esa-sceva/satcom-chunk-collection huggingface dataset:
# Incremental Formula-Based Fix Localization
## 6 Related Work
### Spectrum-Based Fault Localization  
Most existing automatic fault localization techniques are spectrum-based fault localization (SFL) [4; 6]. SFL profiling the buggy program with a given test suite and count the number of passing and failing tests that cover a statement. Based on this coverage information, they compute for each statement a suspicious score which measures the likelihood of them being faulty. The output to developers a list of statements ranked by their suspicious score. SFL techniques require lightweight computation; thus, they can be applied to a very large program. However, these techniques usually return a long list of program entities with no context information. Moreover, in order to rank the actual faulty statement at the  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Solver Time**} & \multicolumn{3}{c}{**Running Time**} \\ \hline
**Bug ID** & **Agx** & **FS** & **FI** & **Agx** & **FS** & **FI** \\ \hline Chart5 & 470 & 571 & 108 & 3167 & 3953 & 1968 \\ Chart7 & 15,170 & 546 & 3 & 18,962 & 3376 & 3227 \\ Chart18 & 458 & 0 & 139 & 5749 & 2597 & 2434 \\ Chart22 & 220 & 400 & 119 & 3509 & 3070 & 2192 \\ Codec16 & 58 & 193 & 76 & 2237 & 3085 & 2485 \\ Codec18 & 2938 & 139 & 152 & 7870 & 2341 & 2009 \\ Comp24 & 9370 & 212 & 179 & 26,295 & 3950 & 3094 \\ Comp27 & 29,469 & 1219 & 355 & 63,236 & 6746 & 5035 \\ Comp6 & 46 & 67 & 3 & 1586 & 1745 & 1368 \\ Csv2 & 5 & 10 & 8 & 1419 & 2478 & 1390 \\ Csv8 & 202 & 102 & 81 & 2369 & 3841 & 2597 \\ Lang14 & 69 & 18 & 13 & 1772 & 1702 & 1668 \\ Lang21 & 2897 & 4755 & 4 & 6534 & 8128 & 2495 \\ Lang22 & 25,962 & 53,232 & 4 & 29,507 & 57,691 & 3452 \\ Lang30 & 166 & 141 & 2 & 2397 & 1974 & 1640 \\ Lang31 & 851 & 57 & 3 & 5757 & 2488 & 1616 \\ Lang40 & 445 & 19 & 3 & 6444 & 2438 & 1802 \\ Lang58 & 157 & 257 & 136 & 5907 & 2998 & 2298 \\ Math94 & 56,595 & 24,535 & 5 & 59,014 & 27,860 & 2569 \\ Math97 & 722 & 180 & 116 & 2512 & 2091 & 1661 \\ Mock11 & 4 & 10 & 7 & 1743 & 1807 & 1698 \\ Mock21 & 5 & 40 & 22 & 1725 & 2115 & 1766 \\ \hline Sum & 146,279 & 86,703 & 1538 & 259,711 & 148,474 & 50,464 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparing execution time of AgxFaults (Agx), Single-path Control-Flow-Sensitive (FS), and Single-path Control-Flow-Insensitive (FI) approaches on real bugs.  
top of the suspicious list, they require to have a comprehensive test suite that contains sufficiently many passing and failing executions. These limitations limit the usefulness of their fault localization result. Our approach required only a single failing test case, and it can return a small sets of suspicious statements, where a suitable modification can make the test become passing.
## 3 Interference Mitigation Schemes
### MAC Approach
#### 3.2.1 Dynamic Coexistence Management  
Dynamic coexistence management (DCM) [25] is a new MAC scheduling algorithm designed to manage the coexistence of WBASNs based on the IEEE 802.15.4 standard [5]. WBASNs with DCM are able to detect and mitigate the effects of coexistence independently. A scenario in which many WBASNs transmit simultaneously may cause beacon collision and data collision [11]; DCM uses beacon replacement and channel switching, respectively, to resolve these collisions.  
Because clear channel assessment (CCA) is not used in beacon transmission, beacon collision prevents the sensors from transmitting to the superframe. At the end of a contention-free period (CFP) in the superframe, the coordinator can detect a lost beacon. If a single beacon is lost, the coordinator searches the current channel for the inactive part of superframe. The beacon will resume normal activity if the coordinator concludes that the beacon was lost only once. In contrast, the beacon must be replaced on the timeline if the loss occurred at the second superframe or frames from coexisting WBASNs are detected. When a beacon loss is detected, the coordinator scans the current working channel during the beacon interval (BI) of the superframe to decide when to transmit a beacon. There are two cases: The coordinator will transmit a beacon in a gap of its own period, or it will replace the beacon at the largest available gap.  
In data transmission, the coordinator detects lost data at the end of the CFP. Whether these collided frames are retransmitted or not is based on acknowledged or unacknowledged mode, respectively. In the DCM scheme, the coordinator scans a candidate channel for a possible channel switch. The two possible options are the full BI scan and the inactive period scan, which are described as follows. In the case of the full BI scan, the coordinator will have information about the preexisting WBASNs in the candidate channel and the available gap for its own period. In case of the inactive period scan, the coordinator scans the channel to find a gap without missing a beacon, even though the information about the preexisting candidate channel is not available. In the case of an unsuccessful scan for a candidate channel, other channels will be monitored during the inactive parts of the next superframes. If there is no inactive period in superframe (SD = BI), the coordinator will search for the whole BI.
# DNCL: Hybrid DOA Estimation and NMDS Cooperative Multi-Target Localization for RFID
## 5 Experiment and Result Analysis
### Experimental Setup
#### 5.1.3 Metrics  
In the evaluation of the DNCL system, we used the localization error to measure its localization performance. The localization error is defined as the Euclidean distance in each experiment from the true position to the estimated position of the tag, which may be calculated as  
\[error=\sqrt{\left(\hat{x}_{i}-x_{i}\right)^{2}+\left(\hat{z}_{i}-z_{i}\right)^ {2}} \tag{20}\]  
where \(\left(x_{i},z_{i}\right)\) is the real position of the \(i\)th target tag, \(\left(\hat{x}_{i},\hat{z}_{i}\right)\) is the estimated position of the \(i\)th target tag.
## 5 Discussion
### Analysis of Carbon Emission Reduction and Payback Period  
As shown in Table 1, when the main engine runs at 85%SMCR, fuel consumption per hour is 3016.7 kg/h. The diesel engine's fuel consumption rate stands at 215 g/kWh, and the BORC system and PORC system can obtain a net output power of 293.1 kW and 329.2 kW, respectively. Therefore, carbon emission reduction can be estimated approximately from the fuel consumed. Assuming that the ship's main engine is running at 85% SMCR for 300 days with heavy oil, the BORC system can save 1.52 tons of fuel oil per 24 h on average by utilizing unused energy from the ship's main engine, with a fuel-saving rate of 2.10%, saving 456 tons of fuel oil and 1416 tons of carbon emissions reduction in a year. Using the PORC system to recover the waste heat of the ship's main engine can save 1.70 tons of fuel every day on average, with a fuel-saving rate of 2.40%. The annual fuel saving and carbon emission reductions are estimated at 510 tons and 1581 tons, respectively. When compared to the BORC system, the PORC system can save 11.7% more oil and reduce carbon emissions by 11.7%, which has clear advantages. It is worth emphasizing that actual carbon emission reduction should take into account variations in ship types, fuel class selection, actual sailing conditions of ships, environmental changes, and others.  
The investment initial cost of the ORC system in 2022 is calculated by the following equation [5,22]:  
\[C_{2022}=C_{2001}\frac{CEPCI_{2022}}{CEPCI_{2001}} \tag{11}\]  
Among them, CEPCI\({}_{2022}\) published by the American Institute of Chemical Engineers is 688.8, and CEPCI\({}_{2001}\) is 397 [23].  
The total investment cost in 2001 can be expressed as:  
\[C_{2001}=C_{V}+C_{con}+C_{\text{exp}}+C_{f,p} \tag{12}\]  
\(C_{\text{V}}\), \(C_{\text{con}}\), \(C_{\text{exp}}\) and \(C_{f,p}\) denote the investment cost of evaporator, condenser, expander and mass pump respectively, calculated as follows:  
\[C_{\text{exp}}=3.5\times[10e^{(2.705+1.44\times\lg W_{\text{exp}}-0.177\lg(W_{ \text{exp}})^{2})}] \tag{13}\]  
\[C_{\text{con}}=1397A_{con}^{0.89} \tag{14}\]  
\[C_{\text{V}}=1397A_{\text{V}}^{0.89} \tag{15}\]  
\[C_{f,p}=1120W_{f,p}^{0.8} \tag{16}\]  
\(W_{\text{exp}}\) denotes expander output power, kW; \(W_{\text{f,p}}\) denotes work pump power consumption, kW; A\({}_{\text{con}}\) and A\({}_{\text{v}}\) denote condenser and evaporator heat transfer area, respectively.  
Assuming an average seawater temperature of 25 \({}^{\circ}\)C, recovering of waste heat generated by 85% SMCR, the cost of the BORC system is approximately 640 thousand dollars and the cost of the PORC system is approximately 1.12 million dollars. Based on the fuel oil price provided by SHIP & BUNKER on 30 June 2023 [24], the yearly fuel cost savings of the BORC system are $205,884 and the fuel cost savings of the PORC system are $230,265. That means the BORC system has a payback period of around 2.8 years and the PORC system has a payback period of about 4.86 years.
# Secured Real-Time Machine Communication Protocol
## 6 Results and Analysis
### Remote Network  
Figure 10 illustrates the test results for a client connecting to a server located in a remote network via VPN over the Internet. Among the three protocols tested, ModBus, being the simplest, demonstrates the best performance, with an average connection time of 19.49 ms. SRMCP follows, with an average connection time of 50.49 ms, despite executing both the TLS handshake and the Kyber KEM. When compared to local network results, the remote network introduced a delay of 16.77 ms for ModBus and 40.86 ms for SRMCP, a 243% difference in terms of increased delay. This difference aligns with SRMCP performing two handshake procedures, whereas ModBus over TCP requires only one. The OPC UA exhibited the worst performance, with an average connection time of 129.57 ms. The remote network added a delay of 103.96 ms due to its complex procedures, which require multiple round trips to complete.  
Figure 11 depicts the time required for the client to read values from a remote server via VPN over the Internet. ModBus averages 52.87 ms per read operation, benefiting from its straightforward protocol with minimal packet overhead, making it efficient for basic data retrieval tasks. In contrast, the OPC UA requires an average of 95.62 ms per read, with a peak time of 253.05 ms. SRMCP, however, outperforms both protocols, with an average read operation time of 30.32 ms, due to its \"Port Hopping\" mechanism.  
Figure 12 presents the time required for the client to write a single variable to a server located on a remote network. ModBus exhibits the second-best performance with an average write operation time of 19.49 ms, attributed to its simplicity. Although ModBus confirms write operations by sending a response, it does not include the written value, limiting the client's ability to verify the data integrity directly. SRMCP, despite involving a dual layer of encryption and including the written value in the server's confirmation response, still outperforms ModBus with an average write operation time of 24.73 ms due to its simultaneous data transmission approach. In contrast, the OPC UA has the worst performance due to its heavy overhead, with an average write operation time of 92.52 ms and a maximum of 194.79 ms, which is more than twice the maximum write time of SRMCP at 70.20 ms.  
Figure 9: Testing results for writing a variable over a local network.  
Figure 11: Testing results for reading multiple variables over a remote network.  
Figure 10: Testing results for remote network connection time.
## 3 Material and Test Procedures  
### Composite Material and Test Specim  
A T700/epoxy composite laminate was used in this study. The mechanical properties of the composite material are shown in Table 1. All the properties were obtained from material tests conducted according to ASTM standards [24; 25; 26] and some references [27; 28; 29]. To validate the developed PFA method, tensile and compressive tests were performed for composite laminate specimens with open holes. The test specimen geometry and dimensions are presented in Figure 3. Some strain gauges were attached near the open-hole (2-2.5 mm apart from the hole edge) to measure the longitudinal strain value. The location of the attached strain gauges is illustrated in Figure 3. One of two holes were applied to the composite laminate specimen as a notch. The composite laminate was manufactured with two types of stacking sequences. The stacking conditions are listed in Table 2. The type-2 stacking sequence was only introduced to the one-hole tensile test specimens. A type-1 specimen exhibits a 90-degree orientation ply in the middle of the laminate. The specimens contain eight plies and the measured specimen thickness was 1.2 mm. To apply the DIC method, speckle patterns were introduced on the surface of the specimens.
## 2 AI for the Study of Orbital Fractures
### Advantages of AI Models for Orbital Fractures  
The application of AI in the context of orbital fractures has proven beneficial, with impacts in diagnostic and surgical settings. The models studied are demonstrating per  
formance equal to or slightly better than that of humans in terms of diagnostic accuracy, both in terms of location (orbit floor vs. medial wall) and stage (acute vs. chronic) [14]. In addition, the rapid automatic and reproducible segmentation reduces the processing time from more than 20 min (for the manual one) to a few seconds. In addition, the high correlation with manual masks ensures reliability for 3D modeling and volumetric calculation [16; 17]. Even in complex cases characterized by bilateral orbital fractures, AI shows that it can be a useful and valuable tool. Indeed, these models would allow the automatic reconstruction of orbital walls, enabling custom implant design and preoperative simulation [19; 20]. Another aspect to be considered is the reduction in clinical burden and decision-making time, especially in emergency settings and in places without specialists or in low-resource locations, allowing a reduction in diagnosis time and the optimization of clinical workflows [15; 21].
## 4 High Damping Alloy Mounting Structure Test
### Sweep Test  
The swept test frequency range of the mounting structure is 0-1500 Hz with an excitation amplitude of 1 g. Extracting the mass block measurement point sweep response data. Comparing the carbon steel material and the high damping alloy material mounting structures sweep response data, the mass block measurement point results are shown in Figures 19 and 20. The comparison results of the vibration response are shown in Table 6.  
Figure 18: The position of measurement points of the installation structure.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Model Order**} & \multicolumn{2}{c}{**High Damping Alloy**} & \multicolumn{2}{c}{**Carbon Steel**} & \multirow{2}{*}{**Amplitude Reduction Rate**} \\ \cline{2-2} \cline{5-6}  & **Modal Frequency/Hz** & & & & \\ \hline
1 st order & 158 & 162.68 & 178 & 433.57 & 62.48\% \\
3 rd order & 531 & 67.99 & 592 & 186.91 & 63.62\% \\ \hline \end{tabular}
\end{table}
Table 5: The data comparison of sweep response comparison.  
According to the sweep results of the mass block measurement points in Table 6, the first three natural frequencies of the carbon steel material mounting structure are 146 Hz, 560 Hz, and 1065 Hz, and those of the high damping alloy material mounting structure are 139 Hz, 566 Hz, and 978 Hz. The natural frequency of the high damping alloy material mounting structure is slightly lower than the carbon steel material mounting structure. The first three vibration response amplitudes of high damping alloy material are lower, especially the amplitudes of the first and third orders. The degree of vibration reduction is relatively large, greater than 30%. The test results verify the reasonableness of the finite element calculation results, and the vibration reduction effect of high damping alloy material is better.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameters** & **Materials** & \begin{tabular}{c} **First** \\ **Order** \\ \end{tabular} & \
\begin{tabular}{c} **Second** \\ **Order** \\ \end{tabular} &
\begin{tabular}{c} **Third** \\ **Order** \\ \end{tabular} \\ \hline \multirow{2}{*}{Natural Frequency} & Carbon Steel & 146 Hz & 560 Hz & 1065 Hz \\  & High Damping Alloy & 139 Hz & 566 Hz & 978 Hz \\ \hline \multirow{2}{*}{Response Amplitude} & Carbon Steel & 5.168 g & 1.967 g & 4.308 g \\  & High Damping Alloy & 3.529 g & 1.768 g & 2.517 g \\ \hline \multicolumn{2}{c}{Reduction Rate} & \multicolumn{1}{c}{—} & 31.7\% & 10.1\% & 41.5\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: The comparison of the mass block measurement point results of the mounting structure.  
Figure 19: The waterfall of the mass block measurement points of the Q235 mounting structure.
# Marginal Contribution-Based Distributed Subchannel Allocation in Small Cell Networks
## 4 Distributed Algorithm for Subchannel Allocation  
In this section, we present a distributed algorithm used by each SBS for subchannel allocation of all links that it handles. In general, there may be a large number of links in a SCN, and the amount of subchannel sensing and information exchange can be overwhelming. Hence, we are looking to limit the amount of subchannel sensing and information exchange requirements. For subchannel allocation, the proposed MCBR algorithm makes decisions based on information obtained from sensing the subchannels where the number of new sensing subchannel is limited to one. Moreover, for the particular sensing subchannel, the algorithm requires information only from those other links that are currently allocated to that subchannel.
# Uniform Pooling for Graph Networks
## 4 Experiments
### Strengths and Possible Limits  
The proposed UGPool could learn hierarchical graph embeddings, which outperforms other node-based graph pooling methods while maintaining high efficiency in multiple graph classification tasks. Moreover, UGPool could be integrated with multiple graph convolution networks to effectively improve performance compared to no pooling. However, the proposed method also has possible limitations, i.e., the feature of graph edges is not used when calculating node scores and pooling nodes, which may not be conducive to processing graphs with rich edge information. Please note that the proposed UGPool method could achieve almost comparable performance to the state-of-the-art method (EdgePool) with a more efficient runtime. The principles of these two pooling methods are quite different. The EdgePool is an edge-based pooling method that coarsens the graph by contracting edges and merging connected nodes. In contrast, the UGPool is a node-based pooling method that coarsens the graph by assigning a score to each node and uniformly pooling the adjacent nodes in the score-space. The runtime of EdgePool is related to the number of edges, and the runtime of our  
Figure 3: The sample image of fMRI and the illustrations of our experiments of graph classification.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset** & **If Pool** & **GATConv** & **GCNConv** & **SAGEConv** & **SGCConv** \\ \hline \multirow{2}{*}{ABIDE} & No Pooling & 61.26 \(\pm\) 1.45 & 60.90 \(\pm\) 0.84 & 62.30 \(\pm\) 0.88 & 61.48 \(\pm\) 1.03 \\  & UGPool & **64.58 \(\pm\) 1.18** & **63.45 \(\pm\) 1.42** & **64.77 \(\pm\) 1.37** & **65.03 \(\pm\) 1.52** \\ \hline \multirow{2}{*}{HCP} & No Pooling & 79.70 \(\pm\) 1.26 & 79.65 \(\pm\) 1.55 & 79.41 \(\pm\) 1.54 & 80.60 \(\pm\) 2.04 \\  & UGPool & **84.11 \(\pm\) 1.41** & **84.29 \(\pm\) 1.25** & **85.14 \(\pm\) 0.97** & **84.92 \(\pm\) 1.11** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Classification accuracy of brain connectivity with and without global UGPool architecture.  
UGPool is only related to the number of nodes. Therefore, the operating efficiency of our UGPool is significantly higher than that of EdgePool, as graph data generally has much more edges than nodes. This advantage will be more prominent in processing large-scale graphs with dense edges.
## 3 Materials and Methods
### Data Analysis  
As shown in Figure 7, the collected data were processed and analysed using the IBM Statistical Package for the Social Sciences 23.0 (SPSS). Firstly, hierarchical ([PERSON]'s method) and non-hierarchical (K-means) clustering procedures were utilized to identify target segments. Subsequently, an independent sample \(t\)-test was conducted to examine the internal consistency of segments and their differences in perceived benefits, future behavioural intention, and overall satisfaction. In cases where the assumption of variable homogeneity was not met ([PERSON]'s test; \(p>0.05\)), [PERSON]'s \(t\)-test was accordingly reported. For normality, values of skewness and kurtosis were used. The values satisfied the cut-off level of \(\pm 2.58\)[70; 71]. There were no outliers in the data, as assessed by the inspection of a boxplot for values greater than 1.5 [70]. Perceived ease of use was assessed using four items (e.g., \"My interaction with the VR simulator was clear and understandable\") and demonstrated a [PERSON]'s alpha of 0.734. The perceived benefits of VR learning were evaluated through six items (e.g., \"Using the VR simulator facilitates the comprehension of my previously acquired knowledge\") and yielded a [PERSON]'s alpha of 0.910. Behavioural intention was gauged using three items (e.g., \"I would study more if I had access to VR simulations in my field\") and showed a [PERSON]'s alpha of 0.852.
# Multi-Dimensional Sparse-Coded Ambient Backscatter Communication for Massive IoT Networks
## 4 Two-Stage Detection
### Stage-2: Utilizing Sparse Code Structure
Hence, the updated message from FN \(\tilde{k}\) to VN \(n\) can be calculated by  
\[\text{I}_{\tilde{k}\to n}(\bar{m}_{n})=\text{max}_{\bar{m}_{i},i\in \mathcal{I}_{\tilde{k}}\setminus\{n\}}\left(\bar{\text{I}}_{\tilde{k}\to n}( \{\bar{m}_{i}\}_{i\in\mathcal{I}_{\tilde{k}}})\right),\quad\bar{m}_{n}=1, \cdots,\tilde{M}. \tag{41}\]  
It is noted that FNs send the compressed messages of size \(\tilde{M}\leq M\) by the information projection for reducing the computational complexity of MPA. On the other hand, VNs receive the messages ofsize \(\tilde{M}\) from FNs, expand the messages to size \(M\), and send the updated messages to FNs which are expressed by  
\[\bar{\mathbf{z}} =[\bar{z}_{1},\cdots,\bar{z}_{\tilde{K}}]^{T}=[\mathbf{z}_{1}^{T},\cdots,\mathbf{z}_{K}^{T}]^{T}\in\mathbb{C}^{\tilde{K}\times 1}, \tag{33}\]0  
where \(\mathcal{J}_{n}=\left\{\tilde{k}|a_{n}\tilde{g}_{\tilde{k},n}>0\right\}\) and \(\mathrm{I}_{\tilde{k}\to n}(m_{n}),\ m_{n}=1,\cdots,M\) denotes the expanded information which can be obtained by  
* **Reflection Coefficient Expansion:** \[\bar{\mathbf{z}} =[\bar{z}_{1},\cdots,\bar{z}_{\tilde{K}}]^{T}=[\mathbf{z}_{1}^{T},\cdots,\mathbf{z}_{K}^{T}]^{T}\in\mathbb{C}^{\tilde{K}\times 1}, \tag{33}\]1 (43)  
After \(N_{I}\) iterations, the log-likelihood ratio (LLR) for the \(m^{\prime}\)-th most significant bit (MSB) where \(m^{\prime}=1,\cdots,V\) for \(\mathrm{tag}\) n can be calculated as [44]  
\[\bar{\mathbf{z}} =[\bar{z}_{1},\cdots,\bar{z}_{\tilde{K}}]^{T}=[\mathbf{z}_{1}^{T},\cdots,\mathbf{z}_{K}^{T}]^{T}\in\mathbb{C}^{\tilde{K}\times 1}, \tag{33}\]2 \[\bar{\mathbf{z}} =[\bar{z}_{1},\cdots,\bar{z}_{\tilde{K}}]^{T}=[\mathbf{z}_{1}^{T},\cdots,\mathbf{z}_{K}^{T}]^{T}\in\mathbb{C}^{\tilde{K}\times 1}, \tag{33}\]3  
Finally, the \(M\)-ary backscatter data can be detected as  
\[\hat{d}_{n,m^{\prime}}= \begin{cases}1,&\quad\text{if }\Theta_{n,m^{\prime}} \leq 0\\ 0,&\quad\text{if }\Theta_{n,m^{\prime}}>0\end{cases} ,\quad\text{for }a_{n}=1. \tag{46}\]  
As we previously discussed, when the dimension of the signal space is sufficiently large, the MSC-AmBC system can enjoy long-range and high-rate communications, connecting massive low-power IoT devices to the Internet.