esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
The 3D finite element analysis is carried out on the helical pile model to study the effect of uplift load on the lateral capacity of helical piles and the effect of lateral load on the tensile capacity of the helical piles. The modeling and numerical analysis was first compared for individual load on a helical pile with the analytical and experimental results and then the validated model is used to study combined loading behavior. From the combined loading analysis of the helical pile embedded in soft clay, the following conclusions are drawn:  
1. The proposed numerical model made a good prediction of the uplift and lateral capacities of the helical pile and can be employed for field application considering some correlating factors related to in situ soil conditions. Compared with the theoretical results, the predicted results have a maximum percentage variation of 5% and 8% in the case of uplift and lateral capacity, respectively. The results from laboratory tests on remolded clay beds varied within 10% and the proposed model is suitable for design purposes.
2. The uplift and lateral capacities increase with the increasing number of helical plates and embedment depth ratio. The reason behind this, as reported by previous literature, is that increasing the number of helical plates increases the bearing resistance and uplift resistance, which leads to an increase in capacity. In the case of lateral loading, their influence on the capacity values is greater. Furthermore, increasing the eccentricity of loading decreases the lateral capacity substantially.
3. The lateral capacity of the helical pile decreases when it is subjected to combined uplift loads. However, the magnitude of an applied uplift load has less effect on the decrement of capacity. According to the numerical results, increasing the number of helical plates appears to restore the lateral capacity. As a result, the inclusion of a helical plate should be seen as critical from a design standpoint.
4. The combined loading also increases the bending moment at the central core and this increment leads to a limitation in choosing pile geometry while designing helical piles to produce significant lateral resistance.
5. The effect on the uplift capacity of a helical pile due to lateral load is to increase the capacity by almost 6%. The observed percentage increase seems less effective at increasing the number of helical plates. From the failure contours of combined loading, it is observed that the soil mobilized near the pile head due to lateral movement of the pile, and that failure contours developed along with the cylindrical shear failure surface.  
The author [PERSON] contributed in carrying out the experimental investigation and numerical analysis. The corresponding author [PERSON] reviewed the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
## 1 Introduction
### Covering 1-faces
**Lemma 3**: _There exists a \(\lambda>0\) so that, if all protecting balls are smaller than \(\lambda\), C3.a and C3.b imply C3._  
Consider any two non-adjacent balls \(b=B(c,r)\) and \(b^{\prime}=B(c^{\prime},r^{\prime})\). If \(c\) and \(c^{\prime}\) are on different 1-faces, \(b\) and \(b^{\prime}\) do not intersect by C3.b. Then C3 is satisfied trivially. So, assume that \(c\) and \(c^{\prime}\) belong to a common 1-face \(\sigma\). We show that \(\mathrm{seg}_{\sigma}(b)\) and \(\mathrm{seg}_{\sigma}(b^{\prime})\) do not intersect for sufficiently small \(\lambda\). It follows from the differentiability of \(\sigma\) that there exists a \(\lambda>0\) so that any ball of size smaller than \(\lambda\) intersects \(\sigma\) in a single segment. Assuming that \(b\) and \(b^{\prime}\) have radii smaller than \(\lambda\), we have \(b\cap\sigma=\mathrm{seg}_{\sigma}(b)\) and \(b^{\prime}\cap\sigma=\mathrm{seg}_{\sigma}(b^{\prime})\). We claim that no point in \(\mathrm{seg}_{\sigma}(b^{\prime})\) can lie in \(\mathrm{seg}_{\sigma}(b)\) when \(\lambda\) is sufficiently small. If there were such a point, there would exists a ball \(b^{\prime\prime}\) with one of the following properties: (i) either \(b^{\prime\prime}\) is adjacent to \(b\) and its center \(c^{\prime\prime}\) lies in \(\mathrm{seg}_{\sigma}(b)\), or (ii) \(b^{\prime\prime}\) is adjacent to \(b^{\prime}\) and \(c^{\prime\prime}\) lies in \(\mathrm{seg}_{\sigma}(b^{\prime})\). Without loss of generality, assume that (i) holds since the other case can be argued exactly the same way. We know that the length \(d_{\sigma}(c,c^{\prime\prime})\) is at least \(\frac{7}{6}r\) by property C3.a. Making \(\lambda\) sufficiently small, \(d(c,c^{\prime\prime})\) can be set arbitrarily close to \(d_{\sigma}(c,c^{\prime\prime})\) which would imply that \(d_{\sigma}(c,c^{\prime\prime})\) can be made arbitrarily close to \(r\). This would contradict that \(d_{\sigma}(c,c^{\prime\prime})\) is at least \(\frac{7}{6}r\). Therefore, we can claim that the curve segments of two non-adjacent balls cannot intersect if they are centered on a same 1-face \(\sigma\).
## 4 Results and Discussions  
### Verification Model  
To verify the simulation model in this work, simulation results were compared to the penetration experiments of previous works. [PERSON] and [PERSON] [17] examined an oblique impact test using the  
Figure 8: Simulation model for (**a**) normal penetration and (**b**) oblique penetration.  
Figure 7: Material properties of vacuum-arc-remelted (VAR) 4340 with respect to strain rate [17].  
projectile having an ogive nose with the CRH value of 3 (CRH = 3) at various impact velocities and angles. The comparisons were conducted with the impact angle of 15\({}^{\circ}\) and 30\({}^{\circ}\), which are presented in the previous works. Collision with the angle of 30\({}^{\circ}\) was analyzed for 1156 m/s and 753 m/s. Collision with the angle of 15\({}^{\circ}\) was investigated for 1209 m/s and 985 m/s. The target material was 6061-T6511 aluminum. The projectile material was VAR 4340 steel.  
Under the conditions mentioned above, the results of the simulation and the experiment were listed in Table 1 and showed good agreements. The final coordinates of the projectile tip were (\(-\)39.1 mm, 130.7 mm) in the experimental result and (\(-\)37.1 mm, 129.55 mm) in the simulation result. Figure 9a presents the experimental result of the final deformed shape after impact for V = 985m/s, 15\({}^{\circ}\) angle of obliquity. Figure 9b presents the simulation result of the same conditions. The most significant difference was shown in the case of the impact angle of 30\({}^{\circ}\) and the velocity of 1156 m/s. In this case, the final position of the projectile tip showed an error of 17.25% in the Y-directional coordinate. In most other cases, the error is less than 5%. The analysis results showed that the analytical model of this study could predict the results of the experiment precisely considering the problems of measurement and experimental errors.
# Enhancing Target Speech Based on Nonlinear Soft Masking Using a Single Acoustic Vector Sensor
## 4 Experiments and Results
### Experiments on Simulated Data
#### 4.1.3 Experiment 3: The Performance versus DOA Mismatch  
In practice, the DOA estimation of the target speaker may be inaccurate or the target speaker may make a small movement that causes the DOA mismatch problem. Hence, this experiment evaluates the impact of the DOA mismatch on the performance of our proposed speech enhancement algorithm. The experimental settings are as follows. (1) The parameters of algorithms are set as same as the _Experiment 1_. (2) (\(\theta_{s}\),\(\phi_{s}\)) = (45\({}^{\circ}\),45\({}^{\circ}\)) and (\(\theta_{1}\),\(\phi_{1}\)) = (90\({}^{\circ}\),135\({}^{\circ}\)). (3) The SIR-input is set to 0 dB, while the SNR-input is set to 10 dB; the performance measurement metrics are chosen as SINR and LSD. (4) We consider two cases:  
Case 1: Only \(\phi_{s}\) is mismatched, and the mismatch (\(\partial\phi_{s}\)) ranges from 0\({}^{\circ}\) to 30\({}^{\circ}\) with 5\({}^{\circ}\) increments.  
Case 2: Only \(\theta_{s}\) is mismatched, and the mismatch (\(\partial\theta_{s}\)) ranges from 0\({}^{\circ}\) to 30\({}^{\circ}\) with 5\({}^{\circ}\) increments.  
Experimental results are given in Figures 8 and 9 for Case 1 and Case 2, respectively. From these results, we can clearly see that when the DOA mismatch is less than 20\({}^{\circ}\), our proposed AVS-SMASK algorithm is not sensitive to DOA mismatch. Besides, our AVS-SMASK algorithm outperforms the AVS-FMV algorithm under all of the conditions. However, when the DOA mismatch is larger than 20\({}^{\circ}\), the performance of our proposed AVS-SMASK algorithm drops significantly. Fortunately, it is easy to achieve 20\({}^{\circ}\) DOA estimation accuracy.  
Figure 8: (Experiment 3) The performance versus the \(\partial\phi_{s}\). (**a**) SINR results and (**b**) LSD results (Case 1).  
Figure 7: (Experiment 2) The performance versus \(\Delta\theta\). (**a**) PESQ results and (**b**) LSD results (Case 2: \(\theta_{s}\) of the target speaker changes from 0\({}^{\circ}\) to 160\({}^{\circ}\)).
## 5 Results  
### Comparison with State-of-the-Art Methods  
We compared our proposed method with numerous existing advanced methods on the DRIVE, CHASE_DB1, and STARE datasets. The comparison results for each dataset are presented in Tables 1-3, with the optimal performance values highlighted in bold for each metric.  
For the DRIVE dataset, the proposed method achieved the highest accuracy, AUC, specificity, and sensitivity values of 0.9690, 0.9858, 0.9829, and 0.8230, respectively, among all the comparison methods. Notably, our method significantly outperformed the others in terms of accuracy.  
Regarding the CHASE_DB1 dataset, while [PERSON] et al. [35] achieved the highest AUC, the difference was merely 0.0063 compared to our method. However, our method significantly outperformed theirs in terms of the other three metrics (accuracy, specificity, and sensitivity), with improvements of 0.0087, 0.0045, and 0.0024, respectively. Compared with all other methods, ours yielded the best results in most evaluation metrics, particularly in accuracy and specificity, reaching values as high as 0.9757 and 0.9858, respectively.  
Regarding the STARE dataset, our method achieved the highest accuracy and specificity, reaching 0.9765 and 0.9862, respectively, with an accuracy improvement of 0.0053 compared to the second-best method. Although the method in [36] achieved the highest sensitivity value, its accuracy and specificity values were lower than those of our proposed method.  
According to the experimental data presented in Tables 1-3, our proposed method exhibited superior segmentation performance compared to the other methods, achieving the highest accuracy values of 0.9690, 0.9757, and 0.9765, respectively, on all three datasets. Accuracy serves as a critical measure of the model's overall performance, with higher values indicating superior segmentation capabilities. Furthermore, our approach outperformed other methods across most of the evaluated metrics on all three datasets. In summary, the quantitative experimental results clearly showcase the exceptional segmentation performance of our model.
# Improving the Phosphatase-Catalyzed Synthesis of 5'-Nucleotides: A Reaction Engineering Approach
## 1 Introduction
Kinetic data indicate that the key step in the NSAP-catalyzed reaction is the formation of a phosphate-enzyme intermediate (E-P), which undergoes either nucleophilic attack by a substrate hydroxyl group to form the phosphorylated product (transphosphorylation) or hydrolysis, releasing inorganic phosphate (Pi) in the reaction medium. In addition, due to the reversibility of the phosphate transfer, a progressive dephosphorylation of the formed phosphate ester may occur (Scheme 1) [17; 26]. Thus, in the case of the PhoC-_M_m-mediated phosphorylation of inosine (I) to inosine \(5^{\prime}\)-monophosphate (IMP) in the presence of PPi, a maximum 41% conversion was observed due to the high hydrolytic activity of the wild-type catalyst toward the synthesized IMP [25]. In order to reduce the dephosphorylating activity and to increase the efficiency of the transphosphorylation reaction, a mutated acid phosphatase was identified (II71T-G92D PhoC-_M_m_) with more advantageous kinetic parameters responsible for the improved productivity (K\({}_{\text{m inosine}}\) = 117 mM vs. 42.6 mM for wild-type and mutant enzyme, respectively) [27]. Later, a number of acid phosphatases from enteric bacteria with regioselective PPi-nucleoside phosphotransferase activity were isolated, cloned, and used for the synthesis of IMP [28; 29]. For all the investigated bacteria, the IMP productivities were inferior to those of the enzyme from _M. morganii_ (PhoC-_Mm_) even in the case of mutant acid phosphatases [30]. However, some reaction details for this industrially relevant enzymatic phosphorylation are still lacking and the influence of the reaction parameters upon nucleotide production has never been deeply investigated. It is worth noting that, not unlike conventional chemical routes, enzymatic strategies are hampered by many potential bottlenecks and need fine-tuning as well. Therefore, applying the approach of reaction engineering, e.g., investigating multiple reactions at varied parameters, can help overcome these difficulties and additional benefits such as the shift of unfavorable equilibria can be obtained. Indeed, reaction engineering has long provided an effective tool for bioprocess development, resulting in cost-effective and scalable methodologies [31]. In this study, the fed-batch phosphorylation of inosine to IMP using the wild-type Phoc-_Mm_ as the biocatalyst and disodium pyrophosphate (PPi) as the phosphate source was investigated. The reaction conditions were optimized with respect to the type of buffer, amount of phosphate donor and mode/time of its addition, substrate concentration, and amount of added biocatalyst. Up to a 66% conversion and 62% of isolated yield of IMP was obtained. To the best of our knowledge, this represents the best result so far observed with a wild-type non-specific acid phosphatase.
## 2 Modeling of Offshore Oil and Gas Platform Cluster System
### Topology Model  
The cluster distribution network of offshore oil-gas platforms with \(n\) nodes is shown in Figure 1.  
In Figure 1, \(S_{\text{G1}}\) is the apparent power of the upper distribution network, \(Z_{\text{i-1}},i\) denotes the line impedance from node \(i-1\) to node \(i\), \(U_{i}\) is the voltage of node \(i\), \(S_{\text{i-1}},i\) is the line-flow power from node \(i-1\) to node \(i\), \(S\)i is the apparent injection power of node \(i\), and \(S_{\text{Li}}\) is the sum of the load power \(P_{\text{i}}/Q_{\text{i}}\) of node \(i\) and the connected power of the distributed generation units. WT/PV ES and other distributed generation units are connected to the local line through the power electronic converter and supply power to the load of the offshore oil-gas platform, together with the distribution network. The excess renewable energy can also be transmitted through the distribution network to supply power for other loads.  
The injection power of any node m and the flow power between nodes can be derived from Figure 1:  
\[\left\{\begin{array}{l}S_{m}=\sum\limits_{i=m}^{n}S_{Li}+\sum\limits_{i=m+1} ^{n}\Delta S_{i}\\ S_{m,m+1}=\sum\limits_{i=m+1}^{n}(S_{Li}+\Delta S_{i})\end{array}\right., \tag{4}\]  
where \(\Delta S_{i}\) is the power loss of the line, which can be expressed as follows:  
\[\Delta S_{i}=\frac{P_{i}^{2}R_{i-1,i}+Q_{i}^{2}R_{i-1,i}}{U_{i}^{2}}+j\frac{P _{i}^{2}X_{i-1,i}+Q_{i}^{2}X_{i-1,i}}{U_{i}^{2}}, \tag{5}\]  
where \(U_{i}\) is the voltage amplitude of node \(i\); \(P_{i}\) and \(Q_{i}\) are the injected active and reactive powers, respectively, of node \(i\); and \(R_{i-1,i}\) and \(X_{i-1,i}\) are the resistance and reactance, respectively, between nodes \(i-1\) and \(i\).  
Node \(1\) is a slack bus, and the voltage of node \(m\) can be expressed as follows:  
Figure 1: A cluster-distribution network with \(n\) nodes for offshore oil–gas platforms.  
\[\dot{U}_{m}=\dot{U}_{1}-\sum_{i=2}^{m}\frac{P_{i}R_{i-1,i}+Q_{i}X_{i-1,i}}{U_{i}}-j \sum_{i=2}^{m}\frac{P_{i}X_{i-1,i}-Q_{i}R_{i-1,i}}{U_{i}}. \tag{6}\]
# EAOA: Energy-Aware Grid-Based 3D-Obstacle Avoidance in Coverage Path Planning for UAVs
## 3 Energy-Aware Grid-Based 3D-Obstacle Avoidance (EAOA)  
### Basic Approach  
Different grid-based related works avoid the whole cells that contain obstacles by moving immediately to another grid-cell [10; 31]. In Figure 1, the obstacle \(Ob1\) in the second column is avoided by moving at the edge of the cell. However, in the third column, another way to avoid the obstacle \(Ob2\) is by making a detour (the basic approach). We noticed that, by making a detour, the path will have a lower number of turns and shorter path length. In Section 4.2, we present the results of adopting this approach (Scenario 1). As relying on the 2D top view doesn't always give a shorter path or ensures an optimal coverage, we extend the basic approach by including 3D information. We rely on the area's frontal view to decide about the obstacles shape and location and the detour to make. In the next section, we present preliminary concepts and definitions. Results of this approach are presented in Section 4.2-Scenario 2.
# An Overview of Fog Data Analytics for IoT Applications
## 2 Integration of Fog Computing and IoT  
The traditional cloud computing paradigm cannot resolve the low latency and faster response needs of time-critical applications, and that is where fog provides a solution to these problems. The fog computing paradigm effectively handles the problem of vast data collection from IoT devices with a low latency and faster response time, and support demands of quality of service [21]. Smart healthcare devices and wearable sensors monitoring patients will need an immediate response in critical situations, and fog can meet these requirements effectively. Fog's architecture inherently supports agility and flexibility compared to the cloud, which may be packed with much power but does not provide agility, needing computation for sensor networks. Many works have proposed architecture with different perceptions that are most suitable for dealing with various problems specific to certain use cases. However, the fundamental architecture remains the same, with the three-tier architecture consisting of cloud, fog, and IoT layers. Figure 2 describes the architecture advocating programmability, flexibility, and efficient data analytics aspects of fog nodes.
## 3 Simulation Results  
Case studies were performed using two test systems. First, we verified the proposed method using the IEEE 14-bus test system, which is widely utilized to evaluate algorithms considering transmission networks [39]. Then, we provided the simulation results considering an actual power system, i.e., that of Jeju Island in Korea.  
Figure 1: Concept of the method to determine frequency–power droop coefficients of voltage sourced converter (VSC) and line commutated converter (LCC) high-voltage direct currents (HVDCs).  
Figure 2: Overall procedure of the proposed method to calculate droop coefficients.
## 4 Discussion
### Land Uses Changes and Fragmentation  
It is generally accepted that land use changes affect biodiversity levels, modifying the dynamics of animal and vegetal populations and their long-term viability [49]. Fragmentation relies on the structural complexity of the land use matrix, modifying the dispersion capability for species in the landscapes. According to our results, reforestation activities  
Figure 5: Corridor in the Manabi province (Ecuador) for land use scenarios: 2008 (LUSES2008), 2018 (LUSES2018), 2018 avoiding deforestation (LUSES2018 + def), and 2018 considering reforestation (LUSES2018 + for). Corridors show the area representing the set of possible paths connecting two patches. Corridors were divided into six categories: no corridor (0), very low (1–13), low (14–55), medium (56–138), high (139–291) and very high (>292).  
resulted in a more compact network with more fragmentation (i.e., an increasing number of patches and average patch size decreases). Those effects were more concentrated along areas with native forests remnants. Areas with dominant agricultural uses maintained high values of fragmentation metrics [50]. Our results are consistent with previous findings, showing that forest plantation expansion may increase native forest habitat but also increased fragmentation [51]. This contrasting pattern may lead us to perceive that the current shift from marginal tropical agricultural lands to plantations will have a negative effect on flora, fauna and some ecosystem services [52]. However, the presence of remnants of patches of forests, as part of an interdependent forest net, can act as safeguarded areas contributing to landscape arrangement [53]. Forest plantations may increase the dispersion availability in fragmented landscapes, with plantations serving as steppingstones. On this case, generalist species can easily move across a landscape because they do not need specific habitat conditions, prefer open spaces or more edges, or have a larger home range or higher dispersion capability than specialist species [54].  
Our results also show that forest area in Ecuador is decreasing, and reforestation efforts are not enough to reduce the high rates of deforestation suffered in the country. Ecuador is one of the South American countries with the highest deforestation rate. The coast of Ecuador presents highly fragmented ecosystems with very little protection [24; 55]. It is essential to stop deforestation, since if this trend continues, a highly fragmented ecosystem will be formed, with very small patches, harming species that have a high area requirement or forest specialists.
# FL-LoRaMAC: A Novel Framework for Enabling On-Device Learning for LoRa-Based IoT Applications
## 4 Proposed Framework: FL-LoRaMAC  
This section describes the proposed framework FL-LoRaMAC that satisfies the communication design considerations for the implementation of various steps of federated learning. It includes guidelines for the global model downloads, local gradient updates, and downloading the updated global model. Additionally, it also describes how these gradients are processed on the end devices as well as on the centralized server and some mechanisms to optimize communication bandwidth.  
The working of FL-LoRaMAC is divided into three parts:  
1. Network joining;
2. Proposed MAC for gradient updates;
3. Gradient processing.  
Figure 4: LoRaWAN classes of operation.