esa-sceva/satcom-chunk-collection huggingface dataset:
# Advanced Assistive Maintenance Based on Augmented Reality and 5G Networking
## 4 Scene Processing
### Objects Detection  
The problem of detecting isolated submeshes within the global mesh may be reinterpreted as a graph theory problem, leveraging the concept of connected components.  
Let \(M=(V,T)\) be a mesh object as defined in Section 4.1. One can build an undirected graph \(G=(V,E)\) where every vertex in \(G\) corresponds to a vertex in \(M\); an edge \(e\in E\) exists between two vertices if the same are connected by a triangle side in \(M\). By doing this, isolated submeshes in \(M\)'s domain translate into connected components in \(G\)'s one.  
**Definition 1**.: _A connected component (or simply component) of an undirected graph is a subgraph in which any two vertices are connected to each other by a path of edges, and which is connected to no additional vertices in the supergraph._  
Computing all the components of an undirected graph is achievable in linear time, \(O(|V|+|E|)\), using either breadth-first or deep-first search [33]. While looping through all vertices in \(V\), a new search is started whenever the loop encounters a vertex \(v\) that has not yet been included into a previously found component. The search will then find the entire component containing \(v\) before returning.  
Our implementation of the algorithm (using deep-first search) is provided in Algorithm 2. It is straightforward to see how this approach returns in fact _every_ connected component of the mesh-graph: along with actual objects and obstacles, walls themselves, for instance, constitute a component. We observed, however, that uninteresting outcomes can be easily filtered out by an adjustable threshold on components' dimensions (see Section 6.2).  
The set of identified obstacles is now sent back over the 5G link to the AR device, in the form of a list of 3D coordinates, allowing for such points to be highlighted to the operator by positioning holograms corresponding to their spatial locations.  
```
1:procedureComponents(\(G=(V,E)\))
2:\(S\leftarrow\emptyset\)
3:for\(v\in V\)do
4:if\(v\) not visited then
5:\(C\leftarrow\emptyset\)\(\triangleright\) New component
6:RecursDFS(\(v,C\))
7:endif
8:endfor
9:return\(S\)
10:endprocedure
11:functionRecursDFS(\(v,C\))\(\triangleright\) Deep-First Search
12: mark \(v\) as visited
13:\(C\gets C\cup\{v\}\)
14:for\(x\) adjacent to \(v\)do
15:if\(x\) not visited then
16:RecursDFS(\(x,C\))
17:endif
18:endfor
19:endfunction
```  
**Algorithm 2** Connected components retrieval algorithm for undirected graphs. Requires: \(G=(V,E)\), undirected graph. Ensures: \(5\), set of \(G\)'s connected components.
# Reliability Study on FRP Composites Exposed to Wet-Dry Cycles
## 3 Results
### Probability Distribution Type  
The above results show that the tensile strength of FRP is more sensitive to the environment, and its degradation is also more random, thus the variability of tensile strength need to be analyzed emphatically. The common distribution types for reliability analysis of FRP composites include Normal, Lognormal, Weibull and Gamma distribution. In this paper, these representative distribution types were chosen to fit statistical distributions of the data. In order to determine which distribution best fits the data, five widely used test methods, Shapiro-Wilk (S-W test), Kolmogorov-Smirnov (K-S test), Cramer-von Mises (C-M test), Anderson-Darling (A-D test) and Chi-Square (test), were adopted, as shown in Table 2.  
The goodness of fit (\(p\)) for the strength of FRP with/without wet-dry cycles exposure was listed in Table 3a-d. The size of the \(p\) value indicates the degree of coincidence for each probability distribution type. The larger the \(p\) value, the greater the probability that the sample will obey some distribution. In this paper \(\alpha=0.1\) was adopted as the significance level, so when \(p>0.1\), the sample is considered to fit a certain distribution. According to Table 3a, all of the four distribution types fit the tensile strength of CFRP without wet-dry cycles exposure very well, and the best fits are considered to be the Normal distribution and Weibull distribution due to the larger \(p\). As for the GFRP without wet-dry cycles exposure as shown in Table 3b, all \(p\) values calculated by the test for the four distributions were less than 0.1. However, for the other test, all \(p\) values of Normal distribution, Lognormal distribution and Weibull distribution were obvious, larger than 0.1. Meanwhile, the \(p\) values of Normal distribution and Weibull distribution were larger than Lognormal distribution. This indicates that the tensile strength  
Figure 3: The properties of FRP after wet-dry cycle exposure: (**a**) Elastic modulus; (**b**) elongation; (**c**) tensile strength of CFRP, and (**d**) tensile strength of GFRP.  
According to Table 3c,d, the probability distribution of FRP tensile strength is not significantly affected by the wet-dry cycles exposure. The tensile strength of CFRP is better subjected to Normal distribution and Weibull distribution. For the tensile strength of GFRP, except for the test, the other test methods are subjected to Lognormal distribution and Gamma distribution. Under each effective test, the Normal distribution and Weibull distribution are well obeyed. According to the size of the \(p\) value, the fitting degree of Normal distribution and Weibull distribution is better than that of Lognormal distribution and Gamma distribution, while the fitting degree of Normal distribution and Weibull distribution is very close.
## 4 Simulation
### Scenario 1  
One trial of the simulation described above was carried out. In this scenario, the target is jointly observed by the four robots intermittently. The observation measurement availability for both cooperative measurement and target measurement is based on the sensing ranges \(r_{c}\) and \(r_{t}\), respectively, and is shown in Figures 2 and 3.  
Figure 1: Simulation with four local robots and one moving target.  
Although, for each robot, the observability of the local state and the target's state cannot be guaranteed owing to the discontinuous range-only measurement, the joint observability for the entire system over a period of time can still be guaranteed through communication with neighbors according to [35].  
The estimated trajectories of both robots and the target are plotted in Figure 4 in different colors. Each robot's self-localization result and local target tracking result are drawn with solid lines of the same color. As observed in Figure 4, the estimated trajectories indicate that each robot is able to localize its true pose and track the true trajectory of the target. The four robots' self-localization errors and covariances (\(\pm 3\sigma\) bounds) are plotted in Figure 5, with solid lines in color and dashed lines in the same color, respectively. It shows that the self-localization errors by each robot are bounded by the \(\pm 3\sigma\) envelopes in the steady state. Robot 1 has the lowest tracking error as it can access its own absolute measurement. The target tracking results from the four robots are plotted in Figure 6, where, for each robot, the target tracking errors (solid line in colors) are bounded by the corresponding \(\pm 3\sigma\) envelopes (dashed line in the same colors) in the steady state. On the basis of Figures 5 and 6, the min/max self-localization and target tracking errors for time instance \(k\in[200,800]\) are listed in Table 1.  
Figure 3: Measurement availability of target to four robots with \(r_{t}=20\).  
Figure 2: Measurement and communication link availability between two robots with \(r_{c}=10\).  
Figure 4: Estimated trajectories of robots and the target in different colors (black dashed lines indicate the ground truth).  
Figure 5: Self-localization errors and covariances of robots 1–4.
# Performance Evaluation of NoSQL Document Databases: Couchbase, CouchDB, and MongoDB
## 3 NoSQL Document Databases
### Couchbase  
Couchbase is an open-source database document-oriented, developed in C++ by Couchbase Inc. in 2011. Couchbase is designed for web and mobile applications, where their documents are in JSON format and are stored in buckets. A bucket is a collection of documents that are made up of a unique key. Couchbase has its own query language, the NIQL. It provides nine programming languages and supports Linux, macOS, and Windows as operating systems. Couchbase can be downloaded at [[https://www.couchbase.com](https://www.couchbase.com)]([https://www.couchbase.com](https://www.couchbase.com)) (accessed on 10 March 2022) and has three versions: Couchbase Server, Couchbase Capella, and Couchbase Lite.  
Figure 1 illustrates a web interface for managing the Couchbase Server, including a dashboard that provides a graphical summary of the current server status from the statistics. A bucket tab is included, where it is possible to see the active buckets in the system, and allows the configuration of services and indexes. It is possible to add and remove cluster nodes and configure replication across data centers. It also creates and shows collections, documents, queries, and views [14; 15].  
Couchbase Server has a set of services managed by the Couchbase Server cluster. These can be implemented, maintained, or deleted independently of each other. The services are data, query, evening, indexing, full-text search, analytics, and backup.  
Couchbase has a clustered architecture, where the cluster consists of a group of nodes that uses a peer-to-peer topology. The services are established per node, where each node can run on at most one instance of a service, and the data service must run on at least one node. The components of a Couchbase node include the cluster manager and the  
Figure 1: Couchbase web interface.  
other services provided by the Couchbase Server, also including the storage and cache management components [15].  
The main characteristics and advantages of Couchbase are:  
* Provides triggers, CRUD (create, read, update, and delete) operations, ad hoc queries, MapReduce, and indexes;
* It uses MVCC (MultiVersion Concurrency Control);
* It supports master-master replication and master-slave replication;
* It supports horizontal and vertical scaling.  
Some of the limitations of Couchbase are as follows:  
* It is not compatible with the four key properties of a transaction: atomicity, consistency, isolation, and durability (ACID properties);
* Indexing takes up too much RAM.
## 4 Data Processing and Experimental Results: Sounder Mode  
Sounder raw data acquired during the campaign have been delivered by CO.RIS.T.A. and processed by IREA-CNR and University of Trento.
# A Review of Biochar Properties and Their Utilization in Crop Agriculture and Livestock Production
## 5 Biochar as a Feed Additive
Both manures treated with BC produced a significantly higher proportion of granules. [PERSON] et al. [61] evaluated the effects of a 0.8% and 3% BC diet inclusion for steers on growing and finishing diets. Digestibility, and CH4 and CO2 production from cattle were investigated. Dry matter intake was not affected by BC inclusion in the growing diet but increased quadratically in the finishing diet with the highest increase for the 0.8% biochar amendment. Supplementation with BC improved cattle digestion and also reduced CH4 and CO2 production by up to 9.5% and 9.7%, respectively, for the growing diet and by up to 18.4% and 9.9%, respectively, for the finishing diet. While this research shows that biochar as a feed additive can be used as a mitigator for CH4 and CO2 emissions from cattle breeding, it is interesting that the amendment of composing mixtures with biochar has opposite effect resulting in the production of those gases. A detailed evaluation of the biochar-manure interactions with a distinction to the way of application (directly to the manure or as a feed additive) would be interesting, so as to understand the mechanisms behind such opposite results. Some similarities between biochar-soil, biochar-compost, and biochar-animal digestive system can be observed--in the case of animal nutrition, BC amendment to the feed also has a potential to reduce gaseous emission from the manure and provide more nutrients to the animals, resulting in better health and lower feed intakes. Because of the complexity of BC parameters, a detailed evaluation of BC influence on animal health needs to be conducted every time and all the parameters have to be taken into account, including BC particles size, its surface area, C, O, N, H, and also heavy metals (as potential contaminants) content. All the differences in BC properties, as it was in the case of the previously described interactions, can lead to different effects on different animal species, e.g., the same amount of BC diet inclusion was able to increase laying hen production parameters and improve (decrease) the feed conversion ratio, while for chicken broilers the results were the opposite (Table 4). An optimal dose of BC as a feed additive should also be considered because too high a BC inclusion in an animal's diet can lead to digestive problems and thus have a negative influence on the animal's health. However, given that all the BC parameters, including its dose, are optimized, this charred material seems to be a favorable mean of improving the animals' health and their production performance, with more potential fields of utilization. For example, mitigation of odorous volatile organic compound emissions from the manure of livestock with a BC-supplemented diet.
# Low-Complexity Channel Estimation in 5G Massive MIMO-OFDM Systems
## 5 Simulation Results
### Accuracy Comparison  
The judgment of the merits and demerits of the estimated results of the algorithm can be considered from two aspects, namely, the estimated position and amplitude of the tap. Therefore, this section compares the results estimated by the algorithm with the channel model constructed by the experiment to verify its performance.  
Figure 4(a) is a simulation diagram of the test performed when \(SNR=20\) dB and the number of pilots \(N_{p}=700\). According to the experimental results, the results estimated by the CoSaMP algorithm  
\begin{table}
\begin{tabular}{c c} \hline \hline
**SNR** & **Corresponding \(\beta\)** \\ \hline \(<\)10 dB & 0.058 (Figure 3(a)) \\ 10–25 dB & 0.054 (Figure 3(b)) \\ \(>\)25 dB & 0.052 (Figure 3(c)) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Proposed algorithm stop parameter, \(\beta\) corresponding to SNR.  
Figure 4: Stop iteration parameter \(\beta\) under different SNR.  
are not accurate in both the position and the amplitude of the tap; while the results of the S-CoSaMP algorithm are only accurate in the position of some taps, and the tap positions and amplitudes of other results are not accurate. In contrast, the proposed SSA-CoSaMP algorithm is much more accurate, and the estimated taps are accurate both in position and amplitude.  
Figure 5: Accuracy comparison of different channel estimation algorithms when \(\text{SNR}=20\) dB.  
When \(SNR=20\) dB and the number of pilots \(N_{p}=900\), we compare the estimated results of the algorithms with experimental modeling, and we can get Figure 5b. According to the experimental results, the CoSaMP [14] algorithm still cannot accurately estimate, while the estimation performances of the SSA-CoSaMP algorithm and the S-CoSaMP [9] algorithm are basically the same, which are very accurate.
# Performance Analysis of Cache Based on Popularity and Class in Named Data Network
## 3 Static-CAPIC: Cache Based on Popularity and Class with the Static Cache Proportion  
The purpose of Static-CAPIC is to increase the network cache hit ratio using its simple mechanism while considering the popularity and content class. The basic idea is to keep the favorite content as close as possible to the consumer, by dividing the routers' content store for each class and assigning the different cache capacity for each content class according to their needs and character. The traffic character for each content class is modeled based on Table 1. The determination of cache capacity can be carried out by observing the optimum conditions. Furthermore, the Static-CAPIC performs a two-steps CAPIC mechanism as explained in Section 2.2, where the proportion of storage for each content class is pre-determined statically. Once the proportion is set, it does not change.
# Analytical Prediction of Balling, Lack-of-Fusion and Keyholing Thresholds in Powder Bed Fusion
###### Abstract  
This paper proposes analytical modeling methods for the prediction of balling, lack-of-fusion and keyholing thresholds in the laser powder bed fusion (LPBF) additive manufacturing. The molten pool dimensions were first predicted by a closed-form analytical thermal model. The effects of laser power input, boundary heat loss, powder size distribution and powder packing pattern were considered in the calculation process. The predicted molten pool dimensions were then employed in the calculation of analytical thresholds for these defects. Reported experimental data with different materials were compared to predictions to validate the presented analytical models. The predicted thresholds of these defects under various process conditions have good agreement with the experimental results. The computation time for the presented models is less than 5 min on a personal computer. The optimized process window for Ti6 Al4V was obtained based on the analytical predictions of these defects. The sensitivity analyses of the value of threshold to the laser power and scanning speed were also conducted. The proposed analytical methods show higher computational efficiency than finite element methods, without including any iteration-based computations. The acceptable predictive accuracy and low computational time will make the proposed analytical strategy be a good tool for the optimization of process conditions for the fabrication of defects-free complex products in laser powder bed fusion.  
analytical prediction; boundary heat transfer; powder size distribution; lack of fusion; balling behavior; keyholing defect Article
# A Comprehensive Review of Indoor Localization Techniques and Applications in Various Sectors
## 3 Methodology and Application of Indoor Localization in Various Fields
### Strategic Insights: Indoor Localization in Military Applications
#### 3.3.1 UWB-Based Methodology for Military and Emergency Operation
By combining GPS, LoRa, UWB, and altimeter data, and employing advanced sensor fusion algorithms, the system provides flexibility, robustness, and adaptability for critical military and firefighter operations.
# Iterative Decoding of LDPC-Based Product Codes and FPGA-Based Performance Evaluation
## 3 Proposed Low-Complexity Iterative Decoding for LDPC-Based Product Codes
### Iterative Decoding of Product Codes
Decode the LDPC codewords with \(ldpc\_flag=0\) using a generalized decoding algorithm. 2.3. For \(0<i\leq n_{1}\), update the \(ldpc\_flag(i)\) bit of the LDPC codewords with the \(c_{i}\) of \(0\) is set to true. \[ldpc\_flag(i)=\left\{ \begin{array}{ll}1,&\text{if}\ \ \ c_{i}=0,\\ 0,&\text{if}\ \ c_{i}\
eq 0,\end{array} \right.\]  
2.4. Compute the posterior probability information \(\hat{y}_{ldpc}\). **Step3: Outer decoding. **  
3.1. Decode the outer codewords with \(outer\_flag=0\) using the error-trapping algorithm;  
3.2. For \(0<j\leq k_{2}\), update the \(outer\_flag(j)\) bit of the outer codewords with the \(s_{j}\) of \(0\) is set to true. \[outer\_flag(j)=\left\{ \begin{array}{ll}1,&\text{if}\ \ s_{j}=0,\\ 0,&\text{if}\ \ s_{j}\
eq 0,\end{array} \right.\]  
3.3. After decoding, if the decoder corrects the error bits in the outer codewords,  
\(correct\_flag(i,j)=1\). 3.4. Compute \(Iter=Iter+1\), if \(Iter=Iter_{max}\) or \(ldpc\_flag=\mathbf{1}\), output the decoding result, otherwise, go to execute Step4;  
**Step4: Update the prior probability information**  
4.1. For \(0<i\leq n_{1},0<j\leq k_{2}\), if the \(correct\_flag(i,j)=1\) and \(outer\_flag(j)=1\), go to step 4.2;  
4.2. Update the prior probability information according to the decoding result of outer codewords \(y_{outer}\). Then, go to execute step 2;  
\[y_{ldpc}=sign(y_{outer})\left|\hat{y}_{ldpc}\right|\] \[=\left\{ \begin{array}{ll}\hat{y}_{ldpc}&\text{if}\ \ y_{outer} =0,\\ (-1)\times\hat{y}_{ldpc}&\text{if}\ \ y_{outer} =1.\end{array} \right.\]  
**Algorithm 1**The iterative decoding for product codes
## 3 Experimental Results
### Database of Coronary Angiograms  
The database used in this work consists of 130 X-ray coronary angiograms, and their corresponding ground-truth image outlined by an expert cardiologist. Each angiogram is a \(300\times 300\) pixels, gray-scale image in \(pgm\) (Portable gray map) format. The Cardiology Department of the Mexican Social Security Institute, UMAE T1-Leon has provided the whole image database, and the ethics approval for its use in the present research, under reference \(R-2019-1001-078\).  
To evaluate the detection and segmentation performance of the comparative methods presented in this paper, the database of X-ray images was divided into two subsets: training and testing. The training set consists of 100 images, and the remaining 30 angiograms form the test set.  
The complete database of X-ray images, along with the ground-truth images employed in this research will be available via web page ([[http://personal.cimat.mx:8181/~ivan.cruz/DB_Angiograms.html](http://personal.cimat.mx:8181/~ivan.cruz/DB_Angiograms.html)]([http://personal.cimat.mx:8181/~ivan.cruz/DB_Angiograms.html](http://personal.cimat.mx:8181/~ivan.cruz/DB_Angiograms.html))). This database represents the first set of angiograms and ground-truth images made accessible to the scientific community for research and comparison purposes.
# Design, Implementation, and Field Testing
## 3 System Design
### System Design Rationale
#### 3.2.2 Robust Activity Recognition Algorithm Design  
All non-compliance activities that a nursing assistant might engage in while performing bedside care share the same characteristic, i.e., the back is bending too much. Hence, we use the vector determined by the hip center joint and the shoulder center joint, and calculate the angle between this vector and the vector perpendicular to the floor plane, which gives the back flexion angle. This algorithm is robust again occlusion to the lower extremity of a nursing assistant; hence, it can be reliably used in a crowded room.