esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Comparative Structural Assessment and Design Implications of Clamp-Based Connections  
The graph presented (Figure 34) illustrates the relationship between the number of clamps and the mechanical efficiency of the connection, based on normalized performance indicators--displacement per unit load and angular rotation per unit load (\(\mathcal{O}\)/N)--for both 180 mm and 260 mm frame geometries.  
The clamp setup with four fastening points exhibited the lowest displacement and angular rotation per applied force for both frame configurations, confirming the greatest structural stiffness. A moderate increase in both parameters was observed when the system was reduced to three clamps, followed by a more substantial rise when it was reduced to only two clamps. This progression reflects the direct impact of clamp quantity on stiffness, especially between the three- and two-clamp setups, where the loss in rigidity becomes notably nonlinear.  
In the 260 mm frame, lower displacement and rotational values were recorded across all clamp configurations compared to the 180 mm frame, reinforcing its superior stiffness and load-distribution capability. These findings demonstrate that the 260 mm geometry is better suited for high-demand applications requiring minimal deformation, while the 180 mm configuration remains advantageous in lightweight or space-restricted contexts, provided a sufficient number of clamps is used.  
The consistent reduction in both displacement and angular rotation across configurations aligns with experimental and numerical analyses, all of which confirmed that the system remained entirely within the elastic regime, with no evidence of plastic deformation.  
This behavior reflects the Design for Deconstruction (DfD) principles, showcasing the versatility of clamp-based connections for applications requiring repeated assembly, disassembly, or structural reconfiguration. By adjusting the number of clamps, designers can modulate connection stiffness and performance without compromising the integrity of the elements [1; 4; 61].
# A Completion Method for Missing Concrete Dam Deformation Monitoring Data Pieces
## 4 Result and Discussion
### Multi-Value Missing Data Completion  
Similarly, take the deformation sequence of PL5-3 as an example. A one-month missing segment is constructed manually (from 1 September 2014 to 2 October 2014). The BP neural network is used to establish the mapping relationship between other measurement points on the same vertical line and the deformation sequence of target measurement points. First, the training samples made up of known deformation data of each measurement point are imported into the BP neural network for learning. Second, the deformation values of the missing time of other measurement points are constituted and imported into the trained network to calculate the missing data of target measurement points. The calculation results of each method are shown in Figure 8, and the completion accuracy is shown in Table 4.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Date**} & \multicolumn{2}{c}{**Deformation Value/mm**} & \multirow{2}{*}{**Date**} & \multicolumn{2}{c}{**Deformation Value/mm**} \\ \cline{2-2} \cline{5-7}  & **PL5-3** & **PL5-4** & & **PL5-3** & **PL5-4** \\ \hline
2014 August 31 & 6.14 & 6.85 & 2014 September 10 & 7.93 & 9.41 \\
2014 September 1 & 6.18 & 7  
As can be seen from the results in Figure 8 and Table 4, the completion accuracy of the multi-value missing completion method proposed in this paper based on spatial adjacent point BP mapping is higher than that of the other three methods. The coefficient of determination and root-mean-square error all achieve satisfactory results. The interpolation method of spatial adjacent points also has a good estimation result, but since this method only uses the deformation information of the upper and lower measurement points for regression analysis, it cannot fully dig out the relevant information of the deformation of the target measurement points. The nonlinear regression utilizes the idea of a statistical model, which can be used for completion under the condition of tiny changes of environment quantity. The effect of linear interpolation is poor, so it is difficult to estimate multi-value missing data.
# Region-Based CNN Method with Deformable Modules for Visually Classifying Concrete Cracks
###### Abstract  
Cracks are often the most intuitive indicators for assessing the condition of in-service structures. Intelligent detection methods based on regular convolutional neural networks (CNNs) have been widely applied to the field of crack detection in recently years; however, these methods exhibit unsatisfying performance on the detection of out-of-plane cracks. To overcome this drawback, a new type of region-based CNN (R-CNN) crack detector with deformable modules is proposed in the present study. The core idea of the method is to replace the traditional regular convolution and pooling operation with a deformable convolution operation and a deformable pooling operation. The idea is implemented on three different regular detectors, namely the Faster R-CNN, region-based fully convolutional networks (R-FCN), and feature pyramid network (FPN)-based Faster R-CNN. To examine the advantages of the proposed method, the results obtained from the proposed detector and corresponding regular detectors are compared. The results show that the addition of deformable modules improves the mean average precisions (mAPs) achieved by the Faster R-CNN, R-FCN, and FPN-based Faster R-CNN for crack detection. More importantly, adding deformable modules enables these detectors to detect the out-of-plane cracks that are difficult for regular detectors to detect.  
structural health monitoring (SHM); deep learning; convolutional neural network; deformable convolution; concrete cracks; out-of-plane crack +
Footnote †: journal: Applied Sciences
# LR Parsing for LCFRS
## 1 Introduction
TuLiPA [13] is a multi-formalism parser used in a development environment for variants of Tree Adjoining Grammar (TAG). It exploits the fact that LCFRS is a mildly context-sensitive formalism with high expressivity. LCFRS acts as a pivot formalism, i.e., instead of parsing directly with a TAG instance, TuLiPA parses with its equivalent LCFRS, obtained through a suitable grammar transformation [14]. LCFRS has also been used for the modeling of non-concatenative morphology, i.e., for the description of discontinuous phenomena below word level, such as stem derivation in Semitic languages. In such languages, words are derived by combining a discontinuous root with a discontinuous template. In Arabic, for instance, combining the root _k-t-b_ with the template _i-a_ results in _kitab_ (\"book\"), combining it with _a-i_ results in _kitab_ (\"writer\"), and combining it with _ma-O-a_ results in _maktab_ (\"desk\"). Authors of [15; 16] use LCFRS for the modeling of such processes. Finally, in machine translation, suitable versions of LCFRS, resp. equivalent formalisms, have been proposed  
Figure 2: Discontinuity and non-projectivity in NeGra annotation (see (2)) [10]. for the modeling of translational equivalence [17; 18]. They offer the advantage that certain alignment configurations that cannot be modeled with synchronous variants of CFG can be induced by LCFRS [19]. As an example, see Figure 3, which shows an English sentence with its French translation, along with a grammar that models the alignment between both. Parsing is a key task since it serves as a backend in many practical applications such as the ones mentioned above. The parsing of LCFRS has received attention both on the symbolic and on the probabilistic side. Symbolic parsing strategies, such as CYK and Earley variants have been presented [20; 21; 22], as well as automaton-based parsing [23] and data-driven probabilistic parsing techniques [24; 25; 26]. To our knowledge, however, no LR strategy for LCFRS has so far been presented in the literature. LR parsing is an incremental shift-reduce parsing strategy in which the transitions between parser states are guided by an automaton which is compiled offline. LR parsers were first introduced for deterministic context-free languages [27] and later generalized to context-free languages [28; 29] and tree-adjoining languages [30; 31]. Also for conjunctive grammars and boolean grammars, LR parsing algorithms exist [32; 33; 34]. In this paper, we present an LR-style parser for LCFRS, extending our earlier work [35]. Our parsing strategy is based on the incremental parsing strategy implemented by Thread Automata [23]. The remainder of the article is structured as follows. In the following section, we introduce LCFRS and thread automata. Section 3 introduces LR parsing for the context-free case. Section 4 presents the LR algorithm for LCFRS along with an example. In particular, Section 4.1 gives the intuition behind the algorithm, Section 4.2 introduces the algorithms for automaton and parse table constructions, and Section 4.3 presents the parsing algorithm. Section 5 concludes the article.
## Operating Principle of the Proposed Dual-Mode Control Strategy for the DAB Converter  
The operating principle of the proposed dual-mode control strategy for the DAB converter breaks down into two parts: light load and medium-to-full load.  
Since forward power conversion and backward power conversion operate similarly, the analysis will focus on the forward power conversion (power transferred from the DC bus to the battery pack). Figure 4 depicts the structure of the DAB converter for analysis. The components are defined as follows:  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Item** & **Proposed** & **Ref. [10]** & **Ref. [12]** \\ \hline Topology & DAB & DAB & SRDAB \\ Control strategy & Dual-mode PWM-SPS & MEPS & PFM \\ Transformer type & Conventional & Solid-state & Conventional \\  & transformer & transformer & transformer \\ Prototype power rating & 1 kW & 200 W & 1 kW \\ Switching frequency & 100 kHz & 20 kHz & 70-150 kHz \\ Maximum efficiency & 97.30\% & pprox. 90.00\% & 94.60\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of the DAB converter with different control strategies.  
Figure 4: Circuit structure of the DAB converter.  
1. \(S_{1}\), \(S_{2}\), \(S_{3}\), and \(S_{4}\) are power switches of the high voltage full-bridge.
2. \(S_{5}\), \(S_{6}\), \(S_{7}\), and \(S_{8}\) are power switches of the low voltage full-bridge.
3. \(D_{1}\), \(D_{2}\), \(D_{3}\), and \(D_{4}\) are the intrinsic diodes of power switches \(S_{1}\), \(S_{2}\), \(S_{3}\), and \(S_{4}\) of the high voltage full-bridge, respectively.
4. \(D_{5}\), \(D_{6}\), \(D_{7}\), and \(D_{8}\) are the intrinsic diodes of power switches \(S_{5}\), \(S_{6}\), \(S_{7}\), and \(S_{8}\) of the low voltage full-bridge, respectively.
5. \(C_{OSSI}\), \(C_{OSSZ}\), \(C_{OSS3}\), and \(C_{OSS4}\) are the parasitic capacitances of power switches \(S_{1}\), \(S_{2}\), \(S_{3}\), and \(S_{4}\) of the high voltage full-bridge, respectively.
6. \(C_{OSSS}\), \(C_{OSS6}\), \(C_{OSS7}\), and \(C_{OSS8}\) are the parasitic capacitances of power switches \(S_{5}\), \(S_{6}\), \(S_{7}\), and \(S_{8}\) of the low voltage full-bridge, respectively.
7. \(L_{M}\) is the magnetizing inductance of the transformer.
8. \(L_{K}\) is the sum of transformer leakage inductance and auxiliary inductance.
11. \(V_{Dchbus}\) is the voltage of the DC bus (high-voltage side).
12. \(V_{bat}\) is the voltage of the battery pack (low-voltage side).
# A Topology Optimization Method for Reducing Communication Overhead in the Kalman Consensus Filter
## 3 Network Topology Optimization Method
### Network Topology Optimization Method
(1\({}^{\prime}\)-4) The first link belonging to \(\mathcal{L}_{p,3}\) is taken as the initial adding communication link at node \(p\), and  
\[\text{add}^{\prime}_{p}=\left\{(p,s),d_{p}+d_{s},\frac{\Delta l_{p}+\sum \limits_{q\in\mathcal{N}_{p}}\Delta l_{q}+\Delta l_{s}}{d_{p}+2},\frac{\Delta c _{p}+\sum\limits_{q\in\mathcal{N}_{p}}\Delta c_{q}+\Delta c_{s}}{d_{p}+2}\right\} \tag{11}\]  
Step 2\({}^{\prime}\): To determine the final adding communication link at node \(p\):  
(2\({}^{\prime}\)-1) The initial adding communication link at node \(p\) is exchanged with its neighboring nodes, and \(\text{add}^{\prime}_{\mathcal{N}_{p}}\triangleq\left\{\text{add}^{\prime}_{ q}\right\}_{q\in\mathcal{N}_{p}}\). (2\({}^{\prime}\)-2) The same operations in step 1\({}^{\prime}\) are used to determine the final adding communication link at node \(p\), and  
\[add_{p}=\left\{(c,d),d_{c}+d_{d},\frac{\Delta l_{c}+\sum\limits_{b\in \mathcal{N}_{c}}\Delta l_{b}+\Delta l_{d}}{d_{c}+2},\frac{\Delta c_{c}+\sum \limits_{b\in\mathcal{N}_{c}}\Delta c_{b}+\Delta c_{d}}{d_{c}+2}\right\} \tag{12}\]  
According to the rules for deleting and communication links described above, we propose a network topology optimization method summarized in Algorithm 1 as follows, in which the communication range of each node is assumed to be the same.
## 2 Related Works
The EECHIGOWO algorithm demonstrated a substantial improvement in network lifetime, with reported enhancements of up to 333.51% compared to several existing protocols, such as SSMOECHS, FGWSTERP, and LEACH-PRO. Although the EECHIGOWO aims to balance exploration and exploitation, there may still be scenarios where it could get trapped in local optima, especially in highly complex or non-linear search spaces. The EECHIGOWO may also introduce additional complexity in terms of implementation and parameter tuning. [PERSON] et al. (2016) [14] developed an underwater sensor network redeployment algorithm based on the wolf search algorithm (RAWS) to optimize how sensors are deployed in underwater environments. The focus of RAWS was to address challenges like inefficient network coverage, high computational complexity, energy inefficiency, obstacles, and environmental changes affecting underwater sensor networks' performance. The RAWS allows each node to perform an independent search for monitoring purposes. This does not enable sensors to exchange information with one another, which reduces communication overhead and computational complexity. Sensors move towards targets when detected or explore freely when no targets are present. The proposed RAWS included an escape mechanism that allows sensors to change their direction randomly when they encounter an obstacle to avoid it. This feature helps prevent sensor node failure and allows the algorithm to escape local optima during the search process. The node coverage for each node \(s_{i}\), which was computed in Equation (6) as:  
\[N_{event}(s_{i})=\sum_{1}^{n}f(p_{j},s_{i})\
eq 0 \tag{6}\]  
where, \(f(p_{j},s_{i})\) is a function that determines if the target \(p_{j}\) is within the sensing range of the node \(s_{i}\). The performance of the proposed RAWS was evaluated through simulations. The RAWS results demonstrated improvements in network coverage, energy conservation, and obstacle avoidance compared to other existing algorithms, such as the artificial fish swarm algorithm. Although the proposed RAWS has demonstrated some improvement in network performance, it may be sensitive to the initial random deployment of nodes. If nodes are poorly positioned initially, it may take longer for the algorithm to achieve optimal coverage, or it may converge to a suboptimal solution. Also, the escape mechanism employed by RAWS relies on the ability of nodes to detect obstacles. If the detection range is limited or if obstacles are not accurately represented in the model, nodes may fail to avoid them effectively, leading to potential collisions or failures.
After experiments, it was found that the dust suppressor had functions such as wetting, bonding, water absorption, coagulation, and good dust suppression [20]. [PERSON] synthesized a biological dust suppressor through microbial fermentation that has excellent wettability and high biodegradability [15]. The effective reduction of surface tension by fluorine-containing surfactants is manifested in the amphiphilic structure in the microscopic molecular structure of the fluorine-containing solution [21], which includes both the hydrophilic part insoluble in the organic phase and the oil-philic part soluble in the organic phase [22; 23; 24]. Fluorine-containing surfactants are substances that can rapidly reduce the surface tension of a solution and the surface state of a system with a very small dose to produce wetting, infiltration, condensation, and other capabilities [25; 26; 27]. Fluorine-containing surfactants can also be combined well with hydrocarbon surfactants [28; 29]. The compound solution has good performance and can more effectively reduce the surface tension of the solution (common surfactants can reduce the surface tension of water to 30 mN/m when dissolved in water, while fluorine-containing surfactants containing CF\({}_{3}\) or CF\({}_{2}\) can reduce the surface tension of water to 10 mN/m) and enhance the wettability of the solution [30]. Due to its high electronegativity and small atomic radius, fluorine can form a firm carbon-fluorine bond and maintain good thermal stability under high temperatures and pressure [31; 32; 33; 34]. The surface activity of a substance containing fluorine can be classified into four types: anionic, cationic, non-ionic, and zwitterionic. [35; 36]. The key advantage of fluorine-containing surfactants lies in their unique amphiphilic structure, which encompasses both hydrophilic and oil-philic parts, enabling them to significantly reduce the surface tension of a solution with a minimal dose, leading to enhanced wetting, infiltration, and condensation capabilities. While previous research has focused on the performance of fluorinated solutions in organic systems, there is a dearth of studies investigating their specific interactions with coal dust, particularly in terms of wettability and water retention properties. To further investigate the wettability and water retention properties of fluorinated surfactants, four non-ionic fluorinated surfactants (perfluoropropyl acrylate (FS-30), perfluorooctyl acrylate (FS-31), perfluorooctylsulfonyl phenoxyethyl methacrylate (FS-34), and perfluoropropanoic acid (FS-50)) and three anionic surfactants (sodium perfluorooctanesulfonate (FS-60), perfluoroisopropyl acrylate (FS-61), and perfluorooctanesulfonate carbamate acid (FS-63)) were selected for experiments in this study.
# EOR: An Enhanced Object Registration Method for Visual Images and High-Definition Maps
## 5 Discussion
### Scene Understanding from Sequential Experiments  
The sequential experiments reveal additional scene-understanding capabilities provided by the spatial factors. HD map-derived 3D factors exhibit smoother and more consistent trends compared to segmented image-derived 2D factors, primarily due to the inherent noise in visual recognition. The GDOP factor, which generally indicates low uncertainty when objects are evenly distributed, becomes larger when there is clustering or a lack of objects, indicating possible registration challenges. The longitudinal difference factor aligns well with intersection regions, serving as a reliable indicator of intersection boundaries. These findings demonstrate the role of spatial factors in enhancing scene understanding and providing insights into the distribution and quality of environmental features.
# Performance Analysis of a Novel TCP Protocol Algorithm Adapted to Wireless Networks
## 4 Discussion
### Results from Long Distance Experiment  
For this experiment, we obtained \(\overline{\theta}\), \(\delta\), and jitter as a function of the bandwidth for a 94 km distance between terminals. Figure 12 shows \(\overline{\theta}\), \(\delta\), and jitter results obtained by using the D-ITG software. The results show that there are low \(\overline{\theta}\) and jitter variance between TCP-NACK and Generic TCP, but a better \(\delta\) performance for TCP-NACK for bandwidth up to 3000 kbps.  
Figure 11: Short-medium distance results: (**a**) \(\overline{\theta}\); (**b**) upper \(\overline{\theta}\); (**c**) \(\delta\); (**d**) bottom \(\delta\); (**e**) jitter; (**f**) bottom jitter.  
We determined that this was not improved in terms of throughput and jitter, but in terms of delay, we observed a significantly reduction; this is due to the fact that we considered to modify the ACK timeout value for MAC layer in order to reach long distance [24], but we did not consider changing the main parameters related to ACK timeout at Transport layer as this is out of this study's scope.
## 4 Results and Analysis
### Questionnaire Data Results and Analysis  
#### 4.3.1 Questionnaire Validity Test  
The data from 237 questionnaires were imported into SPSS 21.0 for reliability and validity testing. The reliability of the four dimensions of the questionnaire was monitored, and a Cronbach's value of >0.7 was found, indicating that the overall reliability of the questionnaire was good. A Kaiser-Meyer-Olkin (KMO) test revealed a KMO value = 0.978 > 0.8 and a \(p\)-value (Sig.) = 0.000 < 0.05, with high significance. The questionnaire passed the BART spherical test of the KMO checksum.
## 3 Results
### Validation of the Method  
The calibration curves of the matrix curves were constructed with spiked water samples, following SB-MSPE. Linearity studies of extraction were performed, covering the whole working range. The data were processed by linear regression analysis of least squares of the peak area versus analyte concentration (ng/mL). The calibration curves were found to be linear up to 250 ng/mL, with coefficients of determination (\(\mathbf{r}^{2}\)) > 0.992,  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Examined Parameter** & **Value** \\ \hline Elution Solvent & CH\({}_{3}\)CN (ACN) \\ Adsorbent Mass & 40 mg \\ Volume of Elution Solvent & 0.5 mL \\ Volume of Sample & 10 mL \\ Stirring Speed & 750 rpm \\ Addition of Salt & 0\% \(\
icefrac{{\mathrm{v}}}{{\mathrm{v}}}\) \\ pH & 7 \\ Activation Time with MeOH & 5 min \\ Activation Time with DW 1  & 5 min \\ Duration of Extraction & 30 min \\ Duration of Elution & 10 min \\ Total Duration & 50 min \\ \hline \hline \end{tabular}
\end{table}
Table 1: Optimal conditions for achieving the highest recoveries.  
Figure 15: Diagram of the normalized absolute recoveries of the two vials until the eighth cycle, according to the first cycle.  
while LOD and LOQ values were determined as 3 ng/mL and 10 ng/mL, respectively, for ALP and FLT. The calibration results and obtained equations are demonstrated in Table 2.  
Accuracy and precision were studied at three intermediate concentrations. Mean accuracy was expressed as the relative recovery (R\({}_{\text{rel}}\)), which was between 98.0-104.1% for the two compounds, while intraday and interday precision was expressed via the relative standard deviation (RSD), which was lower than 11.9%. The results are summarized in Table 3.
# A Design of a Parcel Delivery System for Point to Point Delivery with IoT Technology
## 3 Improved Parcel Delivery System Design
### Parcel Collection Stage  
When a parcel pick-up starts with a shipping box attached to IoT devices and IoT devices are turned on, ID information is printed through display modules. In a moment, LoRa will be connected to the server. The scanner then collects data from the barcode attached to the shipping box. Information of IoT devices, invoice number, and delivery location are matched to send to the server and stored in table form. The IoT device calculates the shortest route to collect and parcel destination. If that distance is less than threshold value, then add destination as a waypoint.
# On Compressed Sensing of Binary Signals for the Unsourced Random Access Channel
## 2 Compressed Sensing of Binary Signals
In this paper, we address these two points: for the sensing matrix, we propose to use sparse matrices based on LDPC codes; as for the recovery algorithm, we propose to use an MCMC sampling method that approximates the optimal (in terms of bit error probability) MAP estimator.