esa-sceva/satcom-chunk-collection huggingface dataset:
# Open-Vocabulary Part-Level Detection and Segmentation for Human-Robot Interaction
## 3 Methods
### Loss Function  
The loss function of part-level segmentation and detection includes three sections. The image decoder contains the classification loss and the bounding box regression loss. The mask decoder comprises the mask loss. The bounding box regression loss is employed to ensure that the model can accurately predict the bounding box position of the part [38]. The formula can be expressed as follows:  
\[L_{reg}=\begin{cases}\frac{1}{N_{reg}}\sum p_{i}^{*}0.5(t_{i}-t_{i}^{*})^{2} \times 1/\sigma^{2}&\text{if }|t_{i}-t_{i}^{*}|<1/\sigma^{2}\\ \frac{1}{N_{reg}}\sum p_{i}^{*}(|t_{i}-t_{i}^{*}|-0.5)&\text{otherwise}\end{cases} \tag{4}\]  
where \(N_{reg}\) represents the number of RoIs, \(t_{i}\) represents the bounding box offset predicted by the model, and \(t_{i}^{*}\) represents the ground-truth bounding box offset. The classification loss uses a cross-entropy loss function to calculate the difference between the category prediction and the ground-truth label for each RoI. The formula can be expressed as follows:  
\[L_{cls}=\frac{1}{N_{cls}}\sum_{i}L_{cls}(p_{i},p_{i}^{*}) \tag{5}\]  
\[L_{cls}(p_{i},p_{i}^{*})=-\log[p_{i}^{*}p_{i}+(1-p_{i}^{*})(1-p_{i})] \tag{6}\]  
where \(N_{cls}\) represents the number of RoIs, \(p_{i}^{*}\) represents the predicted category probability of the \(i\)-th RoI, and \(i\) represents the sum of all RoIs.  
The mask loss is used to ensure that the model can accurately segment the pixels of the part. The formula can be expressed as follows:  
\[L_{mask}=\frac{1}{m^{2}}\sum_{1}^{m^{2}}[-y*log(sigmoid(x))-(1-y)*log(1-sigmoid(x))] \tag{7}\]  
where \(y\) represents the label value of the mask of the current location, which is 0 or 1; and \(x\) represents the output value of the current location.
# An Efficient Blockchain Transaction Retrieval System
## 3 System Implementation
### Retrieval Feature Extraction Processing  
First of all, the blockchain retrieval system needs to extract the retrieval features from the block. Blockchain links block together through hash pointers. The data in the blocks are semi-structured, and each block consists of a block header and block body. The block header comprises consensus-related information, including version number, parent block hash, timestamp, Merkle root, block size, and other information.  
The first step is to extract the characteristics, including the transaction timestamp, sender account address, and receiver account address, and generate the block summary file shown in Table 1. The block hash is the hash name for the entire block, followed by the transaction hash for each transaction, the account address of the sender participating in the transaction, the account address of the recipient participating in the transaction, and the time when the transaction was created.  
The second step is to process these retrieval characteristics. The sender and receiver account addresses are processed separately for the accuracy of retrieval. The key-value pairs of the sender's transaction address and block hash and the key-value pairs of the receiver's transaction address and block hash are generated, respectively. These are sent to the retrieval structure maintenance module to update the account address retrieval structure. Then, the transaction timestamp in the block and block hash are sent to the time retrieval structure for updating.
# Non-Destructive Porosity Measurements of 3D Printed Polymer by Terahertz Time-Domain Spectroscopy
## 3 Results
### Model  
In order to explain the findings presented here, we employ a simple geometrical model of 3D printed materials. The model, depicted in Figure 9, assumes that the deposited tracks are cylindrical, rigid, parallel, and regularly spaced. Of course, the real structure deviates from these assumptions to various degrees, especially in the fact that track cross-sections are non-circular. Nevertheless, the model provides a good first-order approximation and allows for the interpretation of the experimental results.  
Figure 9a shows a partial cross-section of cross-hatch printed material, where the spacing between the tracks equals their diameter, resulting in dense packing. It is easy to see that the volume fraction of the voids in this case equals \(p=1\)-\(\pi/4=0.22\). (This is much higher than the observed porosity values due to flattening of the real printed tracks: see below) If the track spacing is either larger or smaller than the track diameter, the packing density is reduced and porosity increases (Figure 9b,c). This explains the observation that there is a \"sweet spot\" of printer settings, where porosity is minimised. A linear  
Figure 8: Correlation between porosity and the strength of the diffraction peak.  
Figure 9: A schematic drawing of a small part of the cross-section of 3D printed materials, depicting three layers and four end-on tracks. This simple geometric model shows variations in porosity as a function of the printer settings. \(D\) is the track diameter; \(L\) is the distance between the centres of neighbouring tracks; \(H\) is the vertical distance between layers. (**a**) Cross-hatch pattern, \(L=D\); (**b**) cross-hatch pattern, \(L>D\); (**c**) cross-hatch pattern, \(L<D\); (**d**) circular pattern, hexagonal packing; (**e**) similar to (**a**), but with tracks deformed due to melting; (**f**) layer dimensions.  
printing pattern can produce a dense hexagonal packing (Figure 9d), where porosity equals \(p=1\)-\(\pi\)/(\(2\surd\)) = 0.09, explaining why the observed porosity is lower in the linear-printed samples. The model also allows interpretation of the positive correlation between porosity and the diffraction loss peak: Bragg diffraction arises due to variations in the refractive index; these are larger in materials with higher porosity. Finally, Figure 9e depicts a more realistic picture of the 3D-printed structure, showing tracks that are deformed and flattened due to melting. Such deformation fills in the voids, significantly reducing porosity and explaining why the measured values are much lower than those estimated by the model.  
The simple model depicted in Figure 9 shows that a \"sweet spot\" for minimum porosity occurs when the track spacing and layer height are both equal to the track diameter, i.e., \(D=L=H\). However, track deformation due to melting should also be taken into account. Minimising porosity will also lead to a reduced diffraction loss peak.
## 2 Materials and Methods
### Multi-Temporal SPOT5 Pan-Sharpened Surface Reflectance
#### 2.1.2 Surface Reflectance  
The SPOT5 multispectral images were atmospherically corrected and adjusted for bi-directional reflectance and topography, representing surface reflectance with a nadir-view and a 45\({}^{\circ}\) incidence angle [41]. Elevation data were obtained from the 1 s SRTM DEM [42; 43; 44].
## 5 Conclusions  
The study has demonstrated that _P. euphratica_ forest is a considerable carbon sink in hyper-arid regions, with an average NEP of 334 g C m\({}^{-2}\) year\({}^{-1}\), which is comparable to levels of other riparian forests. The seasonal and inter-annual variations in NEP were mostly driven by temperature and groundwater depth under similar eco-physiological processes, which were significantly correlated with the \(T_{\text{a}}\), PAR, \(VPD\) and \(E_{p}\). Moreover, the inter-annual variation wherein annual NEP was lower due to higher temperatures and shallower groundwater depth in 2014 and 2016, was likely to be controlled by groundwater, since there were similar meteorological variables among the years. Finally, we demonstrated that accumulation of salinity was likely to occur with higher temperatures and shallower groundwater depths, thus affecting the growth of the desert riparian forest.  
The following are available online at www.mdpi.com/1999-4907/8/10/379/s1, Table S1: Monthly change of climatic variables included the mean (\(T_{\text{mean}}\), \({}^{\circ}\)C), maximum (\(T_{\text{max}}\), \({}^{\circ}\)C), minimum (\(T_{\text{min}}\), \({}^{\circ}\)C) of air temperature, precipitation (\(P\), mm), relative humidity (\(RH\), %), sunshine duration (\(S_{\text{d}}\), hours), wind speed (\(U\), m s\({}^{-1}\)) and pan evaporation (\(E_{\text{p}}\), mm) at Ejin station during the past 60 years (1957-2016).  
**Acknowledgments:** This work was funded by the National Natural Science Foundation of China (No. 31370466, No. 41401033 and No. 31370467). This work also was supported by the Key Research Program of Frontier Sciences, CAS (QYZD)-SSW-DQC031). The authors thank anonymous referees for their reading of this manuscript and helpful reviews and comments.  
**Author Contributions:** [PERSON] analyzed data and wrote the paper; [PERSON] conceived the study; [PERSON] collected data and revised the paper; [PERSON] and [PERSON] revised the final paper.  
**Conflicts of Interest:** The authors declare that they have no conflict of interest.  
Figure 9: The relationship between net ecosystem productivity (NEP) and groundwater depth (GWD) in July and August across three years (2014–2016) at the study site.
## 7 Future Perspectives  
Despite all the advantages of the FRF, a broader penetration of this technology beyond academic centres has not happened thus far for the following reasons. First of all, CS reconstruction is time-consuming even on very powerful computer hardware, which adversely affects clinical utility where almost immediate image reconstruction is a requirement. However, this may be further addressed with AI-based algorithms and thorough validation. Secondly, image analysis software is currently not designed to accommodate 5D data, and while there is no product of a free-running approach on any vendor's MRI system, there will not be an analysis software soon that can accommodate these 5D datasets. Coupled with that is the significant increase in data to be analysed: while a traditional cardiac 2D scan includes 10-12 short-axis slices of 5-10 mm each, the isotropic free-running dataset with an isotropic spatial resolution of 1-2 mm easily leads to a 5- to 10-fold increase in multiplanar reformatted planes that are available for analysis and can no longer be handled by a single operator in a reasonable amount of time. Therefore, AI-assisted algorithms are also needed here to make inroads. Finally, the utility and reproducibility of this approach will  
Figure 11: Cardiac cine imaging in various short-axis, and left two-chamber and four-chamber cardiac views (using multiplanar reconstructions), acquired using the 5D free-running framework in two healthy adult subjects using a 7 min acquisition without contrast at 0.55 T. Despite the significantly increased noise and decreased blood–myocardium contrast at low field compared to 1.5 T, cardiac anatomy and function can still be depicted in detail using the fully automated free-running framework without the need for an ECG signal or repetitive breath-holding.  
have to undergo deliberate and rigorous testing in larger multi-centric trials that include gold-standard comparisons.
## 2 AM Categories and Common Metal AM Processes
### Material Extrusion (ME)  
Figure 2 shows that ME technologies made up 10% of the metal AM market in 2020 [18]. Stratasys Ltd. patented the Fused Deposition Modeling (FDM) process, which uses thermoplastic polymers, in 1989 [5]. In 2009, the patents expired, and other manufacturers started to build new versions of FDM, which are called Fused Filament Fabrication (FFF) [19]. One of the recent developments in the metal AM market is the development of metal filled filaments, which can be printed using FFF machines. In this filament, the metal powder is infused in a standard ABS or PLA filament, and the powder percentage can vary. The prints made using these filaments are not pure metal parts, but rather consist of metal particles in a polymer matrix, and are brittle and require care while handling the printed objects [20]. The parts fabricated using these processes are not metallic parts, but rather parts with metal content. It should be noted that the tensile strength of the filaments is reduced as the metal particle content increases; however, the thermal conductivity of the filaments increases, and can make them suitable for fabricating circuits and electromagnetic parts [21]. Bound Powder Extrusion (BPE) is another recent ME development in the metal AM market. In this technology, the filament consists of a plastic binding agent and fine metal powder that is extruded through a nozzle. The desired part is printed layer by layer in a fashion nearly identical to conventional FFF printing; however, this process is followed by two additional post processing operations: washing and sintering, to produce a final high density fully metal part.  
Figure 2: Metal Additive Manufacturing Market in 2020.
## 4 Experiments and Results
### Training Infrastructure  
Experiments outlined in this paper utilize the Carbonate supercomputer's GPU partition, a powerful research cluster specialized for deep-learning tasks. Located at Indiana University, this resource offers 24 GPU-accelerated nodes, each equipped with cutting-edge hardware as follows:  
* NVIDIA Tesla V100 GPU: Delivers high-performance graphics processing for efficient training.
* Intel Xenon Gold 6248 20-core CPU: Provides robust central processing power for computations.
* 1.92 TB solid-state drive: Ensures fast data storage and retrieval.
* 768 GB of RAM: Supports large model training and data handling.  
This research was supported in part by Shared University Research grants from IBM Inc. and Lilly Endowment Inc. and by Indiana University's Pervasive Technology Institute [39]. Additionally, the experiments employed the following:  
* PyTorch 1.12.1: A deep-learning framework for model development and training.  
* Python 3.7.9: The general-purpose programming language used for the research.
* CUDA 11.3: Enables efficient utilization of the NVIDIA GPUs for computations.
# Outlier Detection Based Feature Selection Exploiting Bio-Inspired Optimization Algorithms
## 4 Projection Pursuit  
PP belongs to the same family of the Principal Component Analysis (PCA) method. The central target of PP is to look for linear projections (combinations of variables) that reveal interesting structures in multidimensional and large data [35; 36]. These projections are in one, two, or three dimensions. The interesting projections are found through the optimization of the projection index using a robust optimization algorithm capable of determining the optimum as well as the local optima that can also reveal hidden interesting projections [27]. The PP indices are categorized according to the search purpose, for example, detecting outliers or finding clusters. In this research study, the interesting projections are those that detect outliers. [PERSON] [35] and [PERSON] and [PERSON] [36] showed that the Gaussian distribution is the least interesting one, while the most interesting structures are those that bear a minimum resemblance to the Gaussian distribution. Sometimes the outliers can form a small cluster, PP can also identify unbalanced clusters, and it considers the smallest one as outliers.  
Before going through the details of PP, it is necessary to introduce the principal idea of this study. The concept of this proposal is to detect the outliers in the feature set instead of the data objects. To do this, the dataset matrix must be transposed. Given the mathematical definition, let us denote by \(X\) an \((P\times N)\) dataset matrix, and \(X^{\prime}\) an \((N\times P)\) transposed dataset matrix, where the rows represent the features (\(N\) features) and the columns stand for the observations or the samples (\(P\) observations). It is worth knowing that the transposed matrix is used in PP (instead of the original dataset matrix) to find out the optimal projection that reveal the outliers (i.e., the irrelevant features). This research study considers a one-dimensional projection that can be defined as \(P\)-dimensional vector called \(a\). The coordinates of the projected data are presented as an \(N\)-dimensional vector \(w\) such that  
\[w=X^{\prime}a \tag{1}\]  
where \(X^{\prime}\) is the transposed dataset matrix and \(a\) is the projection vector.  
Therefore, finding the projection vector \(a\) is just optimizing the PP index \(I(a)\). For more details about PP, one can refer to [27; 37].  
In this research work, two well-known PP indices dedicated to the search of outliers [27; 38] are proposed, including the kurtosis index [39] and Friedman index [40]. The detection of the outliers is univariate.
# Cyber Security Risk Modeling in Distributed Information Systems
## 2 Literature Review and Problem Statement
### Main Approaches to Risk Assessment  
There are two main approaches to risk analysis--qualitative and quantitative [50; 51; 52].  
* Quantitative risk analysis is a numeric estimate of the overall effect of risk on the project objectives, such as cost and schedule.
* Qualitative risk analysis prioritizes the identified project risks using a pre-defined rating scale and involves identifying threats and the potential impacts if they do.  
The most attractive, at first glance, is a quantitative approach that allows comparing the security of different systems using numerical scale and mathematical representation of found values, but its implementation is complicated for a few reasons and limitations [53; 54]  
Figure 4: Data from PwC’s “The Global State of Information Security® Survey 2016”.  
In this regard, a qualitative approach is currently used for risk analysis. It provides a simple ranking of threats and associated risks according to their severity. Probability data is not required and only estimated potential loss is used. Most qualitative risk analysis methodologies use several elements: vulnerabilities, threats, and controls. This is the most widely used approach to risk analysis in distributed systems nowadays [55]  
The article [56] presents a systematic review of mapping (SMR) tools that automate the cybersecurity risk assessment step based on research published between 2012 and 2020. The paper provides an update on the automation of the risk assessment phase. In addition, variables are identified that are taken into account by models of international organizations: ISO, NIST, OWASP, etc. The model helps to assess risks, regardless of the size of the organization, and takes into account the opinions and improvements provided by cybersecurity experts. However, this paper does not present the other risk management steps (risk treatment, monitoring, and analysis) that will be needed to develop new cybersecurity risk management models.  
The article [57] considers the cyber risks of a critical transport infrastructure implemented using IoT technology, which can become a target for many threats due to its cyber-physical nature. The new approach uses decision scales and importance indices to help stakeholders assess the level of vulnerability, the source of the threat, and the physical impact. Monte Carlo simulation results have shown that IoT-enabled transport infrastructure is prone to cyber-physical attacks when a threat source can physically access IoT devices, resulting in high levels.  
In the article [58], the authors present a generic configurable SCADA system dependency model that captures complex dependencies within a system and facilitates targeted risk assessment. The model was developed by collecting and analyzing dependencies in the SCADA system from 36 experts in the field. The authors demonstrated how dependency modeling can be used for risk assessment using the example of a SCADA system for water distribution. However, there is no comparison of SCADA DM with other emerging target models of the SCADA system.
## 2 Results  
The PRISMA diagram shows the literature research results (Figure 1). We identified 1001 records overall for screening. A total of 114 records were retrieved and assessed for eligibility. Finally, 18 full-text manuscripts, including 13 randomized clinical trials (RCTs) and five prospective comparative studies met inclusion criteria and were included. Regarding urological field, eight studies assessed the use of BTX-A in bladder pain syndrome (BPS)/painful bladder syndrome (PBS)/interstitial cystitis (IC), three studies in prostate pain syndrome (PPS)/chronic prostatitis (CP) and one study in chronic scrotal pain (CSP). Moreover, four studies investigate the role of botulinum toxin injections in gynecological pelvic pain (GPP) and two studies evaluated its role in myofasical pelvic pain (MPP). The characteristics of these studies, including patients' demographics details are summarized in Supplementary Table S1. There was a significant heterogeneity in the design and in the outcomes measured by each study and it means that is difficult to estimate the global response of BTX-A among pain and other functional outcomes. To facilitate data comparison and interpretation, we divided all the studies evaluated into major subtypes of CPPS and reported main data extracted among the reduction in pain score and frequency and noturia episodes in separate dedicated tables.
## 4 Experimental Results  
We conducted a number of experiments to compare the FQS algorithm with other state-of-the-art QS algorithms, which are known to be among the fastest in practice: FJS [13], Horspool [14] and the QS itself [10]. The implementation of FJS is provided by their authors in the paper [13]. The implementation of the other two competitive algorithms are downloaded from the website developed by [PERSON] and [PERSON] ([[http://www-igm.univ-mlv.fr/](http://www-igm.univ-mlv.fr/)]([http://www-igm.univ-mlv.fr/](http://www-igm.univ-mlv.fr/)) lecroq/string/). Their website provides the C code for a large number of exact string pattern matching algorithms, which they reviewed in [1,9]. Our implementation of the FQS algorithm is also based on the codes for the QS algorithm provided at the site.  
The experiments were conducted on two sets of data: one is a set of randomized text files, the other contains three practical text files. These three practical text files, _E. coli_, Bible and World192, were downloaded from the Large Canterbury Corpus ([[http://corpus.canterbury.ac.nz/](http://corpus.canterbury.ac.nz/)]([http://corpus.canterbury.ac.nz/](http://corpus.canterbury.ac.nz/))). The computing environment was a personal computer with an Intel Core2 CPU with 1.66 GHz and 8 GB of RAM working in the Ubuntu 12.04 operating system.
## 5 Off-Design Analysis  
### Quasi-Steady State Analysis  
To establish the control logic for the part-load operation, an in-house quasi-static cycle analysis code, KAIST-QCD (Quasi-static Cycle Design) was developed by a KAIST research team [32]. In the system performance analysis, various factors influence the system behavior. Quasi-steady state is defined as a pseudo steady state of the system and the analysis method is applied when the off-design performance of the system is important but the transient performance is less of an interest. The quasi-steady state analysis provides quick and simple solutions that describe each operating condition in the system. Compared to the transient analysis, the quasi-steady state analysis requires less calculation time and provides useful information for designing control schemes for the system.  
In this paper, the component design parameters of compressors, turbines and, heat exchangers described previously are used for the quasi-steady state analysis. During the quasi-steady state analysis, compressor inlet condition is first defined. Compressor performance is affected by two factors: the compressor inlet condition and the rotating speed. Based on turbine and compressor off-design performance maps, mass flow in the system is calculated. After that, heat loads between hot and cold side of heat exchangers are calculated. The algorithm of quasi-steady state analysis is shown in Figure 27. In this scenario, it is assumed that only mass inventory control was applied, and RPM was kept at design value for grid stability.  
Figure 27: S-CO\({}_{2}\) cycle quasi-steady state analysis code algorithm.