esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 System Model Analysis  
We consider a three-dimensional underwater sensor network for environmental monitoring (see Figure 2). In this architecture, a certain number of underwater sensors are deployed to cover a large area of a lake or ocean. Metal pieces at the seabed and thin ropes are used to attach the sensor nodes and distribute them at different depth levels [1]; they are deployed at different water depths to observe water quality parameters since water characteristics vary mostly in vertical direction. An electronically controlled engine at the sensors is used to change their depths [1]. This deployment strategy requires building a tree-like hierarchical multi-hop routing topology. The sensed data is sent to the surface station, which floats on the water surface and uses long distance radio communication to send this data to an onshore station. The onshore station is located at the monitoring center and performs further analysis of the data.
# Progressive Collapse Analysis of SRC Frame-RC Core Tube Hybrid Structure
## 4 Progressive Collapse Analysis
### Nonlinear Dynamic Analysis
#### 4.5.1 Case 1--Column C1 at Ground Floor Removed

Table 12: The maximum inter-story displacement angle in cases 2, 3, 4 and 5. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Case** & **Seismic Wave** & **Shear Distribution** \\ \hline \multirow{4}{*}{2 (C2)} & HOLLYWOOD & 1:1.76 \\  & ELCENTRO & 1:2.19 \\  & NRIGDE & 1:2.01 \\  & Artificial wave & 1:2.17 \\ \hline \multirow{4}{*}{3 (C3)} & HOLLYWOOD & 1:1.81 \\  & ELCENTRO & 1:2.03 \\  & NRIGDE & 1:2.05 \\  & Artificial wave & 1:2.19 \\ \hline \multirow{4}{*}{4 (W1)} & HOLLYWOOD & 1:1.37 \\  & ELCENTRO & 1:1.61 \\  & NRIGDE & 1:1.47 \\  & Artificial wave & 1:1.59 \\ \hline \multirow{4}{*}{5 (W2)} & HOLLYWOOD & 1:1.23 \\  & ELCENTRO & 1:1.58 \\  & NRIGDE & 1:1.44 \\  & Artificial wave & 1:1.56 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Shear distribution in cases 2, 3, 4 and 5. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Case** & **Beam Number** & **Seismic Wave** &
\begin{tabular}{c} **Average Bending** \\ **Moment/kN-m** \\ \end{tabular} & **Portion (\%)** & **DCR** \\ \hline \multirow{4}{*}{2 (C2)} & \multirow{4}{*}{B1} & HOLLYWOOD & 748.75 & 48.74 & 1.54 \\  & & ELCENTRO & 885.00 & 51.12 & 1.82 \\  & & NRIGDE & 1576.25 & 49.03 & 3.24 \\  & & Artificial wave & 1445.00 & 48.31 & 2.97 \\ \cline{2-5}  & \multirow{4}{*}{B4} & HOLLYWOOD & 787.50 & 51.26 & 1.62 \\  & & ELCENTRO & 846.25 & 48.88 & 1.74 \\  & & NRIGDE & 1638.75 & 50.97 & 3.37 \\  & & Artificial wave & 1546.25 & 51.69 & 3.18 \\ \hline \multirow{4}{*}{3 (C3)} & \multirow{4}{*}{B2} & HOLLYWOOD & 365.00 & 25.44 & 0.75 \\  & & ELCENTRO & 447.50 & 22.13 & 0.92 \\  & & NRIGDE & 730.00 & 23.86 & 1.5 \\  & & Artificial wave & 627.50 & 22.70 & 1.29 \\ \cline{2-5}  & \multirow{4}{*}{B5} & HOLLYWOOD & 622.50 & 43.38 & 1.28 \\  & & ELCENTRO & 870.00 & 43.02 & 1.79 \\  & & NRIGDE & 1245.00 & 40.69 & 2.56 \\  & & Artificial wave & 1090.00 & 39.44 & 2.24 \\ \cline{2-5}  & \multirow{4}{*}{B5} & HOLLYWOOD & 447.50 & 31.18 & 0.92 \\  & & ELCENTRO & 705.00 & 34.86 & 1.45 \\  & & NRIGDE & 1085.00 & 35.46 & 2.23 \\  & & Artificial wave & 1046.25 & 37.86 & 2.15 \\ \hline \multirow{4}{*}{4 (W1)} & \multirow{4}{*}{B5} & HOLLYWOOD & 602.50 & 48.20 & 1.24 \\  & & ELCENTRO & 782.50 & 40.94 & 1.61 \\  & & NRIGDE & 1426.25 & 47.90 & 2.93 \\  & & Artificial wave & 1196.25 & 46.68 & 2.46 \\ \cline{2-5}  & \multirow{4}{*}{B8} & HOLLYWOOD & 647.50 & 51.80 & 1.33 \\  & & ELCENTRO & 1128.75 & 59.06 & 2.32 \\  & & NRIGDE & 1551.25 & 52.10 & 3.19 \\  & & Artificial wave & 1366.25 & 53.32 & 2.81 \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Force condition of beams in cases 2, 3, 4 and 5.
_Article_  
**Correlation between Bone Turnover Markers and Bone Mineral Density in Patients Undergoing Long-Term Anti-Osteoporosis Treatment:**  
**A Systematic Review and Meta-Analysis**  
**[PERSON] 1,2,1,2,**, [PERSON] 2,1, [PERSON] 2,3, [PERSON] 1,2,**, [PERSON] 2 and [PERSON] 2,4,**()  
1 Department of Chemistry, Tsinghua University, Beijing 100084, China; [EMAIL_ADDRESS] (L_H.); [EMAIL_ADDRESS] (M.W.)  
2 A Division of Life Science and Health, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China; [EMAIL_ADDRESS] (D.L.); [EMAIL_ADDRESS] (F.W.); [EMAIL_ADDRESS] (Y.C.)  
3 School of Life Sciences, Tsinghua University, Beijing 100084, China  
4 Tsinghua-Berkeley Shenzhen Institute (TBSI), Tsinghua University, Shenzhen 518055, China  
* Correspondence: [EMAIL_ADDRESS]; Tel.: +86-755-26033033  
1 They are co-first authors.  
Received: 19 December 2019; Accepted: 18 January 2020; Published: 24 January 2020  
**Featured Application: This article was conducted to systematically analyze the correlation between BTMs and BMD and identify potential BTMs that show promising correlations with BMD changes in patients treated for osteoporosis, providing a clinical reference for the physicians in evaluating the long-term effect of the intervention on BMD changes and further on the future risk of fragility fracture.**  
**Abstract: This systematic review and meta-analysis aimed to evaluate the correlations between the bone turnover markers (BTMs) and the bone mineral density (BMD) in patients treated for primary osteoporosis and to identify promising BTMs for the prediction of future BMD changes. The PubMed, EMBASE, Cochrane Library, and Web of Science databases were searched for relevant studies that investigated the relationship between the BTMs and the BMD changes in patients treated for osteoporosis. All significant correlation coefficients of the baseline BTMs or changes in BTMs from baseline with the BMD changes from baseline under different interventions from eligible studies were used for systematic review and the subgroup analyses. The correlations were analyzed in terms of bone sites, intervention, time duration of BTMs measurements, and time duration of BMD measurements. Twenty-two records reporting correlation coefficients and the corresponding \(p\)-values were included, 13 of which were enrolled in the further subgroup analyses. The combined results from the systematic review and meta-analyses indicated that the changes in osteoclast (OC), procollagen type I N propeptide (PINP), and urine N-terminal crosslinking telopeptide of type I collagen (U-NTX), or the PINP at baseline tended to be useful in evaluating the long-term BMD changes after drug intervention.**  
**Keywords: bone turnover markers; bone mineral density; correlation; osteoporosis; systematic review and meta-analysis**
## 3 Methodology
### End-to-End Delay  
Figure 2(a) presents the end-to-end delay of the proposed NBEER protocol, while Figure 2(b) showcases a comparison between NBEER and state-of-the-art routing protocols such as Co-UWSN and CEER. In this analysis, the end-to-end delays of NBEER, Co-UWSN, and CEER were evaluated and compared using Table 7. The study incorporated simulations of various technologies not considered in the analysis, and their results were integrated into the findings. From the observations in Figure 2(a),b, it is evident that NBEER exhibits lower end-to-end latency compared to competing technologies. This advantage can be attributed to the shorter minimum forwarding distance between nodes in both dense and sparse conditions.  
On the other hand, Co-UWSN and CEER experience increased end-to-end delays in the final round due to extended data transmission distances. Network stability is achieved after approximately 4000 rounds as the network sparsity gradually increases, leading to data transmission over the shortest feasible distance. Co-UWSN and CEER, through threshold modifications and weighting functions, facilitate network load balancing, resulting in shorter end-to-end delays compared to NBEER. These modifications also contribute to the transmission of more reliable packets, thereby minimizing retransmissions. This characteristic is particularly advantageous for collaborative schemes like Co-UWSN and CEER, which are gaining popularity in the field.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & E2 ED & E2 ED & E2 ED & E2 ED & E2 ED & E2 ED & E2 ED & E2 ED & E2 ED & E2 ED & Avg \\ \hline
**Protocol** & **After** & **After** & **After** & **after** & **After** & **After** & **After** & **After** & **After** & **After** & **E2 ED** \\  & 1000 s & 2000 s & 3000 s & 4000 s & 5000 s & 6000 s & 7000 s & 8000 s & 9000 s & 10,000 s & \\ \hline
**Co-** & 84.03 & 80.01 & 73.02 & 60.01 & 53.03 & 51.01 & 46.01 & 43.03 & 34.03 & 34.01 & 55.19 \\
**UWSN** & 86 & 82 & 79 & 76 & 75 & 72 & 68 & 70 & 62 & 68 & 73.8 \\
**NBEER** & 78 & 74 & 68 & 65 & 60 & 57 & 45 & 44 & 42 & 25 & 53.03 \\ \hline \hline \end{tabular}
\end{table}
Table 7: End-to-end delay after equal intervals of time.  
Figure 3: (**a**) End to End delay of the Proposed work (**b**) End-to-end delay of Present and Proposed Works vs. time (s).  
Figure 4 shows the average end-to-end delay values, highlighting NBEER's advantage over Co-UWSN and CEER, with NBEER exhibiting an average end-to-end delay of 53, outperforming Co-UWSN (55.19) and CEER (73.8). The successful implementation of neighbor node cooperation and direct data transfer contribute to NBEER's exceptional performance in comparison to other state-of-the-art protocols.
## 3 Results and Discussions
### Effect of Nanoparticles on Compaction Characteristics  
The compaction characteristics of untreated and nanoparticles-treated residual soils are shown in Figure 2. The maximum dry density (MDD) recorded for natural soil (NS) was 1.828 kg/m\({}^{3}\). With the presence of magnesium hydroxide nanoparticles, the MDD increased to 1.837 kg/m\({}^{3}\). However, the MDD decreased to 1.794 kg/m\({}^{3}\) for soil samples treated with calcium hydroxide nanoparticles. The optimum moisture content (OMC) of NS had a value of 18%, and upon treatment with calcium and magnesium hydroxide nanoparticles, the OMC of the treated soils reduced to 17.1% and increased to 19.3%, respectively. This observation shows that the presence of magnesium and calcium hydroxide nanoparticles have different impacts on the compaction characteristics of residual soils. The increment in MDD and reduction in OMC of magnesium hydroxide nanoparticles-treated soil (MS) can be related to the decreased thickness of DDL around the soil particles due to the presence of divalent magnesium cations. The reduced thickness of DDL led to lower attraction of water molecules; thus, this contributes to the reduction in the OMC and increment in MDD of MS. The compaction characteristics of calcium hydroxide treated residual soils (CS) exhibited a different trend whereby their MDD decreased accompanied by increased OMC. Similar trends were reported by [29; 30] that adopted calcium-based additives in the stabilization of residual soils. It is quite likely that the pozzolanic reactions between the soil and calcium ions led to a higher accumulation of water molecules around the soil particles [31]. This situation resulted in an increased OMC of CS, causing large water films developed around the soil particles. The presence of large water films prevents the closer packing of soil particles and reduces their MDD as observed.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Property** & **Natural Soil (NS)** & \
\begin{tabular}{c} **Calcium Hydroxide** \\ **Treated Soil (CS)** \\ \end{tabular} &
\begin{tabular}{c} **Magnesium Hydroxide** \\ **Treated Soil (MS)** \\ \end{tabular} \\ \hline Liquid limit (\%) & 50.10 & 46.00 & 41 \\ Plastic limit (\%) & 21.80 & 20.35 & 19.65 \\ Plasticity index (\%) & 28.30 & 23.85 & 21.35 \\ British Standard (BS) Soil & CH & CI & CI \\ Classification System & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Arterberg limits of the untreated and nanoparticles-treated residual soil samples.
# A Virtual Learning Architecture Enhanced by Fog Computing and Big Data Streams
## 4 Big Stream Fog-Based e-Learning Architecture
### Integration of Storm with the Proposed Architecture  
In this subsection, we detail the integration of Storm with the aforementioned architecture. Storm is a stream-processing engine that is free, open source, fast, scalable and fault-tolerant. It is capable of integration with all known Databases (DBs) and to process and partition streams of data as necessary. In Storm, a stream is an unbounded sequence of tuples, themselves processed in parallel in a distributed fashion. The tuple features an ID and a series of fields that in our case can identify the student, the LO, the time of the day that the LO was accessed, the GPS coordinates of the place that the LO is being used in, the time spent on the LO, the teacher who created it, the course the LO belongs to, and so on. Storm's model is based on a topology, a graph connecting spouts and bolts.  
The spouts of Storm, that is the sources of data, can be of two different types: reliable and unreliable, i.e., they respectively do not forget and forget the data to be transmitted in case of failure. In our scenario, the spouts can be located both in the smart gadgets, which can be considered both reliable and unreliable sources according to their inherent nature (a sensor can be unreliable, while a smartphone can be reliable instead), and in fog gateways, in this case usually reliable, being capable of re-transmitting data. As explained in Section 4.5, the cloud layer can be considered filled with data sources as well and, as a consequence, possible spouts.  
Bolts are processing units performing operations such as filtering, aggregation, interacting with DBs, and the like. They can perform simple stream transformations or be grouped into multi-bolts in order to execute complex tasks and being sources, in turn, of multiple streams. In our architecture, bolts are located in fog gateways as slayers of more powerful master bolts located in the cloud. Cloud-bolts are in charge of directing the work of distributed fog-bolts across the network by using inner APIs, based on lookup and retrieval procedures as the ones described in Section 4.4. Moreover, by means of the stream grouping feature, cloud- and fog-bolts are also able to decide which streams they receive and how to partition them across different tasks of a certain bolt, e.g., in a shuffle mode, on the basis of certain fields such as the student ID, and so on.  
Figure 2: A general overview of the big stream and fog-based e-learning structure we propose.  
Storm provides an abstraction that allows one not to have to consider how the parallel tasks are performed, i.e., more worker processes are active for the same topology and, inside each worker, various threads execute different tasks for various spouts and bolts. In our architecture, however, worker processes are located only in cloud servers and in fog gateways, rather than in the ultimate smart devices.
## 3 Methods  
### General Workflow  
Evaluation of the positioning accuracy of a moving vehicle under a forest canopy is quite a challenging task. To evaluate this, a test field consisting of four different forest stands was established. Several different combinations of GNSS and IMU were mounted on an all-terrain vehicle (ATV) (Figure 1). The positions of 224 trees along the driving route were measured using total-station and real-time kinematic GPS. These trees were used to form the network of reference objects. In addition to GNSS and IMU, a laser scanner was mounted on the ATV. Thus, when a laser point cloud was computed using the recorded GNSS and IMU observations and laser scanner measurements, the position of the ATV could be determined based on the reference objects, _i.e._, trees, measured from the point cloud. The accuracy of the post-processed GNSS-IMU trajectory could be verified, and this trajectory was used as a reference for several other positioning systems. A large number of forest test plots 32 by 32 m in size were created along the trajectory, and the forest parameters for these test plots were estimated using up-to-date ALS data. This enabled the analysis of how forest parameters affect the accuracy of positioning.
## 4 Comparative Experiment and Ablation Study
### Comparison of Performance and Instance Mask of Different Prototype Branches
#### 4.2.2 Comparison of Instance Mask of Different Proto Modules  
The ProtoC1 (k = 1) structure is able to further reduce the parameters and computations of the model, but in our experiments, we found that using a 2D convolution of kernel size 1 reduces the accuracy of the instance mask, the \(mAP_{0.5-0.95}\) of the mask decreased 7.5%, so, we discarded this structure and used this structure as a comparison.  
The comparison of crack detection results of the YOLOv8n-seg model using the default Proto module, ProtoC1 module, and ProtoC1 (k = 1) module is shown in Figure 16. We can see that using the ProtoC1 (k = 1) structure results in roughness at the edges of the instance mask and a decrease in mask accuracy. The YOLOv8n-seg model using the ProtoC1 module, generates an instance mask with smooth edges and the same mask accuracy as the ProtoC1 model, while our proposed ProtoC1 module is more lightweight.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Module** & **Layer** & **Weight** & **Parameters** & **GFLOPs** & \(mAP_{0.5}^{mask}\) & \(mAP_{0.5-0.95}^{mask}\) \\ \hline YOLOv8m-seg(Proto) & 331 & 41.82 M & 27,286 K & 109.6 & 0.995 & 0.785 \\ +ProtoC1 & 325 & 40.51 M & 26,671 K & 90.8 & 0.993 & 0.778 \\ YOLOv8n-seg(Proto) & 261 & 7.15 M & 3409 K & 12.4 & 0.967 & 0.716 \\ +ProtoC1 & 255 & 7.04 M & 3352 K & 10.7 & 0.966 & 0.713 \\ +ProtoC1(k = 1) & 255 & 7.01 M & 3336 K & 9.9 & 0.928 & 0.638 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The comparison of YOLOv8n-seg and YOLOv8m-seg model using different Proto modules.  
Figure 15: Comparison of crack detection results of different algorithms. (**a–c**) are different crack pictures.
# Towards Deep-Learning-Driven Intrusion Detection for the Internet of Things
## 5 Implementation and Results
### Data Preprocessing  
The IoT simulation dataset consists of 5 million network transactions (represented as features) from the six sensors distributed in a smart home network simulation. We use Scapy, an open-source network penetration testing framework, to extract these features by stripping down each network packet. The 5 million network transactions were pruned out by the input data-preprocessing program to make the input dataset of 59,529 readings. It is important to note that these network simulations were gathered from two separate simulations, i.e., first simulation with all benign network transactions, and second simulation with a mix of malicious network transactions. Each network transaction in the second network simulation was marked as malicious as the entire network was affected by the malicious activities occurring within the network. In our experimentation, in the dataset of 59,529 transactions, a total of 31,046 network transactions were malicious while the rest of 28,483 network transactions were benign.
## 6 Conclusions  
In WSNs, sensor nodes suffer from limited energy and computational restrictions. Numerous data routing and dynamic or static data filtering schemes have been proposed that try to conserve energy in WSNs. Data routing protocols aimed at saving energy are always proposed without considering security measures. Moreover, data filtering schemes are proposed without regard to energy efficient data routing, wherein most of the underlying routing protocols are a low-cost-first routing protocol.  
In multi-hop communication, nodes near the BS are burdened with relaying heavy data to the BS, which creates a hotspot problem. Rapid depletion of energy at nodes around the BS eventually disconnect the rest of the network from the BS. Injection of false votes by adversaries have objective of depleting the energy of the intermediate nodes that relay the data generated by the source cluster. Similarly, legitimate data endorsed by compromised nodes with bogus MACs get dropped en-route during the en-route filtering that blocks true information from reaching the BS. Constant use of a minimum cost path results in depletion of energy at nodes on the same path and causes network partitioning. To tackle the unbalanced energy consumption problem in WSN from a data routing and filtering scheme perspective, we proposed a fuzzy rule-based data route and intermediate filtering node selection scheme. Our scheme allows cluster member nodes to generate authentication keys afresh and share these with the intermediate verification nodes. The proposed method demonstrates effective and actual efficiency in the WSNs, and extends the network lifetime while improving filtering capacity due to dynamic authentication key sharing. The data forwarding is carried out along the suitable path chosen in response to input factors such as energy status, the number of verification keys within a certain number of hops on the path, and the distance between the cluster and the base station. Dynamic intermediate verification node selection helps to improve the fitness of the route by improving the proximity of verification nodes to the event cluster. Compromised intermediate verification nodes no longer possess network-life-long authentication keys for report verification, which results in improved detection and filtering of false reports.  
This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (No. NRF-2015R1D1A1A01059484).  
[PERSON] and [PERSON] conceived and designed the study. [PERSON] performed the simulations, analysis, and wrote the manuscript.  
The authors declare no conflict of interest.