esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Mechanical and Fracturing Behaviours of Samples Containing Pre-Existing Flaws  
As stated before, the crack patterns between moulded and 3 DP gypsum samples should be compared before the physical simulation to confirm their feasibility. [PERSON] et al. (2016) and [PERSON] et al. (2017) used the single-flawed 3 DP sample by FDM and powder-ink binders (PIB) methods to discuss the feasibility of 3 DP samples in simulating rock failure [59; 60]. The present study uses more complicated samples with three double-flaw system types (Figure 5) to test the coalescence behaviour. The flaw length \(2a\) is 12 mm, and the bridge length \(2b\) linking the two inner tips is 20 mm. The inclination angle is 45\({}^{\circ}\), but with different bridging angles (\(\beta\)). The cracking processes of the specimens belonging to these three double-flaw systems are illustrated in Figure 6. The 3 DP specimens show the advantages of repeatability and data reliability in the physical simulation of the cracking  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sintering** & & & \\
**Temperature (°C)** & **Dry Density (g/cm\({}^{3}\))** & **UCS (MPa)** & **Failure Strain** \\ \hline
20 & 1.29 & 2.7 & 4.3\% \\
60 & 1.26 & 5.2 & 3.0\% \\
100 & 1.25 & 8.6 & 2.8\% \\
140 & 1.22 & 4.4 & 3.3\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Dry density, UCS, and failure strain of the cylindrical 3 DP specimens with different sintering temperatures.  
Figure 4: (**a**) Stress–strain curves and (**b**) peak stresses of the 3 DP PP samples with different sintering temperatures (solid point—average value of each group), and (**c**) their failure modes.  
1. As shown in Table 3, the failure strains of the pre-flawed specimens are significantly higher than those of the intact moulded gypsum (0.1%\(\sim\)0.2%). The double-flawed specimens made from moulded gypsum have a failure strain ranging from 0.7-0.8%, while those made from 3 DP gypsum are usually higher than 1%. The higher failure strain of the 3 DP specimen is thought to be attributed to the plastic deformation brought on by the binder used in the 3 DP process.
2. The crack patterns of these two types of specimens are similar regardless of flaw geometries. The coalescences appear slightly before the peak stress, and their development induces specimen failure. The result shows the feasibility of the 3 DP gypsum in the physical modelling of the rock failure process.
3. The crack initiation and propagation processes of 3 DP samples are generally consistent with the findings based on the moulded gypsum or other brittle rocks in the literature [51, 61, 62, 63, 64, 65, 66, 67, 68].
4. The tensile wing cracks, apparently initiating before the anti-wing and coplanar cracks, are more likely to develop in the moulded gypsum specimens than the 3 DP samples, which indicates the higher brittleness of the moulded gypsum.
5. Another obvious difference is that the crack opening of the moulded gypsum is slightly narrower than that of the 3 DP gypsum, also indicating the difference in brittleness.
## 5 Results and Discussion
### Stainless Steel 410  
A sheet of stainless steel 410 (410 SS) was tested as a representative ferrous alloy. Although it was noted as an annealed sheet, its hardness was equal to that of a hardened and 650 \({}^{\circ}\)C-tempered condition (G5a in Table 1). Because of its low bulk-wave attenuation coefficients, a nominally annealed rod (G5b) was used for damping measurements. Tests resulted in very low values, i.e., \(\alpha_{\mathrm{L}}\) = 0.97 and \(\alpha_{\mathrm{T}}\) = 2.6 dB/m/MHz, respectively. As expected, \(\alpha\)-S and \(\alpha\)-A values were low, as plotted in Figure 11a,b. In Figure 11a, \(\alpha\)-S\({}_{\mathrm{o}}\) values were in the same range as the calculated values, similar to the case of the Al-7475 sheet. In order to reduce data scattering effects, five wavelet frequency steps were averaged, giving a frequency step of 38.1 kHz. Above 900 kHz, observed \(\alpha\)-S values decreased and seemed to follow \(\alpha\)-S\({}_{\mathrm{1}}\) curve, plotted in the blue dotted curve. For asymmetric modes, no attenuation data were obtained below 570 kHz, indicating that \(\alpha\)-A\({}_{\mathrm{o}}\) was too low to measure using the present test method. At higher frequencies, a dozen attenuation data points were obtained. The frequency range of 570 to 1000 kHz is where A\({}_{\mathrm{1}}\) mode is expected, but deviation was large below 800 kHz, making it difficult to associate the observed data to the A\({}_{\mathrm{1}}\) mode at present.  
Figure 11: (a) \(\alpha\)-S\({}_{\mathrm{o}}\) values for 410 SS sheet of 3.2-mm thickness plotted in blue dots with Olympus V101 as Tx and KRN-BB-PC as Rx. Calculated \(\alpha\)-S\({}_{\mathrm{o}}\) (solid blue curve), \(\alpha\)-S\({}_{\mathrm{1}}\) (blue dotted), \(\alpha_{\mathrm{L}}\) and \(\alpha_{\mathrm{T}}\) (blue and red dashed lines) are also plotted. (b) \(\alpha\)-A\({}_{\mathrm{o}}\) values for 410 SS sheet of 3.2-mm thickness plotted in red dot symbols, using short-pulse excited NDT C16 Tx at 60\({}^{\circ}\) incident angle and KRN-BB-PC Rx. Calculated \(\alpha\)-A\({}_{\mathrm{o}}\) (solid red curve), \(\alpha\)-A\({}_{\mathrm{1}}\) (red dotted), \(\alpha_{\mathrm{L}}\) and \(\alpha_{\mathrm{T}}\) (blue and red dashed lines) are also plotted.  
From these 410 SS test data and those of Al-7475 above, it is evident that the present test method requires much larger sample length and width than those listed in Table 1 when \(\alpha_{\rm L}\) and \(\alpha_{\rm T}\) are low and especially when thickness is large.
# Siamese-SAM: Remote Sensing Image Change Detection with Siamese Structure Segment Anything Model
## 3 Methods
### Proposed Approach
#### 3.1.3 Differential Information Enhancement Module  
The DIEM enhances differential features by capturing and emphasizing differences in both horizontal and vertical directions of the input feature maps. This is achieved through a Coordinate Attention Mechanism [42], as shown in Figure 5.  
The module splits the input features into horizontal and vertical components using adaptive average pooling. These components are concatenated, processed through shared convolution and activation layers, and then split back into horizontal and vertical representations. Attention weights are generated for each direction and applied to the original  
Figure 4: The structures of three attention mechanisms, where (**a**) is Channel Attention, (**b**) is Pixel Attention, and (**c**) is Simple Pixel Attention.  
Figure 5: The structure of differential information enhancement module.  
Figure 3: The structure of global information enhancement module.  
features, refining them by focusing on significant spatial differences. This mechanism effectively highlights meaningful changes, ensuring better feature representation for change detection tasks.  
This coordinate-based attention mechanism not only enhances the discriminative capability of the module but also ensures efficient computation. By focusing on directional differences and dynamically adjusting feature responses, the DIEM effectively highlights meaningful changes between bitemporal images, making it a critical component in the proposed network.
# Microbes and Parameters Influencing Dark Fermentation for Hydrogen Production
## 4 Bacteria Used for Hydrogen Production During Dark Fermentation
### Clostridium
#### 4.1.3 Clostridium pasteurianum
Some of the studies have incorporated nanoparticles for bacterial immobilization, increasing the hydrogen-producing ability of this species [106]. Strain DSM525 provided the highest H\({}_{2}\) yield of 3.55 mol H\({}_{2}\)/mol of glucose as a substrate under batch mode at 37 \({}^{\circ}\)C.
## 4 Performance Evaluation and Comparison
### Performance of Proposed MOGA-SVM Using \(K_{cs}\)  
The performance evaluation of the proposed MOGA-SVM using \(K_{CS}\) adopts 10-fold cross validation. Randomly divide 5600 WPS samples into 10 equal-sized subsets; with each set containing 560 (10%) samples with Class 0: 180 samples, Class 1: 63 samples, Class 2: 97 samples, Class 3: 138 samples, and Class 4: 82 samples. In each fold of validation, 90% of datasets (nine subsets) from each class serves as training dataset and 10% of the remaining subset serves as the testing datasets. This process completes one-fold of operations. Subsequently, another set is chosen for validation and the remaining nine subsets are used for training. It is noted that this chosen validation set must be different from the validation sets that were selected in the previous folds of operations. The process is repeated until all of the 10 subsets have been validation.  
Applying 10-fold cross validation, the proposed MOGA-SVM using KCS achieves average \(S_{e}\), \(S_{p}\), and Acc of 92%, 91.2%, and 91.6%, respectively.
## 5 Conclusions  
To build a robust predictive maintenance system for fused deposition modeling (FDM) in additive manufacturing, the authors proposed TCN-TS-SW to accurately provide a nozzle thermal prediction for the monitoring program. The simulation results proved that the proposed two-stage sliding window strategy elevates the prediction robustness and minimizes the training loss against the generic temporal neural network. In addition to that, TCN-TS-SW outperformed the other machine learning algorithms namely multi look-back LSTM, GRU, and LSTM in terms of accuracy and training loss. The statistical performance evaluation for regression; root-mean-squared error (RMSE), mean absolute error (MAE), and \(R^{2}\) is used to display the robustness of TCN-TS-SW in providing an accurate predicted values compared to other models. The effect of modifying the window length is also investigated and through the conducted experiment, \(\Delta=400\) displays more accurate prediction compared to window length \(\Delta=300\) in terms of RMSE, and MAE. However, both scenario received a negative \(R^{2}\) that implies a high variance between the predicted values versus the actual values hence, we conclude that the proposed scheme needs an additional pre-processing procedure for the raw input data as additional step to improve the \(R^{2}\) score and we would add a constant term to further stabilize the equation.  
In the future works of this study, the authors will consider other anomaly scenarios and develop counteractions to prevent it. Further experiment should be conducted to recognize the effectiveness of the proposed system by subjecting the fabricated product in a tensile stress test. It is to supplement the claims of reducing the temperature related degradation experienced by the fabricated Polylactic acid (PLA). Lastly, the researchers will improve the accuracy and real-timeness of the closed-loop predictive maintenance system for fused deposition modelling 3D printer by utilizing the features of 5G plus network on this technology.  
Methodology, software, validation, formal analysis, data curation, and writing--original draft preparation, D.J.S.A.; conceptualization, J.-M.L. and D.-S.K.; writing--review and editing, D.J.S.A., J.-M.L. and D.-S.K. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Priority Research Centers Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (MEST) (2018R1A6A1A03024003) and is supported by the MSIT (Ministry of Science and ICT), Korea, under the Grand Information Technology Research Center support program (IITP-2021-2020-0-01612) supervised by the IITP (Institute for Information & communications Technology Planning & Evaluation).  
Not applicable.  
The authors declare no conflict of interest.
# A Novel Self-Forming Virtual Sub-Nets Based Cross-Layer MAC Protocol for Multihop Tactical Network
## 3 Proposed VSCL-MAC Architecture
### One-Time Neighborhood Discovery  
As soon as the network starts functioning in tactical communication, each SDR sends NBR_DSCV packet to all its one-hop neighbor(s) in its slot. The format of NBR_DSCV packet is shown in Figure 2, where transmitter ID is the MAC ID of sending node, i.e., for a network of \(N\) nodes, \(log_{2}(N)\) bits are required for the node's MAC ID. The four-bit message type field identifies message format, i.e., NBR_DSCV, REQ, REQ_ACK, BRDSCT, combined_REQ or combined_ACK. The bits value 0000 indicates the NBR_DSCV packet, and NBR_MAP field gives hop count information of each neighbor, i.e., two-bit code for each node representing \(2^{2}=4\) possible combinations, with 01, 10, and 11 defining 1, 2, and 3-hop, respectively. The bit code 00 represents that the node is not in a 1-hop or 2-hop neighborhood; usually, it represents itself.  
The transmission of NBR_DSCV packets begins with the lowest node ID, and in the first \(N\) slots, each SDR broadcasts the NBR_DSCV packet. Each shares its NBR_MAP with minimum hop information of all its neighbor(s), including itself. Upon receiving the NBR_DSCV packet, each node extracts the bit code (hop count) of other nodes and inserts the entry (hop count + 1) in its neighbor table (NBR_table). In NBR_table, each node \(N_{i}\) saves the node ID of its 1-hop neighbors in the first column of the table, each in a separate row, and keeps 2, 3, and 4-hop neighbor(s) ID in the corresponding column and in the row of 1-hop neighbor from which it gets the entry, as depicted in Table 1.  
Figure 1: Time division multiple access (TDMA) frame cycles in proposed virtual sub-nets based cross-layer medium access control (VSCL-MAC) design.  
Figure 2: Format of neighbor discovery packet.  
The process of neighbor discovery and the built-up NBR_table are described in Figure 3 it repeats for the next \(N\) slots, and radios that cannot get information in the first receive it in this round.  
The proposed algorithm can maintain more than a 3-hop neighbor's information, depending on the number of bits used to send hop information of neighbors. Tactical networks are congested; more than 4-hop is considered to be out of bounds for radios.
## 4 Conclusions  
In this work, we developed a new FIR DF architecture based on a modified MAC unit architecture called TMAC, with the aim of increasing the digital filtering performance. Theoretical analysis of digital filter parameters was performed using the abstract \"unit-gate\" model. According to the theoretical analysis, FIR DF implementation based on TMAC units can theoretically reduce filtering time by 22.39-29.86% and reduce hardware costs by 2.41-6.32%. The results of the hardware simulation on a FPGA show that the use of TMAC units increased the FIR DF performance up to 10.89% but required more hardware costs compared to traditional FIR DFs using traditional MAC units. The results of this research can be used in the digital signal processing theory and for solving practical problems such as machine learning, multimedia processing, noise reduction and many others.  
In future works, we plan to study the application of the proposed approach for discrete wavelet transform of medical tomograms. This type of medical image usually uses 8-, 12- or 16-bit data representation, and the wavelet filter banks have various orders. Thus, many FIR configurations discussed in this article may be applied in practice to process medical tomograms using a discrete wavelet transform.  
Conceptualization, P.L.; methodology, M.V.; software, M.V.; validation, N.N.; formal analysis, G.V.; investigation, G.V.; resources, N.N.; data curation, N.N.; writing--original draft preparation, G.V.; writing--review and editing, M.V.; visualization, G.V.; supervision, P.L.; project administration, P.L.; funding acquisition, P.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors are grateful to the North Caucasus Federal University for supporting the competition of scientific groups and individual scientists of the North Caucasus Federal University.  
The authors declare no conflict of interest.
## 4 Traffic and Risk Management  
When operating in real world environments, reliability and safety requirements have to be satisfied for the UAV and the operation to ensure mitigation of risks wrt third parties: other manned or unmanned platforms in the airspace, people at ground, etc.  
The work in [14] proposes a method to design reliable UAV architectures accounting for modeling of emergency situations such as collision risk avoidance behaviors.  
Regarding Unmanned aerial system Traffic Management (UTM), an open source software architecture is presented in [15] to track aerial operations an monitor the airspace during in real time. Furthermore, the system is capable of in-flight emergency management and tactical deconfliction. For low-altitude UTM, a 3D flight volumization algorithm along with path planning solutions is presented in [16] for definition and management of geofenced airspaces that would contain compatible UAV trajectories and ensure avoidance of no-fly zones.  
To deal with risk wrt third parties at ground, a method based on Importance Sampling is proposed in [17] to generate reliable ground impact footprints that contains a high percentile of the drone impact points.
## 3 Materials and Methods  
### Sample Collection  
Water samples were collected from the observation wells during summer using downhole pumps in 1.5-L sterile plastic containers. For rRNA 16S analysis, samples were conserved with ethanol at a ratio of 1:1. To isolate clean samples and enrichment cultures and for laboratory model experiments, groundwater was collected in liquid full containers, which were then sealed without a gas phase. The samples were stored at a temperature of 5-7 \({}^{\circ}\)C before they were delivered.  
Filtration was carried out under field conditions in an anaerobic box in a nitrogen atmosphere immediately after sampling. For filtration, 5-mL syringes with nozzles containing membranes (Closed Joint-Stock Company \"Vladipor\") with pore sizes of 4.5, 2.4, 1.2, 0.45, and 0.05 \(\upmu\)m were used. After each filtration step, the filtrate composition was analysed by inductively coupled plasma-mass spectrometry (ICP-MS).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sample** & **1 (Backg.)** & **2 (Inter.)** & **3 (Max)** \\ \hline pH & 7.01 & 6.80 & 6.97 \\ Eh, mV & \(-\)190 & \(-\)28 & 90 \\ TDS, g/L & 0.47 & 3.83 & 14.91 \\ Cl\({}^{-}\), mg/L & 11 & 590 & 3200 \\ HCO3\({}^{-}\), mg/L & 549 & 161 & 180 \\ NO3\({}^{-}\), mg/L & 12.2 & 1124 & 6169 \\ SO4\({}^{2}\) & 30 & 1769 & 470 \\ Ca, mg/L & 123 & 56 & 1436 \\ Mg, mg/L & 33 & 114 & 65 \\ Na, mg/L & 0.24 & 685 & 1249 \\ Mn, mg/L & 0.40 & 2.3 & 1.3 \\ Fe, mg/L & 0.01 & 10.7 & \textless{}0.01 \\ Cu, mg/L & 0.20 & 3.2 & 0.6 \\ Zn, mg/L & 0.14 & 0.31 & 0.2 \\ U, mg/L & 0.002 & 0.0008 & 1.6 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Chemical composition, total dissolved solids (TDS), and Eh–pH of the study samples.
## 4 Algorithm Tuning and Experimental Evaluation
### Complexity Analysis of the Proposed Algorithm  
The procedure of computing the average rating value for each item, given by all users, is a procedure that can be easily executed offline (while loading the ratings from the rDB). In case the aforementioned procedure is selected to be executed online, its complexity is O(r), where r is the number of all user ratings in the rating database. When a new rating is added to the database, the complexity to update the average is O(1), since the new average  
Figure 5: MAE reduction achieved by the inclusion of proposed algorithm to the CF\({}_{\text{EPC}}\) algorithm, when using the CS user similarity metric.  
can be directly computed on the basis of the current one and the number of ratings for the item, as shown in Equation (5):  
\[newMean(i)=\frac{currentMean(i).currentNumRatings(i)+newRating(i)}{currentNumRatings(i)+1}, \tag{5}\]  
Regarding space complexity, the overhead introduced by the procedure is 1 real number per item (its average rating) and hence negligible.  
The procedure of finding the number of black sheep ratings for each pair of NNs (to compute the _bsf_ factor for this pair of users) has a complexity of O(#NNs * #commonRatings). According to [74; 75], the top-K NNs are retained and the maximum number of NNs typically considered ranges from 20 to 60. The average number of common ratings for NNs pairs varies with the dataset, and the settings used to determine NNs; for instance, in [76] it is suggested that CF system implementors may opt to consider only NNs with at least 10 common ratings, to increase accuracy. In all cases, the computation of the _bsf_ factor considers the common ratings for each pair of NNs, and therefore its complexity is identical to the computation of the similarity of the same pair of users, which is an integral step of the CF procedure; therefore, the introduction of the computation of the _bsf_ factor does not affect the overall complexity of the algorithm. Notably, the computation of the _bsf_ factor need only be performed between a user and his/her NNs (whose number is typically bounded by the \(K\) parameter of the top-K NN selection step), yielding significantly lower execution time than the computation of pairwise user similarities, which must be performed for all user pairs. The complexity of the rating prediction phase is not altered, as compared to the typical CF algorithm listed in Equation (3), since only one additional multiplication per considered rating is introduced.  
Regarding space complexity, the overhead introduced by the need to maintain the _bsf_ factor values is 1 number per each NN pair (the value of their black sheep factor) and can be easily accommodated in contemporary hardware.
## Nomenclature  
1D =one dimensional  
3D =three dimensional  
x =chi (AVO rotation angle)  
\(\sigma\) =sigma (smoothing factor for GRNN)  
AI =acoustic impedance  
AVA =amplitude versus angle  
AVAz =amplitude versus azimuthal  
AVO =amplitude versus offset  
CCUS =carbon capture utilization and storage  
CO\({}_{2}\) =carbon dioxide  
CSSI =constrained sparse spike inversion  
D =distance between sample in GRNN modeling  
E-W =east to west direction  
EEI =extended elastic impedance matrix  
EEI\({}_{\text{Po}}\) =posterior extended elastic impedance matrix  
EEI\({}_{\text{pr}}\) =prior extended elastic impedance matrix  
EI =elastic impedance  
EGR =enhanced gas recovery  
EOR =enhanced oil recovery  
GRNN =generalized regression neural network  
GI =gradient impedance  
GWC =Gas Water Contact  
MCMC =Markov Chain Monte Carlo  
NW-SE =northwest to southeast direction  
P =GRNN training input vectors  
P\({}^{\prime}\) =GRNN new input vector for testing or prediction  
P\({}^{\prime}\) =posterior EEI probability function
# Energy Reduction Multipath Routing Protocol for MANET Using Recoil Technique
## 4 Mathematical Support of Proposed Model for Results
### Lemma-2  
The packet loss percentage increases with node mobility in either case, i.e., reactive or proactive protocols, and can be expressed using Equations (24) and (25).  
\[M_{1}>M_{2},\ PL_{P}(M_{1})>PL_{P}(M_{2}) \tag{24}\]  
\[M_{1}>M_{2},\ PL_{R}(M_{1})>PL_{R}(M_{2}) \tag{25}\]  
**Proof.** As per lemma 1, it is stated that the increase in the mobility of that node results in the increase of route discovery process due to the frequent link failure. And link failure improves the chances of packet loss. It is concluded that packet loss percentage increases with mobility.  
\(M_{1}\) and \(M_{2}\) signify two different values of mobility. The derivatives \(PL_{P}^{\prime}(M)\geq 0\) and \(PL_{R}^{\prime}(M)\geq 0\) show the packet loss function increases with mobility.  
In the simulation result (Figure 13b), the slope of the tangent line would be the same as the slope of the equation of best fit line on the curve \(y=-1.64x+98\), which represents the derivative of packet delivery ratio (always inversely proportional to packet loss), whose slope is \(-1.64<0\). Hence, the slope of packet loss function would always be greater than 0. As per the result, it is concluded that the packet loss percentage function is monotonically increasing with the mobility of the node \(\square\).