compared to a naive model that randomly predicts one of the four categories with 25% accuracy, this model achieves highly satisfactory results. The importance of individual predictors in the model is illustrated in Figure 1. This figure displays the frequency of using individual input variables in the nine component models of the boosted CART model. Only those predictors that were used in the models at least seven times are shown in the figure. The remaining predictors were also used in the component models but are not displayed in Figure 1.  
We can conclude that the course of previous unemployment plays a crucial role in an individual's future unemployment duration. Among the variables used in all ten component models are, among others, _work before registration_ (the number of days from the last occupation to the current registration), _number of previous registrations_ and _average unemployment_ (average number of days spent in unemployment per year). The variable _cumulative previous registrations_ (the cumulative days of previous registrations before the period under review) is missing in only one component model, while _numbers of previous registrations with interventions_ or _without interventions_ appear in eight out of ten component models. Since we are unable to illustrate the boosted CART model with a dendrogram, given that it consists of ten component models, we instead present the CART model without the boosting technique in Appendix A, and the same model in a set of rules for the individual  
Figure 1: Frequency of using predictors in the component models of the boosted CART model. values of the target variable in Appendix B. It is very interesting to observe how the input characteristics of jobseekers influence the duration of their unemployment.
## 2 Calculation Methodology of the PVCS Global Cost  
In this section, the cost aspect of the PVCS is deepened. The purpose is to be able to provide the global cost of the PVCS, including the costs of investment \(C_{i}\), exploitation \(C_{\text{exp}}\), maintenance \(C_{\text{maint}}\), and externalities \(Ext\). This calculation methodology is detailed over a 30-year analysis period, as the average lifespan of a PV panel is 30 years. Then, the global cost \(C_{g}\) is expressed in Equation (1):  
\[C_{g}=C_{i}+C_{maint}+C_{\text{exp}}-Ext \tag{1}\]
## 5 Simulation Results and Analysis
### Setup and Parameters  
The proposed system in this paper operates in the Ku band. The values of the relevant simulation parameters are given in Table 1.  
The values of the parameters of the NPA model are set as \(\beta_{1}=2.96\) and \(\beta_{3}=0.1418\exp\{-j2.816\}\). The axial angle \(\omega_{k,l}\) of the antenna beam of the \(l\)th satellite with respect to the \(k\)th GU is continuously and uniformly distributed in the interval \((0,\pi/5)\), and the channel spatial angles \(\mathbf{v}_{k,l}^{x},\forall k,l\) and \(\mathbf{v}_{k,l}^{y},\forall k,l\) obey independent and identical uniform distributions in the interval [\(-1\),\(1\)]. In addition, the power consumption of the circuits for each satellite includes the power consumption of the phase shifters \(NP_{PS}\), the power consumption of the switches \(NP_{SW}\), the power consumption of the intrinsic oscillator \(P_{LO}\), the power consumption of the baseband digital precoder \(P_{BB}\), and the power consumption of the RF chain \(N_{RF}P_{RF}\).
## 3 Related Work and Methodology
### Global and Local Moran's I
#### 3.3.1 Spatial Weight Matrix  
The estimation of a spatial weight matrix is essential in the context of Global and Local Moran's I analysis. This matrix defines the spatial relationships between individual data points, specifying which observations are neighbors and to what extent they influence one another within a given geographic area. Thus, the accurate estimation of the spatial weight matrix is a crucial prerequisite for meaningful spatial analysis and the interpretation of results [41]. In this study, the spatial weight matrix characterizes the interactions between geographical earthquake epicenters, primarily predicated on the proximity or contiguity of the mapped areas. Spatial weights are formulated based on the adjacency connections among these territories [42]. The construction of these weights is defined as follows:\[\mu=\begin{bmatrix}\mu_{11}\mu_{12}\cdots\mu_{1m}\\ \mu_{21}\mu_{22}\cdots\mu_{2m}\\ \vdots\quad\vdots\quad\vdots\\ \mu_{m1}\mu_{m2}\cdot\mu_{mm}\end{bmatrix} \tag{5}\]  
where \(m\) represents the number of regions, and \(\mu_{ik}\) indicates the spatial adjacency relation between regions \(i\) and \(k\).
# Detecting and Measuring Defects in Wafer Die Using GAN and YOLOv3
## 1 Introduction
[13] applied cycle-consistent adversarial network (CycleGAN) to generate the saw-mark defect in heterogeneous solar wafer images and to solve the unbalanced classification problem arising in manufacturing inspection. Their experiment showed that the CNN's classification accuracy rates of GAN-based data augmentation were better than those of doing over-sampling and assigning higher class-weights on minor classes. In addition to the research related to defect classification, the GAN-based method was also applied to the field of defect detection. [PERSON] et al. [14] introduced an image generating process for welded joints, which was based on the affine transformation and CycleGAN. Then the YOLOv3 model was used for the welding head detection, with a better average precision (AP) of 91.02% than faster region-based convolutional neural network (Faster RCNN). [PERSON] et al. [15] used CycleGAN to augment images of healthy apples and apples with anthrax, thereby increasing the number of images and enriching its content. After that, YOLOv3-dense was used to test the apple for anthracnose. Experiments showed that their model performs at an AP of 95.57%. This method could also be applied to the detection of apple surface diseases in orchards. However, some welded joints on metals or anthraces on apples generated by CycleGAN are not expected output [14; 15]. After augmenting the data using GAN, rich pseudo images are obtained. Although the appearance diversity of defects increases, no corresponding annotation files are provided [14; 15], as operators have no time for time-consuming annotation work. Besides, GAN is currently unable to form specific structures, generative images that are not only blurry but also incorrectly colored. These undesirable generative images should be manually deleted. Cooperating GAN with an automatic annotation method is another contribution of this study, so that the data pairs are available for training the YOLOv3 die defect detection model. This research uses a series of pre- and post- digital image processing (DIP) techniques to reduce the generation loading of GAN and to develop an autoannotation procedure for pseudo defective images. The DIP techniques not only help to generate realistic pseudo defects but also save the time needed for annotating defective pseudo images. This paper is composed of four parts. Following this introductory section, which has summarized the literature on die inspection and presents the contributions of this study, is the second section that describes the hardware architecture for capturing images, and the methodology. It also introduces GAN, the automatic annotation mechanism, and the modified application of YOLOv3. The third section presents the experimental results. A spot-checking process helps us to determine the YOLOv3 as the base model. The hyperparameters to be used in the GAN + YOLOv3 mechanism are derived, based on the design of the experiment (DOE). The defect detection results are reported, compared, and analyzed. The final part is the conclusion.
## 2 Theoretical Background and Mathematical Formulation
### Analytical Impact Force Model for Bang Machine  
Figure 3 shows the mean value and data obtained from 10 field measurements for the bang machine. The maximum value of the measurements ranged from 3865 to 3903 N, and the mean value was 3890 N. According to the study of [PERSON] _et al._[23] as well as Japanese standards JIS A 1418-2 [23], the maximum load was 3900 N for the bang machine.  
However, 4500 N was presented in the commentary of the KS F 2810 [15]. The impact time subjected to the impact load was measured at 21.24 ms, and is deemed to be appropriate when compared with the impact time presented in KS (20 \(\pm\) 2 ms). With respect to the measurement value, the impact exposure level was calculated and compared with KS. As shown in Figure 3b, there was an excess of 4 dB and 3.8 dB in comparison with the standard value with respect to 250 Hz and 500 Hz, respectively.  
For the development of the analytical impact force model, the optimization was performed using Equations (2)-(4). The analytical impact force model for the bang machine is expressed in Equation (5), and the coefficients are summarized in Table 3.  
\[M_{BM-IF}\left(x\right)=C_{0}\times x^{6}+C_{1}\times x^{5}+C_{2}\times x^{4}+C _{3}\times x^{3}+C_{4}\times x^{2}+C_{5}\times x+C_{6} \tag{5}\]  
where \(M_{BM-IF}(x)\) is the proposed impact force model for the bang machine, \(x\) is the input variable, (_i.e._, time), and \(C_{0}\sim C_{6}\) are coefficients for the model.  
Figure 4a shows the impact load for the bang machine which is artificially generated using Equation (1), and the impact time was 20.874 ms (\(\Delta t=1/8192\)). The impact exposure level for both the mean of experimental test and the proposed model is summarized in Table 4. The impact exposure level, shown in Figure 4b, was found to adhere well to KS.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Coefficient** & **Value** \\ \hline C\({}_{0}\) & \(-0.001054\) \\ C\({}_{1}\) & \(0.062730\) \\ C\({}_{2}\) & \(-1.175034\) \\ C\({}_{3}\) & \(5.420442\) \\ C\({}_{4}\) & \(8.873756\) \\ C\({}_{5}\) & \(411.677662\) \\ C\({}_{6}\) & \(-12.918740\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: The coefficients for the proposed bang machine impact force model.  
Figure 3: Ten experimental test data results of the bang machine: (**a**) time series data; and (**b**) impact exposure level.
## 3 Results and Discussions  
### Dose-Effect of Toxic Substances on the ROUR  
PTEs exhibited significant inhibition effects on the ROUR (Figure 3). Generally, the ROUR decreased dramatically with the increasing concentration of PTEs, 12.0 \(\pm\) 5.3% and 22.0 \(\pm\) 15.1% for Zn\({}^{2+}\) and Ni\({}^{2+}\) (60 mg/L), respectively. As for phenol, a similar declining behavior was found and the ROUR decreased to 36.0 \(\pm\) 5.5% when phenol concentration was 60 mg/L. Additionally, our results indicated a positive correlation between the ROUR-reciprocal and the concentration of toxic substances, suggesting that Hill equation could describe the equilibrium inhibition state [57], as expressed in Equation (3).  
\[\text{ROUR}=\frac{k_{\textit{Toxicity}}^{-1}}{k_{\textit{Toxicity}}^{-1}+[ \textit{Toxin}]} \tag{3}\]  
where \(k_{\textit{Toxicity}}^{-1}\) was 13.40, 15.54, and 8.21 mg/L for Zn\({}^{2+}\), Ni\({}^{2+}\), and phenol, respectively.  
The ROUR reflects the metabolic activities of the activated sludge, which are normally catalyzed by enzymes in microbial cells [41]. As the biodegradation of pollutants by the activated sludge depends on the microbial activities [58], higher concentration of metals can damage enzymatic activities, consequently resulting in the inhibited activities or even death of cells [59, 60]. Accordingly, the ROUR decreased gradually with the increasing PTE concentrations. In the present study, the EC\({}_{50}\) of Zn\({}^{2+}\) for the activated sludge (13.40 mg/L) was similar with those obtained from individual bioreporter strains, e.g., 10.9 mg/L by _Psychrobacter_ sp. bioreporters [24] and 16 mg/L by _Pseudomonas_ Shk1 bioreporters [42], but much lower than other previous studies on the activated sludge (around 50 to 60 mg/L) [42, 61]. Similarly, the EC\({}_{50}\) of Ni\({}^{2+}\) and phenol in the present study was also lower than previous studies on the activated sludge (21-190 mg/L for Ni\({}^{2+}\) and 416-608 mg/L for phenol) [48, 62, 63] or by _Pseudomonas_ Shk1 bioreporters (96 mg/L for Ni\({}^{2+}\) and 482 mg/L for phenol) [64, 65]. Different experimental system might explain this discrepancy. Most of the previous studies used an enclosed system to test the oxygen consumption after 10 min-aeration [42, 61], which was easy in operation but hardly applied for online OUR monitoring as the wastewater samples required sequential collection. In contrast, our laboratory monitoring system used the batch reactor by injecting wastewater and the activated sludge simultaneously and cultivating the mixture with different HRT (ranging from 1 to 60 min, 10 min for the dose-effect of PTEs), consequently resulting in relative longer exposure of the activated sludge to the toxic substances and lowering the EC\({}_{50}\). Our results suggested that the EC\({}_{50}\) varied across toxic compounds, and the developed laboratory OUR monitoring system could simulate WWTP-like process and provide accurate EC\({}_{50}\) values to evaluate the impacts of the shock load by industrial wastewater.
## 5 Experimental Results
### Experimental Setup  
A DPI system is created to evaluate the performance of the proposed multi-pattern-matching algorithm in the DPI-matching process. The pattern-matching is conducted on the GPU, which accelerated the processing of network packets with its parallel-computing capability. The pattern-matching on the GPU is performed in three steps. Figure 8 shows the steps of the pattern-matching process on the GPU.  
Network packets are obtained using the libpcap packet capture library created for Unix systems. The first step of the pattern-matching process is transmitting these network packets from the CPU to the GPU memory via PCIe (1). Due to the additional overheads associated with conveying data from the CPU to the GPU, first, all packets are transferred to a buffer, and then the buffer is passed to the GPU. Next, the DPI pattern-matching is executed on the GPU (2). Finally, the pattern-matching results are transferred to the CPU memory via PCIe (3). Table 5 shows the hardware configuration used.  
The pattern sets used in the analysis are obtained from the SNORT [46] rules, which contained the patterns of network packets containing malicious activities. Table 6 shows the average lengths of the patterns in the datasets containing different numbers of patterns. The lengths of these patterns range from 10 to 171 bytes. After obtaining the patterns, the network packets containing these patterns in their payload are created to obtain a dataset. Each network packet in the dataset is 256 bytes and generated by adding a randomly chosen pattern to a random packet payload location. This dataset, consisting of network packets containing malicious activities, is used in the analysis to evaluate the performance of the PA in the DPI-matching process.
## 2 Analysis of the State of the Art
### [PERSON] _et al._
#### 2.1.2 Users Models Comparison
The first approximation for this is found in [PERSON] _et al_. [32] work. They present a user profile which abstracts from physiological aspects and let users to manage and configure their own profile. Just in the opposite, [PERSON] _et al_. [36] model user capabilities as a set of abilities which allow users to perform some task or activities. On the other hand, [PERSON] comments in [19] that it not only is difficult to model users because of the wide range of different types of people that exist. He also considers that each individual changes with experience and through time. For example, old people's capabilities decrease with ageing. This idea is shared with [PERSON] _et al_. [5], whose work is centred around the elderly. [PERSON] [41] not only considers that users might evolve (from an AI perspective), but he also takes new context information from the inference process. This also opens a new point of view that we address in Section 1.1 and it takes context as a significant user's environment entity that might directly influence the user's capabilities. In other words, users change through experience, time and, in concrete situations, due to the current context characteristics. For example, an individual might not suffer from any mobility disability, but in a crowded street would be difficult to perform several daily activities (just walking could be difficult). [PERSON] [21]_et al._ also address this issue when they talk about the implicit user information collecting process. This, of course, deals with the concept of dynamism. [PERSON] _et al._[34] consider that respecting user's interactive behavior with applications needs to be taken into account. On the other hand, [PERSON] [26] analyses the differences between emotional and perceptional user characteristics. Table 1 summarizes the analyzed approaches for user modeling emphasizing the modeled user characteristics and domains. Besides, although in a first version every used technique was remarked, for this paper we have focused on emphasizing just those which follow an ontology-based approach. This is because many of the cited works are more theoretical or surveys, or they just give some advices about important context data when facing a context modeling task. Besides, [PERSON] and [PERSON] [40] demonstrate that using ontologies is more appropriate for modeling context-aware systems.
# Use of Machine Learning Methods for Indoor Temperature Forecasting
## 2 Methodology and Materials
### Selection of Predictive Models
#### 2.3.2 Gray Box Model (GBM)  
Hybrid models have been the subject of numerous studies. They have been widely used in the field of predictive control [67; 68; 69] as well as in the area of predicting building thermal load [70; 71] and indoor temperature forecasting [3]. The most common method for creating this model is applying a resistance-capacity (RC) form based on physical and statistical approaches [72; 73; 38; 74]. The thermal resistance R represents the component to resist the heat flux, and the thermal capacity C describes its storage capacity.  
In this work, a simplified (RC) model (Figure 6) was developed to thermally model the considered room.  
(T\({}_{1}\), T\({}_{2}\)) are the respective outdoor and indoor temperatures of the first wall, (T\({}_{5}\), T\({}_{4}\)) the respective outdoor and indoor temperatures of the second wall, and T\({}_{3}\) the indoor air temperature. Q\({}_{\rm h}\) is the heat source power.  
[Q\({}_{\rm h}\) T\({}_{1}\) T\({}_{5}\)] and [T\({}_{2}\) T\({}_{3}\) T\({}_{4}\)] represent the RC model's input and output vectors, respectively.  
The model's parameters (R\({}_{1}\), C\({}_{1}\)), (R\({}_{2}\), C\({}_{2}\)), and (R\({}_{3}\), C\({}_{3}\)) respectively designate the thermal resistance and capacity of the first wall, the indoor air, and the second wall.  
The model can be expressed as a linear stochastic differential equation written into a matrix form for state-space representation by applying [PERSON]'s balance laws to the circuit [75]. In addition, it includes a state equation and an output equation:  
\[\left\{\begin{array}{l}\dot{T}=AT+BU\\ Y=CT+DU\end{array}\right\} \tag{3}\]  
The T vector contains the node temperatures, U the controllable inputs and disturbances, Y the measured output; A, B, C, D matrices have the RC parameters to be identified.  
Figure 6: 3R3C room model.  
As for the parameters of the models, they are determined using the grayest function in MATLAB. The initial values of (R\({}_{2}\), C\({}_{2}\)) and (R\({}_{1}\), R\({}_{3}\)) were selected by applying the French thermal code (RT 2005-2012), while those of (C\({}_{1}\), C\({}_{3}\)) were estimated based on the equations characterizing the walls mentioned in the building thermal code [66; 76] (see Appendix C).
# Performance Analysis of a Color-Code-Based Optical Camera Communication System
###### Abstract  
In this study, we present a visible light communication (VLC) system that analyzes the performance of an optical camera communication (OCC) system, utilizing a mobile phone camera as the receiver and a computer monitor as the transmitter. By creating color channels in the form of a \(4\times 4\) matrix within a frame, we determine the parameters that affect the successful transmission of data packets. Factors such as the brightness or darkness of the test room, the light color of the lamp in the illuminated environment, the effects of daylight when the monitor is positioned in front of a window, and issues related to dead pixels and light bleed originating from the monitor's production process have been considered to ensure accurate data transmission. In this context, we utilized the PyCharm, Pydroid, Python, Tkinter, and OpenCV platforms for programming the transmitter and receiver units. Through the application of image processing techniques, we mitigated the effects of daylight on communication performance, thereby proposing a superior system compared to standard VLC systems that incorporate photodiodes. Additionally, considering objectives such as the maximum number of channels and the maximum distance, we regulated the sizes of the channels, the distances between the channels, and the number of channels. The NumPy library, compatible with Python-Tkinter, was employed to determine the color levels and dimensions of the channels. We investigate the effects of RGB and HSV color spaces on the data transmission rate and communication distance. Furthermore, the impact of the distance between color channels on color detection performance is discussed in detail.  
Keywords:optical camera communication; visible light communication; optical wireless communication +
Footnote †: journal: _Sci_