esa-sceva/satcom-chunk-collection huggingface dataset:
# Investigating BIM Implementation Barriers and Issues in Pakistan Using ISM Approach
## 3 Results and Discussions
### ISM Methodology
#### 3.7.4 Step 4: Development of ISM Diagraph and ISM-Based Hierarchical Model  
Initial diagram was formed based on level partitions by positioning the 12 barriers to show the interrelationships between the BIM barriers. The diagram can be seen in Figure 6. An arrow pointing from barrier i towards barrier j shows that barrier i can lead to barrier j, and in both ways the arrow indicates a mutual relationship. The diagram is then modified by changing the nodes with statements to get 5 levels of the ISM-based hierarchical model, as shown in Figure 7.  
Table 10 and Figure 7 show that a high initial cost of BIM (B9) is the critical barrier in the hierarchy which forces the industry to continue with the old practices (B11-level IV). Communication gap in an Organization (B10) constitutes level-III, which further gives rise to the important level-II barriers, i.e., unavailability of government organization about BIM (B1), unavailability of BIM professionals (B4) and lack of abilities to manage projects through BIM (B7). Level-I consists of barriers (B2, B3, B5, B6, B8, B12)  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & B1 & B2 & B3 & B4 & B5 & B6 & B7 & B8 & B9 & B10 & B11 & B12 & **Driving Power** \\ \hline
**B1** & 1 & 1 & 1 & 1 & 1 * & 1 * & 1 * & 1 * & 0 & 0 & 1 & 9 \\
**B2** & 0 & 1 & 1 * & 0 & 1 & 1 & 0 & 1 & 0 & 0 & 0 & 1 & 6 \\
**B3** & 0 & 1 * & 1 & 0 & 1 & 1 & 0 & 1 * & 0 & 0 & 0 & 1 & 6 \\
**B4** & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 0 & 0 & 0 & 1 * & 9 \\
**B5** & 1 & 1 * & 1 & 0 & 1 & 1 & 0 & 1 & 0 & 0 & 0 & 1 & 7 \\
**B6** & 1 * & 1 & 1 & 0 & 1 * & 1 & 0 & 1 & 0 & 0 & 0 & 1 & 7 \\
**B7** & 1 & 1 * & 1 & 1 * & 1 * & 1 * & 1 & 1 * & 0 & 0 & 0 & 1 & 9 \\
**B8** & 1 & 1 & 1 * & 0 & 1 * & 1 & 0 & 1 & 0 & 0 & 0 & 1 & 7 \\
**B9** & 1 * & 1 * & 1 & 1 & 1 & 1 & 1 * & 1 & 1 & 1 * & 1 & 1 & 12 \\
**B10** & 1 * & 1 * & 1 * & 1 & 1 & 1 & 1 * & 1 * & 0 & 1 & 0 & 1 * & 10 \\
**B11** & 1 * & 1 & 1 & 1 & 1 & 1 & 1 * & 1 & 0 & 1 * & 1 & 1 & 11 \\
**B12** & 1 & 1 & 1 * & 0 & 1 * & 1 & 0 & 1 & 0 & 0 & 0 & 1 & 7 \\
**Dependence power** & **10** & **12** & **12** & **6** & **12** & **12** & **6** & **12** & **1** & **3** & **2** & **12** & & \\ \hline \hline \end{tabular} Note: 1 * shows that transitivity is incorporated.  
\end{table}
Table 10: Final Reachability Matrix.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Barrier** & **Reachability Set** & **Antecedent Set** & **Intersection** & **Level** \\ \hline
**B1** & B1, B2, B3, B4, B5, B6, B7, B8, B12 & B1, B4, B5, B6, B7, B8, B9, B10, B11, B12 & B1, B4, B5, B6, B7, B8, B12 & **2** \\
**B2** & B2, B3, B5, B6, B8, B12 & B1, B2, B3, B4, B5, B6, B7, B8, B9, B10, B11, B12 & B2, B3, B5, B6, B8, B12 & **1** \\
**B3** & B2, B3, B5, B6, B8, B12 & B1, B2
###### Abstract  
In recent decades, adopting alternative resources in infrastructure applications has garnered global attention to address environmental concerns. Olive waste ash (OWA), a locally available byproduct obtained from the olive oil production process, is a promising green material that plays a vital role as a partial cement substitute. This paper evaluates the mechanical and durability properties of cement paste--a key component of paving blocks--incorporating OWA at replacement levels of 0, 5, 10, 15, and 20%, with a constant water-to-cemertitious ratio of 0.45. Density, compressive strength, and flexural strength are assessed at 1, 7, 28, and 90 days, while total water absorption (TWA) and capillary water absorption (CWA) are measured at 28 days. The results reveal that OWA slightly reduces density, compressive strength, and flexural strength, with the optimal results observed at a substitution level of 10%. At 90 days, the compressive strength of the control cement paste is 50 MPa, whereas the 10% OWA mixture exhibits a value of 46 MPa, corresponding to only an 8% reduction. Additionally, two predictive models are proposed: the hyperbolic model for compressive strength variation with curing time and the capillary-diffusive model for capillary water absorption as a function of time. Both models demonstrate a strong fit with experimental data. Correlations between different properties indicate a strong correlation between compressive strength, density, and flexural strength, while a negative linear relationship exists between compressive strength and water absorption. This study underscores OWA's potential to improve sustainable paving blocks by providing suitable mechanical and durability characteristics, offering both environmental and economic benefits.  
c 202515395
_Applied_  
_sciences_  
_Impulse Noise Denoising Using Total Variation with Overlapping Group Sparsity and Lp-Pseudo-Norm Shrinkage [PERSON], [PERSON] *, [PERSON], [PERSON], [PERSON] and [PERSON] School of Physics and Information Engineering, Minnan Normal University, Zhangzhou 363000, China; [EMAIL_ADDRESS] (L.W.); [EMAIL_ADDRESS] (F.L.); [EMAIL_ADDRESS] (Y.C.); [EMAIL_ADDRESS] (F.Y.); [EMAIL_ADDRESS] (Z.C.)
* Correspondence: [EMAIL_ADDRESS]; Tel.: +86-139-0696-8591 check for updates  
Received: 16 October 2018; Accepted: 5 November 2018; Published: 20 November 2018  
**Featured Application: This paper proposes a new model for restoring polluted images under impulse noise, which makes a contribution to the research in image processing and image reconstruction.**  
**Abstract: Models based on total variation (TV) regularization are proven to be effective in removing random noise. However, the serious staircase effect also exists in the denoised images. In this study, two-dimensional total variation with overlapping group sparsity (OGS-TV) is applied to images with impulse noise, to suppress the staircase effect of the TV model and enhance the dissimilarity between smooth and edge regions. In the traditional TV model, the L1-norm is always used to describe the statistics characteristic of impulse noise. In this paper, the Lp-pseudo-norm regularization term is employed here to replace the L1-norm. The new model introduces another degree of freedom, which better describes the sparsity of the image and improves the denoising result. Under the accelerated alternating direction method of multipliers (ADMM) framework, Fourier transform technology is introduced to transform the matrix operation from the spatial domain to the frequency domain, which improves the efficiency of the algorithm. Our model concerns the sparsity of the difference domain in the image: the neighborhood difference of each point is fully utilized to augment the difference between the smooth and edge regions. Experimental results show that the peak signal-to-noise ratio, the structural similarity, the visual effect, and the computational efficiency of this new model are improved compared with state-of-the-art denoising methods.**  
**Keywords: overlapping group sparsity; Lp-pseudo-norm; accelerated alternating multiplier iterative method; impulse noise denoising**
## 1 Introduction  
Modern separation science and technology require the development of new materials with enhanced properties that are able to separate a substance from complex matrices. To this end, a great deal of research has attempted to develop and characterize highly selective new materials. One innovative way of improving the selectivity and retention capacity is to develop molecularly imprinted polymers (MIPs).  
The most common definition of this new class is that they are polymers possessing voids with a shape, size, and group functionality complementary to the molecule used for the imprinting, known as the template. In order to understand the principle underlying MIP preparation, we can imagine a coin being pushed into plasticine. When the coin is taken off the plasticine, a trace remains, having the shape and size of the face of the coin that was pushed into it. If one does the same thing with a template molecule in a polymer matrix, firstly, a composite is obtained at a molecular scale. After the template extraction, a MIP is prepared with voids corresponding to the shape, size, and group functionality of the template. It is very important to keep these voids unchanged after the template extraction. To achieve this, structure stabilization is required, with properties such as a high degree of crosslinking. The MIP preparation methods presented in the state-of-the-art studies can be divided into two categories: chemical methods, based on polymerization (bulk [1], suspension [2], emulsion [3], precipitation [4], etc.) or on sol-gel reactions, [5] and physical methods, referring especially to phase inversion [6], which are implemented far more rarely. MIPs can be produced in various shapes, such as beads [7, 8], pearls [9],regulate particles [10], hydrogels [11], membranes [12], and films [13]. Due to the requirement that MIPs have a high specific surface, a very fast-growing area of development concerns the use of membranes and films. The application of these new materials refers mostly to the purification of a liquid, the separation of a substance from a complex mixture, analysis, and sensing, but other applications have been studied as well, such as catalysis or the slow release of bioactive substances, explaining the need for this Special Issue of _Applied Sciences_. However, the sensing surfaces and the solid phase extraction are at present the main application methods of MIPs membranes and films.
# Nailfold Microhemorrhage Segmentation with Modified U-Shape Convolutional Neural Network
## 3 Materials and Methods
### Method Description
#### 3.2.3 Group Normalization  
Batch normalization (BN) plays an important role in deep learning [19]. By normalizing the features within a minibatch, BN improves the performance of CNNs on numerous computer vision tasks. However, due to memory limitations, most research tends to use a batch size of 1 or 2. Such a small batch size leads to inaccurate estimation of the batch statistics, which will lower the performance of deep learning networks. For the effectiveness of segmentation, group normalization (GN) [38] is employed as the normalization layer.  
GN organizes channels into different groups and calculates the mean and standard deviation across a group of channels. The formulations of group normalization follow:  
\[S_{i}=\left\{k|k_{N}=i_{N},\ \frac{k_{\text{C}}}{C/G}=\frac{i_{\text{C}}}{C/G}\right\} \tag{6}\]  
1. Calculate the mean value \[\mu_{i}=\frac{1}{m}\sum_{k\subseteq S_{i}}x_{k}\] (7)
2. Calculate the standard deviation \[\sigma_{i}=\sqrt{\frac{1}{m}\sum_{k\subseteq S_{i}}(x_{k}-\mu_{i})+\epsilon}\] (8)
3. Normalization \[\hat{x}_{i}=\frac{1}{\sigma_{i}}(x_{k}-\mu_{i})\] (9)
4. Scale and shift \[y_{t}=\gamma\hat{x}_{i}+\beta\] (10)  
In this case, GN takes as input a 4D vector x and outputs a channelwise normalized vector y. The index i can be described as:  
\[i=(i_{N},i_{H},i_{W},i_{C}) \tag{11}\]  
where \(N\) presents the batch axis, (\(H\), \(W\)) are the height and width of the input feature, \(C\) is the channel axis. \(S_{i}\) represents the set of coefficients in the same group, m is the size of \(S_{i}\) where \(\lfloor\cdot\rfloor\) represents the floor operation. G is a predefined hyperparameter that determines the group number (C/G). Similarly, GN utilizes the trainable parameters \(\gamma\) and \(\beta\) to obtain better approximation ability.  
The underlying hypothesis behind GN is that visual representation channels are not entirely independent, namely, each group of channels may follow the same distribution with shared mean and standard variance. Thus, computing different statistics of grouped channels can offer more flexibility and better expressiveness for the deep model.  
In general, GN can be thought of as a powerful normalization method without utilizing the batch dimension. It helps the proposed network to converge and gain better stability.
# Software-Defined Networking: Categories, Analysis, and Future Directions
## 3 Flow Rule Installation Mechanisms
### Summary and Lessons Learned  
The summary of flow installation mechanisms is presented in Table 2, and we draw the following conclusions based on these studies. ORPP [88; 89] resolves the problem of the placement of flow rules by using two frameworks: OFFICER and aOFFICER. Both frameworks provide efficient mechanisms to install flow rules. vCRIB [90] provides abstraction to define and manage flow rules for data center operators by portioning and placing flow rules on switches and hypervisors. DevoFlow [17] helps to amend the OpenFlow protocol by allowing network operators to focus on only selected flows for network management to reduce overhead between the control and data planes. Infinite CacheFlow [91] solves the issue of the limitation of flow rules at switches due to the limited TCAM resources by proposing a hybrid switch design (hardware and software). SwitchReduce [92] minimizes the switch state and controller involvement in SDN. The mechanism in [93] reduces the communication overhead between controller and switches by storing flow rules at the data plane. In [94], a flow-rule-based multiplexing approach is proposed, which optimizes both the flow rule allocation as well as the traffic engineering. DomainFlow [66] presents a flow-level control- and granularity-based mechanism in ethernet switches by using the OpenFlow protocol. SourceFlow [96] presents a mechanism that can handle many flows without affecting the flow granularity, in addition to minimizing the problem of costly and power-consuming search engine devices from the core nodes.
# Reconditioning of Diamond Coated Tools and Its Impact on Cutting Performance for CFRP Laminates
## 2 Materials and Methods
### Cutting Tests  
In this study, the cutting performances of new and reconditioned drills were evaluated by drilling a series of consecutive holes in a workpiece. CFRPs laminated with a thickness of 14 mm and dimensions of 250 mm \(\times\) 100 mm were used as the workpiece. Each workpiece consisted of 72 unidirectional layers of prepreg (T8005 series, TORAY, Osaka, Japan) with a quasi-isotropic laminate lay-up. The carbon fiber volume ratio was about 60%.  
A vertical milling center OKUMA MD-45 VA was utilized to perform the drilling tests. The experimental setup is shown in Figure 4. The cutting parameters were as follows: the drill rotation n was 2700 rpm, the cutting speed V was 53.9 m/min, and the feed rate \(f\) was 0.1 mm/rev, which were kept constant. No coolant was applied during the tests. The number of drilled holes was limited to below 200 in order to avoid the drill substrate wear and the necessity of tool regrinding. A drill flank wear was measured after each 20 holes using a digital microscope (VHX-1000 SP, Keyence, Osaka, Japan). After drilling 200 holes, the drills were checked for the damage to cutting edges, such as cutting edge chipping or a fracture via SEM.
## 5 Results and Discussion
### Throughput  
For the amount of data packets transmitted from one source to another, assessing speed throughput is one of the most important parameters utilized, and it is considered one of the most significant factors in determining network performance. Here, we can see that the amount of data packets transmitted in our technique (OEAODV) is very fast. Overall performance of OEAODV at different speeds is outperforming as compared with previous techniques, which is compared at random speeds from 50 to 80 m/s, and the results of different speeds show that OEAODV is outperforming. Figure 9 shows that the higher the throughput, the lower the rate of packets lost.
## 5 Results and Analysis
### Tight Coupling Solutions  
#### 5.2.1 Segment 1  
In Figure 13 the trajectories obtained with the different TC approaches are shown. Compared with the LC results in Figure 10, all of the solutions show a significant improvement. This is consistent with previous studies and is attributed to the use of GNSS data when a few as one satellite is visible. In particular, the baseline configuration (TC GPS/INS) does not contain a large drift during the long GPS outage on the north/south portion of the trajectory. In this case, although including GLONASS observations still yields a better overall solution, the relative improvement is less than in the loose integration case. This is again attributed to the increased observability resulting from the tight integration. Finally, the trajectory obtained by also integrating the yaw aiding and the velocity and height pseudo-observations shows remarkable improvements relative to the other TC configuration especially during the above-defined critical zone.  
The performance of different TC configurations, in terms of RMS and maximum position errors, is summarized in Table 6.
# Separable Gaussian Neural Networks: Structure, Analysis, and Function Approximations
## 5 Numerical Experiments
### Dimension Scalability  
In order to understand the dimensional scalability of SGNN, we applied SGNN to candidate functions with the number of dimensions from two to five, as presented in Table 3. For comparison, the data points were kept as \(16\),\(384\) such that sufficient data were sampled for \(5\)D functions, i.e., \(d=5\). Each layer has fixed \(20\) uni-variate Gaussian neurons, with initial centers evenly distributed in each dimension and widths being the distance between two adjacent centers. The training time per epoch grows linearly as the dimension increases, with an increment of \(0.02\) s/epoch per layer. For the majority of candidate functions, SGNN can achieve the accuracy level of \(10^{-4}\). It is sufficient to approximate the \(5\)D functions by SGNN with five layers and a total of \(100\) neurons.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Functions** & **Features** & **Explicit Expression** \\ \hline Root sum squared & Sink & \(f_{1}(\mathbf{x})=\left(\sum_{i=1}^{d}x_{i}^{2}\right)^{\frac{1}{2}}\) \\ Second-degree polynomial & Saddle & \(f_{2}(\mathbf{x})=\frac{1}{50}\sum_{j=1}^{d}x_{i}^{2}x_{j+1}\) \\ Exponential-square sum & Flatter sink & \(f_{3}(\mathbf{x})=\frac{1}{5}\sum_{j=1}^{d}e^{x_{j}^{2}/50}\) \\ Exponential-sinusoid sum & Sink and source & \(f_{4}(\mathbf{x})=\frac{1}{5}\sum_{j=1}^{d}e^{x_{j}^{2}/50}\sin(y_{j})\) \\ Polynomial-sinusoid sum & Sink and source & \(f_{5}(\mathbf{x})=\frac{1}{50}\sum_{j=1}^{d}x_{i}^{2}\cos(i*x_{j})\) \\ Inverse-exponential-square sum & Source & \(f_{6}(\mathbf{x})=10\sum_{j=1}^{d}e^{x_{j}^{2}/25}\) \\ Sigmoidal & S-shaped surface & \(f_{7}(\mathbf{x})=10/(1+e^{-\frac{1}{5}\sum_{j=1}^{d}x_{i}})\) \\ Gaussian & Flatter source & \(f_{8}(\mathbf{x})=10e^{-\frac{1}{50}\sum_{j=1}^{d}x_{j}^{2}}\) \\ Linear & Flat & \(f_{9}(\mathbf{x})=\sum_{j=1}^{d}x_{j}\) \\ Constant & Flat & \(f_{10}(\mathbf{x})=1\) \\ \hline \hline \end{tabular} Note: In \(f_{4},y_{j}=x_{j+1}\) with \(j=1,2,\ldots,d-1\) and \(y_{d}=x_{1}\).  
\end{table}
Table 2: Candidate functions and their features.  
The configuration of SGNN cannot effectively approximate the function \(f_{5}\) in 4D. This can be easily resolved by adding more neurons into the neural network (see a similar example in Table 7). In summary, the computational time of SGNN scales linearly with the increase in dimensions.  
Then, 2D and 5D examples are selected to illustrate the expressiveness of SGNN in function approximations. The number of neurons, training size, and mini-batch size are fine-tuned to achieve optimal results.
# A Lesson for the Future: Will You Let Me Violate Your Privacy to Save Your Life?
## 7 Discussion and Suggestions
### Suggestions  
Based on our study's results and findings, we derived the following suggestions that help the governments, especially the government of Canada and health authorities, to better handle, manage, and administer a future pandemic.  
First, our study showed the impact and importance of digital contact tracing in decreasing the spread of infection. Hence, even though contact tracing applications were unsuccessful in their first appearance during the COVID-19, we believe they should be employed again in a future pandemic. However, continuous research and work should be performed to improve the security level and to address privacy concerns.  
Second, to achieve the at least 60% adoption rate, the public should be informed about the advantages and benefits of employing digital contact tracing apps. Our study demonstrates how this can positively change public perception towards utilizing these new technologies, such as CTAs, to stop a future pandemic. However, this process requires precise planning and should be an ongoing process over time. Therefore, people become ready to do their part when the next pandemic hits.  
Third, in a future pandemic, CTAs should be administrated and maintained by the government rather than third-party companies. On the other hand, the government must re-design and improves the CTAs' privacy statements to address public concerns. For example, what data will be collected? Where will the data be collected (e.g., in a public area)? Where and how long will the data be stored? What security measures are employed to protect the data? These are just a few of many questions that should be explicitly discussed and answered to positively change public perception.
# Precoded Generalized Spatial Modulation for Downlink MIMO Transmissions in Beyond 5G Networks
## 5 Conclusions  
In this paper, a novel MIMO system where GSM symbols are transmitted simultaneously to multiple users has been described. By combining large antenna settings at the BS with high-order \(M\)-QAM constellations, the proposed approach is capable of improving the spectral efficiency and energy efficiency. A precoder is applied at the BS to completely remove inter-user interference, while a reduced complexity iterative SU GSM detector is implemented at each receiver. Simulation results show that the proposed approach can achieve a very competitive and very promising performance compared to conventional MU-MIMO systems with identical SE. In fact, system level results based on a C-RAN scenario with multiple RRU showed potential gains of up to 155% in throughput and 139% in coverage when compared to traditional cellular networks. The introduction of imperfect channel estimation reduces the throughput gain to 125%. Future work will include a thorough evaluation of the impact of several hardware impairments (such as phase-noise, non-linear distortion, and I/Q imbalances) and robust mitigation algorithms.  
Conceptualization, J.P.P., A.C., B.B. and N.S.; methodology, J.P.P., A.C., B.B. and N.S.; software, J.P.P., A.C., V.V., B.B. and N.S.; validation, J.P.P., A.C., B.B., V.V. and N.S.; formal analysis, J.P.P., A.C., B.B., V.V. and N.S.; investigation, J.P.P., A.C., B.B., V.V. and N.S.; resources, A.C. and N.S.; data curation, J.P.P., B.B., V.V., N.S. and A.C.; writing--original draft preparation, J.P.P., A.C., B.B. and N.S.; writing--review and editing, J.P.P., A.C., B.B., V.V. and N.S.; visualization, J.P.P., A.C., B.B., V.V. and N.S.; supervision, A.C. and N.S.; project administration, A.C. and N.S.; funding acquisition, A.C. and N.S. All authors have read and agreed to the published version of the manuscript.  
This research was partially funded by the ISCTE-University Institute of Lisbon under the grant ISTA-BMPCDCTI-2019 and by the FCT/MCTES through national funds and when applicable co-funded by EU funds under the project UIDB/50008/2020.  
The authors acknowledge the support of ISCTE--University Institute of Lisbon, Fundacao para a Ciencia e Tecnologia (FCT) and Instituto de Telecomunicacoes (IT), as described above in funding.  
The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.