esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimizing Automated Trading Systems with Deep Reinforcement Learning
## 4 Experiment
### Experiment Procedure  
To avoid the case where the optimization techniques are over-fitted or the obtained parameters do not yield any profit in other periods, we consider 100 periods with the size of each period being 3456 observations (36 days). For each period, the start date and end date are different, the first 80% of the dataset is dedicated for training purposes and the remaining 20% is used for testing the performance. This ratio is chosen according to the Pareto principle, which is commonly applied to optimization efforts in computer science [33]. Without a loss of generality, other ratios can also be applied.  
The parameters used for training the agent are shown in Table 2. The agent is assumed to start with an initial capital of 1,000,000 and a cost of 0.1% is applied to each executed transaction for a more realistic study. The values of other parameters in the system are selected from practice and from previous studies [3; 29; 34]. In our case, future rewards play a more important role because of the volatility of the price, so in this study, \(\gamma\) is assigned a value of 0.98. An initial \(e_{start}=1\) is selected for the first observations and then is set to a new value \(e_{end}=0.12\) after 300 observations (\(e_{step}=300\)). Studying the experiment in [3], we apply the ADAM algorithm to optimize the weights because of its simplicity in implementation, computational efficiency, and low memory requirements. This algorithm is suitable for large data and parameter problems when compared to other stochastic optimization methods such as RMSProp [35], AdaGrad [36]. The Mean Squared Error is used as the loss function for simplicity, the activation function is set as the Leaky Rectified Linear Units function because of additional gains in final system performance relative to more commonly used sigmoidal nonlinearities [34]. The learning process of agents is powered by Double Deep Q-Network (DDQN) and Dueling Double Deep Q-Network (D-DDQN). In both cases, the networks are composed of 2 Convolutional Neural Network (CNN) layers with 120 neurons each. In the case of D-DDQN, CNN layers are followed by two streams of fully connected layers: the first with 60 neurons dedicated to estimate the value function and the second with 60 neurons to estimate the advantage function. We compare the results of the system against two objective functions, the cumulative return and the Sharpe ratio. The performance statistics from the DRL approach are compared with the Bayesian Optimization approach and discussions are presented.  
Figure 3: Average price increment ranges by weekday.  
Three evaluation metrics are introduced to evaluate our results. The first metric is the average reward, which is the average of daily returns over the experimental period. The second metric is the average standard deviation of daily returns. The third metric is the total cumulative reward, which is the total returns at the end of the trading episode. The results from the metrics are discussed together to choose the best configuration for the proposed trading system.
## Appendix D The ST-FIS Method
### Mean Absolute Deviation  
We then arrange a labeling index \(q=1,2,\ldots,100\), to simulate replicates \(z(q,j)_{1},z(q,j)_{2}\) and \(z(q,j)_{3}\). For the 2-th student, this produces simulated grades \(estfis(2,z_{q},D)\) (cf. Equation (84)). Considering the objective performance rate \(estfis(2,zo,D)\) as a proxy of the mean value for teacher's assigned grades \(estfis(j,z,D)\), we calculate the Mean Absolute Deviation \(\Delta\overline{estfis}(j,z_{q},zo,D)\) (cf. Equation (88)) that estimates the average rating error for \(estfis(j,z,D)\), relative to \(\overline{estfis}(j,zo,D)\). This is given by  
\[\Delta\overline{estfis}\big{(}j,z_{q},zo,D\big{)}=0.06\] (A127)  
Obtaining the Complexity \(Cp_{st-fis}\), Consistency \(CI_{st-fis}\) and Efficience \(\beta_{st-fis}\) Indexes Values  
For this case, we set numbers \(N_{ST-FIS}=13\) of components \(E_{ST-FIS}=110\) of elements, and \(Q_{ST-FIS}=53\) of operations that the ST-FIS involves (cf. Table 7). The minimum and maximum values for \(N_{M}\), \(E_{M}\) and \(Q_{M}\) entries across the addressed methods in this study are given in Table 2. Then agreeing to Equations (89)-(91) for the ST-FIS method, we take Pondered-Normalized indicator values of \(\mathit{PN}(N_{ST-FIS})=1,\mathit{PE}(E_{ST-FIS})=0.98\), and \(\mathit{PQ}(Q_{ST-FIS})=0.88\). (cf. Table 3). Also, according to Equation (96) we assign values \(a=0.4,b=0.3\) and \(c=0.3\) (cf. Table 4). reflecting the relative importance of each one of the afore given Pondered-Normalized factors in the determination of \(\mathit{CP}_{ST-FIS}\). Then, as stated by Equation (95), we have,  
The complexity pointer as given by Equation (95) acquires a value of  
\[CP_{ST-FIS}=0.4(1)+0.3(0.98)+0.3(0.88)=0.96\] (A128)  
In turn, as stated by Equation (68), the \(MAD_{ST-FIS}\) entry attains a value of  
\[MAD_{ST-FIS}=\frac{\sum_{q=1}^{\theta(m)}\Big{|}2.5/5-estfis(j,z_{q},D)\Big{|} }{\theta(m)}=0.06\] (A129)  
Then, according to Equation (100), we have a \(CI_{ST-FIS}\) value of  
\[CI_{ST-FIS}=\frac{1}{1+0.06\times 0.96}+CP_{ST-FIS}=1.91\] (A130)  
And correspondingly as stated by Equation (101) we have,  
\[\beta_{ST-FIS}=\frac{(1.91)(0.96)}{(0.06+1)\cdot(0.96+1)}=0.88\] (A131)
# Location-Based Lattice Mobility Model for Wireless Sensor Networks
## 4 Simulation Setup and Analysis of Results
### (D) Residual energy  
Residual energy is the remaining energy level of the sensor nodes after completing the task. Here, we discuss the average residual energy level of the sensor nodes after changing locations. Figures 11-13 compare the residual energy of LMM with those of the CMM, RWMM, and WMM at 10%, 25% and 50% mobile sensor nodes. The sensor nodes have a higher residual energy with the LMM after changing location seven times.  
Figure 11: Residual energy after completion of the ninth cycle at 10% mobility rates.  
Figure 10: Packet drop rates using the LMM, CMM, RWMM, and WMM at different velocities.  
The residual energy of the LMM decreases from 0.12 to 0.38 Joules at 10%, 25% and 50% mobility rates, which is a smaller decrease equivalent to 10.27% after completion of nine cycles than for the other mobility models. The energy levels of the other mobility models decrease from 0.28 to 0.756 Joules after completing ten motion cycles (the completion of event monitoring), which is equivalent to 20.27%. It is almost a double energy consumption waste.  
**(E) Time Complexity**  
The better performance of mobility model depends on the minimum time complexity. The time complexity is measured by manipulating the number of basic operations performed by the mobility model and constant amount of time to accomplish. Our proposed LMM gives lower time complexity as compared to other mobility models. LMM and competing mobility models are based on recursive approach. Therefore, time complexity of mobility models can be obtained using recursive approach shown in Table 3.  
\[T(n)=\left\{\begin{array}{c}O(1)\;If\;n=1\\ at(\frac{n}{5})+O(n)\;If\;n>1\end{array}\right. \tag{31}\]  
Figure 12: Residual energy after completion of the ninth cycle at 25% mobility rates.  
Figure 13: Residual energy after completion of nine cycles at 50% mobility rates.
# Derivation and Verification of Laminated Composite T-Beam Theory
## 3 Model of Finite Element Method  
A simple support laminated composite T-beam, made of AS4/3501-6 carbon/epoxy, was divided into the web and flange to construct its geometric model. The web lamination sequence, symmetric flange lamination sequence, and non-symmetric flange lamination sequence were \([90/45/-45]_{s}\), \([90/45/-45/0/0]_{s}\), and \([0_{4}/90/45/-45/-45/45/90]\), respectively. The finite element software, MSC/NASTRAN, was used to analyze the composite laminated T-beam [29,30]. The material properties of the laminated plates include \(E_{11}=1.27\times 10^{11}\) GPa, \(E_{22}=1.1\times 10^{10}\) GPa, \(v_{12}=0.28\), and \(G_{12}=6.55\times 10^{9}\) GPa. The type of the element was set as the quadrilateral shell element of a 2D orthotropic. The thickness of each laminate was \(1.3208\times 10^{-4}\) m. The beam was a simple support structure whose web was subjected to a uniform force, as shown in Figure 2(a). The point O boundary was set as the pin joint. The translational degrees of freedom were constrained in the x- and z-directions of the support points. The pin joints constrain the structure to the position of the support point; however, they allow the support point to rotate. In addition, the point O' boundary was set to the roller joint. The nodes on the roller were only allowed to translate parallel to the roller support surface and allow the support point to rotate. The length of the beam, flange width, and web height were 0.127, 0.0127, and 0.01905 m, respectively. The beam was set as a simply supported beam with a length of 0.127 m at the boundary condition. A distributed force of 1737.4 N/m was applied to the web. The data convergence or divergence was determined by dividing the maximum deflection value obtained by different element densities in the FEM. The maximum deflection of the supported beam was observed to be located at the center of the beam. When the mesh density per unit length was set higher than 50 elements/mm, the deflection of the beam approached a convergent value, as shown in Figure 2(b), where 12,600 elements and 13,051 nodes were used to achieve the highest accuracy in the FEM.  
Figure 3: \(Cont.\)
## 4 Design
### Design of Kinetic Solar Panel Facade System  
The solar panel facade system to be applied to the Signal Box auf dem Wolf's elevation is designed to be movable. The solar panels to be installed on the building elevation were arranged according to the optimal tilt angle calculated to maximize solar energy generation efficiency every month. The advantage of such a kinetic solar panel facade system is that more solar energy is generated compared with existing solar panels installed at a fixed angle. Second, the kinetic solar panel facade also functions as a sunshade to control sunlight entering the building. The solar panel, which could be moved according to the angle desired by the user, controlled the amount of sunlight entering the building. The size of the solar panel followed the standard for the copper material attached to the existing building elevation. As a result, a solar panel with a length of 2 m and a width of 1 m was planned (Figure 4).  
The solar panel used in this study was the Hanwha Q CELLS Q. PEAK DUO model. Hanwha Q CELLS is among the world's largest and most recognized photovoltaic panel manufacturers. They are renowned for their reliability and the state-of-the-art technologies they use in their modules and cell designs [53]. The Q. PEAK DUO ML-G11 solar module had a power generation efficiency of up to 21.5% and a power capacity of 500 Wp. It is 2054 mm long and 1134 mm wide. The standard size of this model was easily applied to the conceptual design of the elevational solar module in this study (Table 3).  
Figure 4: Principle of solar panel facade system and optimal tilt angle (\(\beta\)o).  
Figure 3: Cumulative solar radiation on the facades of the Signal Box auf dem Wolf for one year in 2022.  
To increase the energy generation efficiency of the solar panel, it must be tilted at a certain angle. We calculated the optimal tilt angle of the solar panel every month to maximize its energy generation of the solar panel using the following equation [54].  
\[\beta o=a_{1}+a_{2}\varphi \tag{1}\]  
Here, \(\beta o\) is the optimal tilt angle, \(\varphi\) is the latitude of Basel (47.57\({}^{\circ}\) N), and \(a_{1}\) and \(a_{2}\) are coefficients derived from solar declination values. Table 4 lists the optimal tilt angle values by function from January to December 2022.  
Kinetic solar panel facade systems were placed according to the monthly optimal tilt angle values on the south, east, and west facades of the Signal Box auf dem Wolf for the solar energy simulation described in Section 3.1. The solar panels were changed according to the different angles of each month. We used Dynamo, a Revit software plug-in, to simulate a solar panel facade according to the angle change.
## Performance Analysis of Cooperative Virtual MIMO Systems for Wireless Sensor Networks  
**[PERSON], [PERSON] * and [PERSON]**  
Department of Electrical and Electronic Engineering, Auckland University of Technology,  
Private Bag 92006, Auckland 1142, New Zealand; E-Mails: [PERSON]DDRESS] (Z.R.);  
[EMAIL_ADDRESS] (A.A.-A.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +64-9921-9999; Fax: +64-9921-9973.  
_Received: 1 May 2013; in revised form: 27 May 2013 / Accepted: 27 May 2013 / Published: 28 May 2013_  
**Abstract:** Multi-Input Multi-Output (MIMO) techniques can be used to increase the data rate for a given bit error rate (BER) and transmission power. Due to the small form factor, energy and processing constraints of wireless sensor nodes, a cooperative Virtual MIMO as opposed to True MIMO system architecture is considered more feasible for wireless sensor network (WSN) applications. Virtual MIMO with Vertical-Bell Labs Layered Space-Time (V-BLAST) multiplexing architecture has been recently established to enhance WSN performance. In this paper, we further investigate the impact of different modulation techniques, and analyze for the first time, the performance of a cooperative Virtual MIMO system based on V-BLAST architecture with multi-carrier modulation techniques. Through analytical models and simulations using real hardware and environment settings, both communication and processing energy consumptions, BER, spectral efficiency, and total time delay of multiple cooperative nodes each with single antenna are evaluated. The results show that cooperative Virtual-MIMO with Binary Phase Shift Keying-Wavelet based Orthogonal Frequency Division Multiplexing (BPSK-WOFDM) modulation is a promising solution for future high data-rate and energy-efficient WSNs.  
**Keywords:** cooperative virtual MIMO; wavelet based OFDM; V-BLAST; wireless sensor networks
# Deep Activation Pooling for Blind Image Quality Assessment
## 3 Experiments
### Evaluation Protocols and Databases  
We utilize two indices in the experiments. The first index is the Spearman rank-order correlation coefficient (SRCC), which is between the subjective mean opinion scores (MOS) and the objective IQA scores. The second index is the Pearson linear correlation coefficient (PRCC), which is between the MOS and the objective IQA scores after a linear regression [23]. The SRCC and PRCC are used to evaluate the prediction monotonicity and consistency, respectively. The values of the SRCC and PRCC reflect the performance of the BIQA model. Typically, the higher the SRCC and PLCC values are, the better the BIQA model is.  
We utilize the SRCC and PRCC to evaluate the performance of the proposed method on two well-known IQA databases, the CSIQ [24] and LIVE [25] databases. The CSIQ database consists of 866 distorted images generated from 30 source images. Six types of distortions at five different levels are applied to the 30 source images, that is, additive white noise (AWN), Gaussian blur (GB), global contrast decrements (CTD), additive pink Gaussian noise (PGN), JPEG compression and JPEG2K compression. Some samples from the CSIQ database are shown in Figure 4. The LIVE database has 779 distorted images generated from 25 source images with five types of distortions at various levels. The distortions include simulated fast-fading Rayleigh channel (FF), GB, JPEG2K compression, JPEG compression and AWN. Figure 5 shows some images from the LIVE database.  
Figure 3: Visualization of the DAP. (**a**) An image sample from the CSIQ database; (**b**) the CSM in the 7 th convolutional layer of the VGG-19 network. The _p_-th yellow rectangle is used to obtain \(s_{p}\) (\(s_{p}\) represents the local quality scores of the _p_-th patch), and the _p_-th green rectangle is used to compute \(w_{p}\) (\(w_{p}\) is the corresponding weight of the _p_-th patch).
## 2 Materials and Methods
### Data
#### 2.2.3 Meteorological Data  
The observational meteorological datasets used in this study include daily precipitation and temperature with a spatial resolution of 0.5\({}^{\circ}\) from 1979 to 2016. The grid precipitation and temperature datasets originated from daily and monthly precipitation and temperature data at 2472 meteorological sites. The data were processed by quality control and then interpolated from station data to grid data, with only stations with data available from 1961 used. A digital elevation model (Global 30 Arc-Second Elevation, GTOPO30) was introduced in order to weaken the effects of elevation on the interpolation precision of temperature and precipitation [38].
## 4 ARCOMEM Data Management
### ARCOMEM Storage
#### 4.2.2 The ARCOMEM Knowledge Base  
The task of the knowledge base is to handle the data that derives from the annotation of the Web objects, as performed by the online, as well as the offline processing modules. Such annotations are described using the ARCOMEM data model (see Section 4.1) that interlinks ETOEs and points to actual content residing in the object store. Thus, the problem translates to building an efficient processing engine that appropriately indexes and stores RDF triples and offers SPARQL querying capabilities, while maintaining the scalability and high-performance characteristics.  
Existing solutions fail to simultaneously offer both query expressiveness and scalability/performance. Thus, we have designed, H\({}_{2}\)RDF [19, 20], a distributed triple index solution, following a hybrid approach that combines the power and expressiveness of an RDF engine with the performance and scalability of NoSQL and MapReduce.  
Figure 5: Sample content of the object store.  
More specifically, H2 RDF implements an enhanced version of the centralized Hexastore indexing scheme [21] over HBase. Hexastore is an RDF store that creates six indices (for all possible permutations of subject, predicate and object \(\{S,P,O\}\): SPO, PSO, POS, OPS, SOP, OSP) in main memory, thus offering the ability to retrieve data for any triple pattern with minimal cost. Taking this one step further, our distributed knowledge base creates three indices ultimately stored in HBase tables. These indices correspond to three combinations of S, P and O, namely SP_O, PO_S and OS_P. The knowledge base is able to provide native SPARQL query functionality, with joins being executed either as MapReduce jobs or as centralized jobs according to their cost. This ensures interactive response times for small selective queries, as well as scalability and distributed execution for large, non-selective queries. Figure 6 pictorially presents the knowledge base architecture. Extensive experiments presented in [19,20] prove the ability of our knowledge base, H2 RDF, to scale to billions of triples and to handle concurrent user requests, achieving fast response times.
## 4 Implementation issues
### Test data  
To verify the method effectiveness against real web pages, a sample of pages actually visited on a live network is needed. For this purpose, the cached files of the campus web proxy server running _squid_[13] have been analyzed and extracted with the help of the _squidpurge_ tool [14]. Of a total of 96134 unique URLs, 10118 were found to refer to HTML documents (_i.e._, excluding other types of data, e.g., images or Javascript include files). File sizes ranged from 220 bytes to 3.3 Mbytes, with an average of 22478 bytes. Exploitable redundancy could be found in 38.7% of the files. The embeddingcapacity was in the range of 0.07-0.1%, depending on the selection of parameters, with more conservative choices producing lower values.
## 2 Materials and Methods
### Validation of the GC/FID Analysis  
The analysis of the FAs using the GC/FID method was validated according to the guidelines of the ICH [36]. The method was validated in terms of linearity and the range of the FA calibration curves. Linearity was validated using the 37-component FAME Mix standard. Every FAME component was identified by the retention times and chromatograms in standard mix. The GC conditions were optimized to obtain a better separation of the FAs, such as the column temperature, flow rate and split ratio. The precision of the method was calculated by the repeatability. The precision was validated by repeating the process five times with a solution of the standard mix. The precision of the chromatographic system was confirmed by checking the %RSD of the retention times and peak areas. Five injections were performed over three days.
## 4 Conclusions  
The present work was set to numerically determine the effect of the deposit layer thickness and thermal conductivity on the minimum thickness of the tube wall that attains safe operation, considering the different working loads of 50%, 75%, and 100% at the maximum operating working pressure of 210 bar. Based on the adopted parameters, the investigation of the minimum thickness of the water tube wall has revealed that:  
1. The thermal conductivity of deposits affects the maximum temperature of the tubes. Consequently, the high potential for overheating, particularly at the deposit layer with thermal conductivity is 0.625 W/m\({}^{2}\) K. The maximum crown temperature exceeds 495 \({}^{\circ}\)C at 100% load in the 6 mm tube thickness with deposit thickness of 0.2 mm, which is near the maximum temperature of 539 \({}^{\circ}\)C of SA-210 C.
2. Increasing the thickness of the deposit layer leads to a linear increase in the tube wall average temperature. Consequently, it is essential to consider the consistency between the chosen wall tube thickness and the maintenance schedule considering the deposit accumulation rates and the operating conditions.
3. The deposit and tube thickness are mandatory inspection requirements. Furthermore, if the deposit thickness and tube thickness exceed the previous results, chemical cleaning and tube replacement are mandatory requirements.  
The present study is limited to the water tube in the furnace section. The identified effect of the deposit layer of magnetite on the tube wall temperature and the corresponding minimum thickness assists our understanding of the role of the deposit layer attributes for suitable selection of the tube thickness. Furthermore, it presents courses of action concerning the operating conditions that minimize the potential overheating of water tubes in the furnace section of the boiler.  
Conceptualization, A.S.A. and K.I.A.; methodology, A.S.A. and K.I.A.; software, A.S.A. and K.I.A.; validation, A.S.A. and M.H.A.; formal analysis, A.S.A. and K.I.A.; investigation, A.S.A. and K.I.A.; resources, K.I.A. and M.H.A.; data curation, A.S.A.; writing--original draft preparation, A.S.A.; writing--review and editing, K.I.A. and M.H.A.; visualization, A.S.A., K.I.A. and M.H.A.; super-vision, K.I.A. and S.A.; project administration, K.I.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Figure 12: The effect of the deposit thickness on the tube stress at 50% of full load.