esa-sceva/satcom-chunk-collection huggingface dataset:
# Model-Based Dynamic Forecasting for Residential Construction Market Demand: A Systemic Approach
###### Abstract  
According to the previous research, proper demand forecasting could help construction-related firms in effective planning for future market changes. However, existing market demand forecasting models are somewhat limited, and most of them bear some critical shortcomings. This research aims to develop a forecasting model for the Korean residential construction industry using system dynamics. In developing the market forecasting model, this research uses variables that significantly impact future construction market change. Many of the existing models do not include as many variables as this model, and none of them have considered complex interlocking effects among these variables. This model is also the first model using a system-based approach by looking at the target industry as a 'one complex system' rather than focusing on individual variables' impact on future market changes. By employing system dynamics, it is possible to consider qualitative and quantitative aspects and produce long-term market forecasting results. The developed market forecasting model consists of two main modules, the first being a prediction module for the grassroots construction market and the second for operation and maintenance (O&M) and the demolition market. Sixteen input variables are grouped into four categories: social, economy, regulation, and past market size among over 25 identified variables. The model utilizes a mathematical function system using the designed feedback loops in producing future market forecasts. Based on the validation tests with past market data, it turns out that the model is reliable, with the determination coefficient (\(R^{2}\)) being over 0.7 on all tested occasions. According to the model's forecasting results, the Korean construction market's size is expected to be 231 billion won in 2015 and 286 billion won in 2030. However, the O&M market's growth rate is expected to be higher than 180%, which is much bigger than those of the grass-root and demolition markets. Thus, this research model is realistic according to the construction paradigm change. This research is considered one of the pioneering studies in construction market forecasting by employing dynamic inter-relationships among various input variables. Therefore, the market forecasting results can be interpreted as more practical and can provide more insights to the construction industry stakeholders. The model is envisioned to provide the public sector with useful guidelines in preparing future public market supply strategies such as construction budget allocations. It would also be helpful for the private sector to develop more proactive and accurate demand strategies for timely decision-making.  
construction market; market scale forecasting; systematic approach +
Footnote †: journal: Applied Statistics
# Research on Kalman Filter Fusion Navigation Algorithm Assisted by CNN-LSTM Neural Network
## 5 Conclusions  
In conclusion, this study focuses on the key technologies of the Kalman filter fusion navigation algorithm assisted by neural networks. By combining the powerful learning capabilities of neural networks with the precise estimation capabilities of the Kalman filter, a novel solution is provided for UAV navigation technology. This algorithm not only significantly improves navigation accuracy and stability but also demonstrates outstanding adaptability in dealing with complex environments and variable noise interference.  
Figure 15: Two-dimensional and \(Z\)-direction error analysis plots before and after signal correction.  
During UAV missions, the neural-network-based Kalman filter fusion navigation algorithm can dynamically adjust and optimize the navigation model based on sensor data in real time, effectively overcoming many limitations of traditional navigation methods. Whether facing complex natural environments or deliberate interference, this algorithm can provide precise and reliable navigation services for UAVs due to its excellent performance.  
In the construction of neural network architecture, this study combines existing research results and proposes a neural network architecture that combines a CNN with LSTM to assist the Kalman filter optimization algorithm. By leveraging the advantages of both, the proposed architecture significantly outperforms traditional neural network architectures, reducing training result errors by nearly 50% through simulation experiments. Moreover, it verifies the effective improvement of navigation performance under strong interference conditions by the optimization algorithm.  
Currently, interference in UAV navigation systems includes not only jamming but also deception techniques, which are widely used. Therefore, in future research, further exploration of interference techniques will be conducted to consider deception techniques in the task environment. The neural network architecture will be further optimized to adapt to even more complex task scenarios.  
In summary, the Kalman filter fusion navigation algorithm assisted by neural networks, with its unique advantages and strong potential, has become an important development direction in modern UAV navigation technology. The optimization algorithm will play an increasingly important role in areas such as autonomous driving, UAV cruising, and intelligent robotics.  
Conceptualization, K.C. and L.Y.; methodology, K.C., P.Z. and L.Y.; software and validation, K.C. and J.S.; formal analysis, K.C.; investigation, K.C. and P.Z.; resources, J.S.; data curation, K.C.; writing--original draft preparation, K.C. and J.S.; writing--review and editing, P.Z. and L.Y.; visualization, L.Y.; supervision, P.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data are not publicly available due to the confidential nature of our school.  
The authors declare no conflicts of interest.
## 3 Background on Mutual Information  
When discussing FS from the perspective of information theory, it is worthwhile to mention a very important term, mutual information (MI). MI is a metric that measures the amount of associated shared information between two variables (two features in our context) [109]. In another context, it quantifies the amount of information shared between two random variables. This quantity is always a non-negative value, and if it is zero, then \(X\) and \(Y\) are independent random variables. By way of illustration, MI measures the associated relationships among two variables. For example, for two given discrete variables, \(X\) and \(Y\), the MI of \(X\) and \(Y\) is given through the following:  
\[MI(X,Y)=\sum_{x}\sum_{y}P_{(X,Y)}(x,y)\log\left(\frac{P_{(X,Y)}(x,y)}{P_{X}(x)P _{Y}(y)}\right) \tag{1}\]  
where the joint probability distribution of \(X\) and \(Y\) is given through the following:  
\[P_{(X,Y)}(x,y)=P(X=x,Y=y) \tag{2}\]  
In the case where \(X\) and \(Y\) are variables of continuous values, MI would be given through the following:  
\[MI(X,Y)=\int_{x}\int_{y}P_{(X,Y)}(x,y)\log\left(\frac{P_{(X,Y)}(x,y)}{P_{X}(x) P_{Y}(y)}\right)dxdy \tag{3}\]  
In FS for a classification problem, we care about the association between the features in a subset and between each feature and the class label. For a given feature subset,\(S=[v_{1},v_{2},\ldots,v_{d}]\), and label, \(l\), \(MI(v_{1},v_{2})\), measures the dependency between feature \(v_{1}\) and feature \(v_{2}\), while \(MI(v_{1},l)\) measures the dependency between feature \(v_{1}\) and label \(l\). For more details about how we take advantage of measuring the dependencies among variables in the subset and among each variable and the label, we refer to our detailed explanations in Section 4.5.
# Pre and Postprocessing for JPEG to Handle Large Monochrome Images
## 3 Experimental Results
### Test 3: Fixing the Size of the Images  
In this test, the standard JPEG method and the proposed method are applied to the images shown in Figure 3 and the simulations results are given in Table 2 and are shown in Figure 4. Here, the sizes for the six images are adjusted to approximately the same value for the two algorithms; PSNR and SSIM values are measured and compared.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Image (a)** & **Image (b)** & **Image (c)** & **Image (d)** & **Image (e)** & **Image (f)** \\ \hline Original size & 257 K \({}^{1}\) & 257 K & 1 M \({}^{2}\) & 1.97 M & 1.97 M & 1.97 M \\ PSNR of JPEG method & 28.86 & 26.95 & 26.52 & 29.04 & 26.95 & 28.69 \\ PSNR of proposed method & 28.84 & 27.39 & 26.92 & 29.13 & 27.00 & 28.70 \\ SSIM of JPEG method & 0.7957 & 0.7455 & 0.6783 & 0.8135 & 0.6932 & 0.8232 \\ SSIM of proposed method & 0.8246 & 0.8200 & 0.7241 & 0.8371 & 0.6771 & 0.8411 \\ Size using JPEG method & 4.87 k & 4.99 k & 18.6 k & 31.5 k & 30.6 k & 30.5 k \\ Size using proposed method & 2.41 k & 2.91 k & 10.2 k & 10.9 k & 5.49 k & 5.88 k \\ CR using JPEG method & 53 & 52 & 54 & 63 & 66 & 66 \\ CR using proposed method & 107 & 88 & 98 & 181 & 367 & 343 \\ \hline \hline \end{tabular} \({}^{1}\) Kilo Byte, \({}^{2}\) Mega Byte.  
\end{table}
Table 1: Results of Test 2.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Image (a)** & **Image (b)** & **Image (c)** & **Image (d)** & **Image (e)** & **Image (f)** \\ \hline Original size & 257 K \({}^{1}\) & 257 K & 1 M \({}^{2}\) & 1.97 M & 1.97 M & 1.97 M \\ PSNR of JPEG method & 28.84 & 27.39 & 26.92 & 29.13 & 27.00 & 28.70 \\ SSIM of JPEG method & 0.7957 & 0.7455 & 0.6783 & 0.8135 & 0.6932 & 0.8232 \\ SSIM of proposed method & 0.8246 & 0.8200 & 0.7241 & 0.8371 & 0.6771 & 0.8411 \\ Size using JPEG method & 4.87 k & 4.99 k & 18.6 k & 31.5 k & 30.6 k & 30.5 k \\ Size using proposed method & 2.41 k & 2.91 k & 10.2 k & 10.9 k & 5.49 k & 5.88 k \\ CR using JPEG method & 53 & 52 & 54 & 63 & 66 & 66 \\ CR using proposed method & 107 & 88 & 98 & 181 & 367 & 343 \\ \hline \hline \end{tabular} \({}^{1}\) Kilo Byte, \({}^{2}\) Mega Byte.  
\end{table}
Table 2: Results of Test 3.  
Figure 4: Simulation Results of test 3, left-hand column (**a,c,e,g,i,k**): results of standard JPEG method, right-hand column (**b,d,f,h,j,l**): results of our proposed method.  
We observe an improvement in PSNR in terms of the proposed algorithm by 3 dB to 4 dB and 0.1 to 0.17 in SSIM value for the same size. Furthermore, the quality of the images is enhanced using the proposed method over JPEG method as listed in Table 2.
# Pido: Predictive Delay Optimization for Intertidal Wireless Sensor Networks
## 3 System Deployment and Motivation
### Delay Problem in IT-WSN
We evaluate the average delay and PDR of all 26 nodes (“Average (26)”) in the Zhoushan system, and we select eight nodes (Node 1–Node 8) as representatives. (**a**) The average delay of 8 nodes and the average delay of all nodes in Zhoushan experiment during high tide period and low tide period; (**b**) The average PDR of 8 nodes and the average PDR of all nodes in Zhoushan experiment during high tide period and low tide period; (**c**) The average buffer occupancy of 8 nodes and the average buffer occupancy of all nodes in Zhoushan experiment. ETX in the indoor WSN and the IT-WSN, which represents the distribution of the ETX among all links. Results shown in Figure 3 indicate that the ETXs of over 80% of links are fewer than five for the indoor WSN, and the value is 75 for the IT-WSN case. Considering that each system has a retransmission limit, over 80% of packets in the indoor WSN can be sent in a single cycle while an IT-WSN needs 15 cycles given a retransmission limit of five cycles. Hence, poor link quality leads to extra transmission cycles, which in turn causes extra delays (14 cycles in this case).
# Concealed Object Detection and Recognition System Based on Millimeter Wave FMCW Radar
## 2 Materials and Methods
### Image Preprocessing  
The data set consists of 250 SAR images, which contains 10 categories such as wrench, wire stripper, hammer, rasp, ax, scissors, key, disc, pliers and gun, and each category contains 25 SAR images. The photo of the test object and the corresponding SAR radar image are shown in Figure 8. The experimental setting is to place the item in the carton, and the effect is the same as that when the clothing covers the object.  
In the reconstruction algorithm, the distance parameter \(Z_{0}\) is given in advance, so that the target can be imaged near this range. The radar original reconstruction image may contain noise, which is caused by the distance error between the target and the radar. In the actual security check process, the relative distance between the object and the radar cannot be guaranteed to be very accurate, so the image preprocessing is very important, which can eliminate noise and enhance the image features and also, improve visual recognition. The radar original reconstructionimage is first processed using the gamma transform algorithm and then linear stretching is carried out, as shown in Figure 9.  
\[s=cr^{\gamma} \tag{7}\]  
where \(c=1\) and \(\gamma=2.4\), gamma transform algorithm deals with the normalized brightness and then reverse-transforms to the real pixel gray value. The linear stretch piecewise function is:  
\[f(x)=\left\{\begin{array}{ccc}0&,&x<30\\ 2x&,&30\leq x<60\\ 120+\frac{(235-120)}{(215-60)}(x-60),&60\leq x<215\\ 235&,&x\geq 215\end{array}\right. \tag{8}\]  
Figure 8: Part of the original data sets and the corresponding SAR radar images.  
The gray value of each pixel in image represents the energy of a certain point of the target at a certain distance, so the radar original reconstruction image indicates that the SAR image has the characteristic that the reflected energy of the target is higher than the noise energy. Since the energy value containing the object information is concentrated in the bright region, the gamma transformation algorithm is used, the parameter is adjusted to 2.4 to increase the contrast in the bright areas and decrease the contrast in the dark areas [19]. Then the pixel gray value is handled by a linear stretching algorithm, which contains four piecewise functions: (1) eliminate the noise; (2) preserve the information of the low gray pixel area; (3) map the original pixel value to a higher and wider brightness region, which increases the contrast and brightness of the image; (4) make the image not appear in extremely bright pixels, which ensures the integrity of the image.  
By using the gamma transformation algorithm, the effective information of radar original reconstruction image is retained, while the noise is reduced. After the linear stretch, the image is enhanced, and improves the visual recognition. Image preprocessing lays a foundation for the subsequent supervised learning. The results are shown in Figure 10.
# An SSI-Based Solution to Support Lawful Interception
## 2 Background
### Self-Sovereign Identity
#### 2.2.5 Decentralized Identifiers  
Decentralized Identifiers (DIDs) are a type of URI that uniquely identifies an entity in the SSI system. Each DID resolves to a DID Document, which includes details about the identified entity (e.g., the public key for the digital signature verification).  
There are two types of DIDs:  
* Ledger-based DIDs: this type of DID involves a blockchain or a generic distributed ledger. It is typically the blockchain address. It is created, updated, and deactivated by performing a transaction.
* Peer-DID: this type of DID does not involve a blockchain. Each endpoint of a peer-to-peer connection generates a Peer DID that is securely shared with the other peer entity using a specific communication protocol.
## 5 Results and Discussion
The proposed ECC consumes less power than SHA-256, which consumes a large amount of power as the main size increases, as seen in the graph. As the main size exceeds 512 bits, the power consumption using SHA-256 becomes linear as shown in Figure 9.  
Figure 8: Transaction cost for gateway-type BIoT architecture. Figure 9: Power consumption of ECC-CRT and SHA-256. The optimization seen in this work aims to increase the overall network performance and extend the network's lifetime. IoT nodes typically use batteries; the energy supply of these nodes is thus an important element in their continuous life and operation in the network environment. In this regard, the energy consumption of the network is measured in a way, namely real energy consumption, and the estimation method for the energy consumption rate is modeled on a milli-joul scale. \[\text{Energy}(mj)=\frac{T_{x}\times 19.5\;\text{mA}+R_{x}\times 21.8\;\text{mA}+ \text{CPU}\times 1.8\;\text{mA}+\text{LMP}\times 0.0545)\times 3\text{V}}{4096 \times 8} \tag{7}\]  
where LPM is low power management, CPU is central processing unit, \(R_{x}\) radio listens, and \(T_{x}\) radio transmit. Now compare the performance of two security algorithms, ECC and SHA-256, in the proposed device-to-device BIoT architecture. Figure 10 depicts the Contiki OS simulation results for the average battery level for all nodes from the start of the simulation to the end. As can be shown, the proposed ECC saves 49% more energy than SHA-256. Furthermore, when using SHA-256, the battery absorbed more energy than when using the proposed stable ECC. Figure 11 depicts the SHA 256 and proposed ECC-CRT storage spaces. The files are represented on the \(x\)-axis, and the storage space in megabytes is represented on the \(y\)-axis (MB). When the file size is 30 kb, SHA 256 and the suggested ECC-CRT require 300 and 350 MB of storage capacity, respectively. In comparison to SHA 256, the suggested ECC-CRT method requires less storage space. Table 3 provides a performance comparison between ECC-CRT and SHA-256. Figure 10: Network lifetime in various battery or energy levels for ECC-CRT and SHA-256. As a result, the ECC-CRT approach is ideal since it is extremely safe and has no flaws that allow its keys to be broken. For the suggested scenario, network efficiency and high securities are key exchange requirements and ECC-CRT is the best choice due to its speedy key generation and reduced key length as compared with SHA-256. The combination of blockchain and IoT will recommend a powerful approach that can greatly direct and spread innovative market approaches.
# A Systematic Parameter Analysis of Cloud Simulation Tools in Cloud Computing Environments
## 2 Highly Prominent and Extensible Categories of Cloud Simulators
### Prominent Cloud Simulators
#### 2.1.1 CloudSim  
According to [PERSON] et al. [11], CloudSim is a toolkit for modeling CEs proposed by the CLOUD Laboratory, University of Melbourne, Australia. CloudSim models DCs, VMs, SBs, and resource methods to model provisioning. It provides a flexible transition between space-shared and time-shared resource allocations for processing components. Researchers can deploy cloud-based apps in a variety of environments with minimal effort and time spent evaluating the results. According to [PERSON] and [PERSON] [12], CloudSim provides simulation, smooth modeling, and functional design within the context of the CC.  
Figure 1: Categories of cloud simulators [10].  
It is a forum for the design of SBs, DCs, and large-scale allocation policies and scheduling. It has some extended features, such as a virtualization engine, to design the development and monitoring of the lifecycle of this system in the DC. The GridSim architecture developed in the lab for power systems serves as the foundation for CloudSim's infrastructure. [PERSON] et al. [13] proposed IaaS (Service Infrastructure) layer modeling for cloud facilitation DCs that can be defined in CloudSim. With CloudSim, one can build various models to explain energy consumption in CEs. Users can simulate VM migration, host machine termination, and energy models. According to [PERSON] [14], CloudSim's simulation framework is a Java-based simulation toolkit that can design extremely large-scale clouds and support communication among cloud system components, as suggested. It is an event-focused simulator that has a unique feature known as a federated strategy. A federated cloud is a collection of internal and external cloud services that are distributed to meet business needs. [PERSON] [15] suggested that CloudSim is a virtualization and multi-tenant infrastructure simulation environment. CloudSim's architecture was inspired by the limitations of true testbeds (such as Amazon EC2) in terms of scaling and replication of test results. As a result, CloudSim provides a generalized and extensible simulation framework that includes modeling, simulation, and testing aspects, which is especially useful for large-scale DCs in the cloud. It enables a cost-free, reproducible, and manageable environment and adjusts for success bottlenecks before deployment on real clouds.
## 3 Full-Scale Measurement in Real Urban Streets for Model Validation
### Measurement System and Configuration  
To conduct a full investigation of the sound field in a street, the sound attenuation and the sound decay were measured in this study. Different measurement systems were used to avoid the influence of background noise. Given the high signal-to-noise ratio requirement for the measurement of the reverberation time, a pistol source was employed in the RT measurements. A sound meter (Nor140, Norsonic, Lierskogen, Norway) was used to record the impulse response at each receiver position. While in the SPL measurements, an omnidirectional sound source with the pink signal was used to prevent the variations due to the pistol source. Meanwhile, in each receiver position, the SPL and impulse response were measured thrice to reduce the error caused by the background noise.  
The main objective of the measurement in this study was to validate the calculation model in the streets with different diffuse conditions. Previous measurements revealed that sound attenuation along the length was more significant than that at the cross sections [30]. Therefore, the measurement  
Figure 4: Images of measured street canyons (**a**–**e**) and measurement configuration (**f**).  
configurations in the five streets were the same in this study, as shown in Figure 4f. The source was placed at the center of the street and 10 receivers were placed along the central axis at distances of 5 to 50 m from the source at intervals of 5 m. Because the most common source and receivers in the measured streets are the pedestrians, the height of the source and receivers were set at 1.25 m, as commonly used in previous measurements [38] and parameter studies [19]. During the measurements, the pistol, omnidirectional source, and sound meter were fixed to supports.
# Performance Analysis of a Vertical FSO Link with Energy Harvesting Strategy
## 2 System Model
### Atmospheric Attenuation  
For optical waves, the effect of the atmospheric attenuation suffered by the light propagating through the atmosphere is mainly caused by either absorption as scattering by air molecules in addition to both absorption and scattering by solid or liquid particles suspended in the air which, as a last resort, indicates the effect of weather conditionson the transmitted laser beam. Absorption and scattering are often grouped together under the topic of extinction, defined as the reduction or attenuation in the amount of radiation passing through the atmosphere. Mathematically speaking, such attenuation is incorporated using the well-known Beer-Lambert law [42; 43; 44] given by:  
\[h_{l}=\exp{(-a(\lambda)L)}, \tag{9}\]  
where \(a(\lambda)\) is the wavelength dependent attenuation coefficient (extinction coefficient) and \(L=Z-z_{0}\) is the propagation path length from the transmitter. As commented, the coefficient \(a\) depends on weather conditions and can be obtained from the atmospheric visibility. Since the attenuation coefficient hardly changes over long periods of time, we have assumed \(h_{l}\) as a deterministic coefficient in our analysis.