esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey on Hardware Accelerators for Large Language Models
## 5 ASIC Accelerators
### [PERSON]  
In 2021, [PERSON] et al. presented another novel approach for the acceleration of Transformer networks, called Sanger [29].  
[PERSON] accelerates the sparse attention models by combining dynamic sparsity patterns and reconfigurable architecture. The software part provides sparsity patterns, which can achieve high performance and a balanced workload. The architecture is designed with reconfigurability to support the dynamic characteristics of sparsity, which helps to improve the compression ratio.  
At the software level, [PERSON] proposes an algorithm to predict dynamic sparsity by computing a low-bit version of the attention matrix and zeroing out small attention weights through binary thresholding. To ensure load balance for efficient hardware implementation, [PERSON] encodes the resulting attention mask, which has unstructured sparsity, into multiple fine-grained structured blocks. The prediction and encoding processes are performed on the fly as [PERSON] dynamically determines the sparsity patterns on a per-sample basis.  
At the hardware level, [PERSON] proposes a score-stationary dataflow that unifies the computation of SDDMM and SpMM operations. Using this dataflow, [PERSON] keeps the sparse scores stationary in the processing elements until the computation is finished, which effectively avoids the decoding overhead.  
To enable greater flexibility in the sparsity patterns, [PERSON] proposes a reconfigurable systolic array based on this dataflow. The implementation was carried out in Chisel, which was then translated to Verilog RTL. The design targeted an ASIC using UMC 55 nm technology and was clocked at 500 MHz.  
Experiments on BERT show that [PERSON] can prune the model to a sparsity of 0.08-0.27 without losing accuracy, achieving speedups of 4.64\(\times\) compared to the V100 GPU, 22.7\(\times\) compared to the AMD Ryzen Threadripper 3970X CPU, and 2.39\(\times\) and 1.47\(\times\) compared to the state-of-the-art attention accelerators A3 and SpAtten, respectively
## 3 Results  
### Average Torso Volume and Surface Area  
The average torso surface area and volume of all participants when wearing each garment configuration are presented in Figure 2. Both surface area and volume increased as additional clothing ensemble layers were donned, with the only exception being the torso volume when donning the SCBA. This negligible difference in volume is likely due to the cinching of the waist and shoulder straps of the SCBA when worn overtop the turnout coat. The SCBA pack straps increased the overall torso surface area, but did not increase the overall torso volume, on average.  
One-way ANOVAs found significant differences (\(p<0.05\)) between garment configurations for both torso surface area and volume. Differences in torso surface area and volume were statistically significant for all garment configuration pairwise comparisons except for the CO when compared to BL and the TS when compared to the TS w/SCBA.  
The change in torso volume between the BL, TS, and TS w/SCBA, when compared to the CO, was calculated and compared. The greatest change in torso volume was observed between the CO and the TS (76%; \(p<0.01\)), followed by the TS with SCBA (74%; \(p<0.01\)), with the smallest difference observed for the BL (11%; \(p=0.35\)). When comparing the change in torso volume between TS configurations, the largest increase occurred when donning the TS overtop the BL (59%; \(p<0.01\)). Wearing the SCBA overtop the TS led to a reduction in torso volume but only by 1.2% (\(p=0.89\)). For the torso surface area, the greatest difference when compared to the CO was observed for the TS w/SCBA (64%;  
Figure 2: The (a) average torso surface area and (b) average torso volume of the measured participants for the compression (CO), base layer (BL), turnout suit (TS), and turnout suit with SCBA (TS w/SCBA) garment configurations. Error bars represent standard error.  
\(p<0.01\)), followed by the TS (50%; \(p<0.01\)), and the BL (5%; \(p=0.36\)). Similar to the torso volume, when comparing the change in torso surface area between TS configurations, the largest increase occurred when donning the TS over the BL (43%; \(p<0.01\)). The SCBA contributed to an additional 9% (\(p=0.15\)) increase in torso surface area when worn overtop the turnout coat, however, this was not found to be statistically significant.
## 4 Results and Discussion
### Multi-Wavelength Light Metric Learning
As shown in Table 2, the calculated differences \(\beta_{SNE}\), \(\beta_{diff}\), and \(\beta_{\text{fact}}\) of MData 3 are 8.542, 8.2606, and 8.861; all these values are larger than those of GData 3, with calculated difference \(\beta_{\text{fact}}\) of MData showing the largest improvement of 0.2524. However, the calculated similarities \(\alpha_{SNE}\), \(\alpha_{diff}\), and \(\alpha_{\text{fact}}\) of MData 3 show a similar improvement in contrast those of GData 3. When \(\Delta_{1}\) denotes the difference between the calculated \(\alpha_{\text{MData3}}\) and \(\alpha_{\text{GData3}}\) (\(\Delta_{1}\)= \(\alpha_{\text{MData3}}-\alpha_{\text{GData3}}\)) and \(\Delta_{2}\) denotes the difference between the calculated \(\beta_{\text{MData3}}\) and \(\beta_{\text{GData3}}\) (\(\Delta_{2}\)= \(\beta_{\text{MData3}}-\beta_{\text{GData3}}\)), then the improvement trend between \(\Delta_{1}\) and \(\Delta_{2}\) can be described as \(\Delta=\left|\Delta_{1}-\Delta_{2}\right|.\) As can be seen in Table 2, the calculated \(\Delta_{fact}\) shows the largest value of 0.0004 in comparison with the calculated \(\Delta_{SNE}\) and \(\Delta_{diff}\). This indicates that the improvement \(\beta\) of MData 3 made by the factor analysis algorithm can effectively compensate for the weakness of a larger \(\alpha\). All these improvements indicate that our multilevel metric learning module, constructed using the factor analysis algorithm, is quite effective in utilizing multi-wavelength light information and building a 3D multi-wavelength light metric space. The calculation results of other algorithms are detailed in Table S2.
# A Survey on Intrusion Detection Systems for Fog and Cloud Computing
## 4 Intrusion Detection and Prevention Systems
### Statistics-Based Techniques  
Statistics-based intrusion detection (SBID), presented in [32], is a method that applies pattern-matching techniques to identify known attacks. It considers the standard patterns in network traffic and flags them when they deviate from the standard statistical parameters (e.g., average, norm, mode, and standard deviation). Statistics-based intrusion detection systems calculate a statistical distribution for the normal behavior profile and identifies low-probability events as potential threats. SBIDSs can operate according to different methodologies: univariate and multivariate. A normal statistical profile is considered for each variable in the univariate, and each metric is considered individually for anomaly detection. The multivariate can consider multiple variables and their correlation and generally achieve a better detection rate.
## 3 Results and Discussion
### Master Curves Result Analysis
#### 3.1.2 Master Curves after Aging  
Figures 2 and 3 show the evolution of G* master curves and \(\delta\) master curves of HCRMB with different amounts of sulfur along with aging. As described in Figures 2 and 3, the main G* master curves for 70% and HCRMB with different amounts of sulfur show a tendency to increase with aging, while the \(\delta\) master curves for 70% and HCRMB with different amounts of sulfur change after aging in the opposite pattern to the G* master curves in Figure 2, showing a decreasing trend, which indicates that the elasticity of 70% and HCRMB with different amounts of sulfur increases with aging, due to the hardening of the bitumen.  
Figure 1: Master curves of G* and \(\delta\) of HCRMB with different amounts of sulfur: (**a**) G* master curves and (**b**) \(\delta\) master curves.  
The evolution of the master curves of G\({}^{\prime}\) and G\({}^{\prime\prime}\) with aging is shown in Figure 4. The main curves of G\({}^{\prime}\) and G\({}^{\prime\prime}\) for HCRMB with different amounts of sulfur increase with increasing frequency (decreasing temperature). The decrease in the logarithmic coordinate corresponding to the intersection between the G\({}^{\prime}\) and G\({}^{\prime\prime}\) master curves after aging indicates that the bitumen hardens with aging. Table 2 shows the logarithmic coordinates corresponding to the intersection points of the HCRMB with different amounts of sulfur. The intersection coordinates of both 70# and HCRMB with different amounts of sulfur decrease from the unaged condition to the PAV condition, indicating that the viscous component of HCRMB with different amounts of sulfur decreases with aging.  
Figure 2: G\({}^{*}\) master curves evolution of HCRMB with different amounts of sulfur along with aging.  
Figure 3: Master curves evolution of HCRMB with different amounts of sulfur along with aging.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Sample Name**} & \multicolumn{2}{c}{**The Logarithm of Frequency Corresponding to the Intersection Point, Hz**} \\ \cline{2-4}  & **Virgin** & **RTFOT** & **PAV** \\ \hline
70\# & 2.079 & 1.680 & 1.112 \\
20 TB\_0.2 Sul & 2.289 & 1.834 & 1.569 \\
20 TB\_0.4 Sul & 2.210 & 1.766 & 1.348 \\
40 TB\_0.2 Sul & 3.587 & 3.508 & 3.045 \\
40 TB\_0.4 Sul & 3.598 & 3.246 & 2.641 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Intersection points of HCRMB with different amounts of sulfur.
## 4 Energy Conserving Solutions in Wireless Networking-Based IoT
### Energy Conserving Solutions for WWAN-Based IoT
#### 4.1.4 Solutions to Gateway and Device Synchronization Problem  
[PERSON] _et al._[56] considered a particular scenario where LTE and WiFi networks coexist in an M2M environment, and suggested device-gateway synchronization as an energy efficient solution. An IoT gateway-calculated Listen Interval (LI) is sent to IoT devices so that they can update their wake up intervals with respect to the gateway. As an industry development, [PERSON] _et al._[59] proposed a mechanism that supports M2M device and gateway synchronization, in which the gateway calculateslisten intervals for particular devices and transmits that information in a beacon format to these devices. Receiving the beacon-like message, the devices update their listen interval and accordingly remain in a sleep mode as per the updated interval to save energy. However, we notice that if the device is in sleep mode, and gateway receives some requests from some application, then it has to wait for the device to wake up. In this regard, this solution becomes not applicable for delay sensitive applications.
###### Abstract  
The present study aims to measure the number of cycles leading to fracture (NCF) of instruments in static and dynamic cyclic fatigue situations under body temperature in stainless steel double-curved canals. The framework was constructed to establish the movement of instruments occurring at a stable body temperature. A step motor, a holding system for an endodontic handpiece, created the movement in and out of the artificial canal of the file mounted on the handpiece. A total of 30 instruments of ProTaper Universal and ProTaper Next (Dentsply Sirona, Maillefer, Ballaigues, Switzerland) were divided into three groups of 10 per group. For group 1 (10 PTU F2), files were rotated in static cycles. For groups 2 (10 PTU F2) and 3 (10 PTN X2), files were rotated in dynamic cycles. Files were rotated using proprietary programs, and the times the files were rotated before fracture were recorded. Data were analyzed using survival probabilities and regression with life data. The ProTaper Next in the dynamic cycles had the largest NCF, and the ProTaper Universal in the static condition had the least. New modes of rotation, material, and design have affected the cyclic fatigue resistance of the instrument.  
cyclic fatigue; dynamic and static test; nickel-titanium; continuous rotation +
Footnote †: journal: Applied Sciences
## 5 Conclusions  
This study analyzes the correlation between occupant behavior, indoor set temperature variations based on occupant behavior characteristics, indoor comfort, and energy consumption in residential buildings with floor heating. The results show that an affordable FMCW radar-based sensor network can effectively detect occupant entry, occupancy duration, and spatial occupancy within predefined zones. This system can distinguish between general activities and sleep activities based on furniture and characteristics within the zone and can also track the duration of each activity. By estimating the insulation value of clothing during sleep, considering factors like beds and blankets in winter, and controlling the floor heating system accordingly, thermal comfort can be maintained while reducing the indoor set temperature. Resetting the indoor target temperature during sleep and resetting the indoor temperature during unoccupied periods can reduce peak load energy consumption by over 21% and total heating energy consumption over a three-month heating period by up to 34%. Future research aims to develop a versatile system applicable to various spaces and building types. This will involve enhancing the reliability and resolution of the sensor systems, integrating them with home automation systems, and conducting comprehensive performance evaluations in diverse environments. It may be possible to provide personalized comfortable environments and promote energy conservation.  
Conceptualization, J.W. and Y.T.C.; methodology, Y.H. and Y.T.C.; software, Y.H.; validation, J.W., Y.H. and Y.T.C.; formal analysis, Y.H.; investigation, [PERSON] and Y.H.; resources, [PERSON] and Y.T.C.; data curation, [PERSON] and Y.H.; writing--original draft preparation, J.W.; writing--review and editing, [PERSON] and Y.T.C.; visualization, [PERSON] and Y.H.; supervision, Y.T.C.; project administration, Y.T.C.; funding acquisition, Y.T.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Research Foundation of Korea (NRF) funded by the Korea government (MSIT) (No. RS-2024-00457571). This work was supported by the Korea Institute of Energy Technology Evaluation and Planning (KETEP) and the Ministry of Trade, Industry & Energy (MOTIE) of the Republic of Korea (No. RS-2021-KP002461).  
The data presented in this study are available upon request from the corresponding author due to privacy reasons.  
The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
# Thermoelastic Investigation of Carbon-Fiber-Reinforced Composites Using a Drop-Weight Impact Test
## 1 Introduction
### Non-Destructive Testing (NDT) of Composites--Thermographic Evaluation Method  
IR thermography offers ideal, cost-effective NDT solutions for a wide range of composites applications, including detection of impact damage, delamination, water entrapment, inclusions, density variations, and evaluation of the adhesive bonds [3; 4; 5]. This technique, which is fast and contact-free, offers wide-area coverage of flat or curved parts. The principle of infrared thermographic NDT systems is based on the detection of infrared energy emitted from an object, and detection devices range from a simple heat gun/IR camera combination to advanced systems capable of defect measurement and advanced materials characterization [6; 7].  
The IR thermographic method measures temperature changes on the surface of materials resulting from stress-generated or externally applied thermal fields. Thermography can be used to study thermal variations. Excitation sources can be applied externally using thermal or mechanical sources to generate coupled stress and thermal fields. These excitation sources can be in the form of mechanical loads, direct heat, and ultrasonic stress waves. [8; 9; 10; 11].
# A GNN-Based False Data Detection Scheme for Smart Grids
## 4 The GNN-Based False Data Detection Scheme
### STGNN for Information Network Prediction  
As shown in Figure 3, the information network prediction unit was devised to simultaneously obtain the high correlation in space and time dimensions of several neighboring data points of the information network obtained from sensor node measurements to make accurate predictions of the state of the information network at future moments. It contained a graph convolution gated recursive unit (GCGRU) [26] layer and a fully connected layer.  
As shown in Algorithm 1, the research represented the network composed of sensor measurement data as an undirected graph. To better model spatial and temporal dependencies, the CCGRU used the graph convolution operation shown in Equation (2) to replace matrix multiplications in the gated recurrent unit (GRU) [27]. The GCGRU can be defined as follows:  
\[\begin{split}& r^{(t)}=\sigma\Big{(}ReLU(\tilde{A}[X^{(t)},\ H^{(t-1)}]W_{r})\Big{)}\\ & u^{(t)}=\sigma\Big{(}ReLU(\tilde{A}[X^{(t)},\ H^{(t-1)}]W_{u}) \Big{)}\\ & C^{(t)}=tanh\Big{(}ReLU(\tilde{A}[X^{(t)},\ (r^{(t)}H^{(t-1)})]W_{C})\Big{)}\\ & H^{(t)}=u^{(t)}\ H^{(t-1)}+(1-u^{(t)})\ C^{(t)}\end{split} \tag{2}\]  
Here, \(\sigma\) denotes the nonlinear activation function. \(X^{(t)}\) and \(H^{(t)}\) signify the input and output at time \(t\), separately. \(r^{(t)}\) and \(u^{(t)}\) signify the reset gate and update gate at time \(t\), separately.  
After obtaining the hidden variable \(H^{(t)}\) of the information network at time \(t\), we used Multi-Layer Perceptron (MLP) to forecast the network state at moment \(t+1\):  
\[\widetilde{X}^{(t+1)}=\sigma(WH^{(t)}+b) \tag{3}\]The prediction module prefers to minimize the negative log-likelihood of the prediction, given the predicted value \(\widetilde{X}^{(t+1)}\). The loss function of this module is  
\[\mathcal{L}_{p}=||\widetilde{X}^{(t+1)}-X^{(t+1)}||_{2} \tag{4}\]  
We minimized the loss function \(\mathcal{L}_{p}\), which contributed to our predicting the next information network state.  
```
0: A attributed graph \(G=(A,X)\), time slices \(\mathcal{T}\) Output: The network state at moment \(t+1\) for\(r^{(t)}\), \(u^{(t)}\), \(C^{(t)}\)do \(H^{(t)}=u^{(t)}\)\(H^{(t-1)}+(1-u^{(t)})\)\(C^{(t)}\) for\(H^{(t)}\)do \(\widetilde{X}^{(t+1)}=\sigma(WH^{(t)}+b)\) endfor endfor return given the predicted value \(\widetilde{X}^{(t+1)}\)
```  
**Algorithm 1** STGNN for Information Network Prediction
# A Multilayer Data Processing and Aggregating Fog-Based Framework for Latency-Sensitive IoT Services
## 5 Experiment and Evaluation
### Experiment Setup  
To set up our experiment, the network limitation regarding the number of connections that a fog server can receive is evaluated. The findings of this experiment will be considered as the guideline for the next experiments. Clients are simulated by iFogSim [25] which is considered as one of the most popular fog computing simulation tools to simulate fog environments and IoT data. It was employed to create connections and send requests to the fog server. A response will be sent back immediately from the fog server to the clients after receiving the requests. The time between sending/receiving requests/responses is called the response time. Different numbers of clients are used to measure the response time. Figure 10 shows the average response time in each circumstance. The vertical axis represents time in milliseconds and the horizontal axis represents the round which indicates the average response time of 100 times. Results indicate that the response time remains stable with 400 to 2000 clients' connections and it starts to oscillate with 2400 clients' connections; as the iFogSim has some limitations regarding the number of network links and limited network usage, it supports only tree-like topologies. Therefore, 2000 clients were used to perform the next experiments.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Host Machine** & **Guest Machine** & **Amazon Web Service—EC2** \\ \hline OS & VMware & CentOS 7 & CentOS 7 \\ \hline CPU & I7-6700 core 4 & Core 2 & Core 2 \\ \hline Memory & 40 GB & 4 GB & 4 GB \\ \hline HDD & 2 TB & 50 GB & 30 GB \\ \hline Location & - & - & Mumbai \\ \hline \hline \end{tabular}
\end{table}
Table 1: The specifications of the cloud server, the host and the guest machines.  
Figure 10: Network limitation.
## 4 Proposed Hybrid GASAVNS Algorithm  
In this paper, a hybrid algorithm is used which includes the GA, SA, and the variable neighborhood search (VNS) algorithm. The SA is inserted into the procedure of GA for local searching. The GA can fall into the local optimal solution, which is why the proposed hybrid algorithm uses the local search algorithm, i.e., SA, to avoid being trapped in the local minima and accelerate the convergence speed. To diversify the solution, the VNS is used with SA with the aim to escape the local optimum and reach a global optimum. VNS works on the principle of using two or more neighborhood structures and systematically changing the neighborhood within the local search [35]. Figure 1 shows the workflow of the proposed hybrid algorithm. The following is a description of the GASA and VNS steps that are used in the proposed hybrid algorithm. The '1' in Figure 1 flowchart represents the link between the scheduling and rescheduling methods allowing for retrieval of scheduling data for use in the rescheduling process.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Jobs**} & \multirow{2}{*}{**Operations**} & \multicolumn{3}{c}{**Processing Time**} \\  & & \(M_{1}\) & \(M_{2}\) & \(M_{3}\) \\ \hline \multirow{3}{*}{\(J_{1}\)} & \(O_{11}\) & 7 & 2 & 3 \\  & \(O_{12}\) & 3 & 4 & 5 \\  & \(O_{13}\) & 3 & - & 6 \\ \hline \multirow{3}{*}{\(J_{2}\)} & \(O_{21}\) & 2 & - & 5 \\  & \(O_{22}\) & 4 & 4 & - \\  & \(O_{23}\) & 2 & - & 5 \\ \hline \multirow{3}{*}{\(J_{3}\)} & \(O_{31}\) & 4 & 2 & 3 \\  & \(O_{32}\) & 3 & - & - \\ \cline{1-1}  & \(O_{33}\) & 2 & 8 & 5 \\ \hline \hline \end{tabular} \({}^{a}\)-\({}^{a}\) shows that the machine cannot proceed with the corresponding operation.  
\end{table}
Table 2: Processing information of FJSP with multi-purpose machine.