esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Type I PKS Phylogeny
#### 3.3.2. Fungal PKSs Producing Non-Reduced Polyketides  
The Diversity of NR-PKS Sequences  
In previous studies, NR-PKSs were divided into nine major groups based on protein sequence similarity and PKS domain content [14; 69; 71; 29]. Similar to previous studies, we observed characteristic domain configurations for the different clades in our phylogeny. The domain structure of NR-PKSs in the studied fungi can be generalized as SAT-KS-AT-DH-ACP-[ACP]-HTH-CMeT-[TE or ADH or NAD]-[Peptides S9]; however, some PKS genes may deviate from this structure. The observed domain content variations are not random but rather occur in two main patterns: either through the duplication of the ACP domain, or through the addition of an N-terminal TE domain. PKS genes possessing an additional ACP domain are scattered throughout different NR-PKS clades, suggesting multiple independent gains. However, the functional significance of ACP domain duplications is still unknown [14].  
Most of the NR-PKS sequences belong to a large clade containing groups NR-I to NR-V and NR-VIII, with domain configuration containing a TE domain at the N-terminal end.  
The irregularly present additional TE domain is involved in a thioesterase-mediated product release, which is the most common release mechanism in TI-PKS [74]. It regularly extends to a C-C Claisen cyclization domain (TE/CLC domain), e.g., in _Aspergillus parasitic_ PksA [75]. Although CYC domains have previously been reported from various fungi [14; 69; 70], we could not identify them in our analyses and thus did not indicate them in the phylogeny.  
Subclade I of NR-PKSs (Including Groups NR-I to NR-V, and NR-VIII)  
The paraphyletic group NR-I contains several clades with previously characterized sequences involved in the biosynthesis of aromatic compounds derived from orsellinic acid, such as grayanic acid (PKS16), physodic acid and olivetoric acid [24; 25], lecanoric acid [76],xanthones, aflatoxin, and naphthoquinones [14]. In addition, the clade also comprises PKS13 sequences from several lichen-forming fungi and _Gibberellla zeae_ as well as a PKS15 sequence from _Botryotinia fuckeliana_ with unknown functions.
## 4 Creation of Models and Extraction of Geometrical Components
### Extraction of Geometrical Components
#### 4.1.2 Detection of Vertical Planes and Poles  
Vertical planes' and poles' extraction follows a similar approach. Specifically, they are obtained as follows:  
1. In these cases, the distribution of the points is analyzed on a 2D histogram generated by projecting all the points into the floor plane.
2. Considering this 2D histogram as a grayscale image, all the points from a vertical plane will form a line on the image, and the poles will form a spot; then extracting the lines and spots, and their corresponding projected points, will provide the vertical planes and poles (Figure 8).  
Finally, image processing algorithms can be applied in order to segment those lines on the 2D histogram and then recover points corresponding to those vertical planes.  
In the cases where two vertical planes are projected to the same line, they can be separated by a similar approach mentioned in the previous section for segmenting horizontal planes; however in this case, the points are projected to a vertical plane. Then, it is possible to cluster them and perform some calculations on them like the area of the projected surface. For the current state of the work, we are more interested in the form of the furniture rather than its parts, so adjacent planes belonging to the same piece of furniture, i.e., the drawers of a chest of drawers, are not separated. If the separation of  
Figure 5: Visualization of a PR2 robot with its coordinate frames and the point cloud from the scene transformed to the world reference frame.  
Figure 6: Example of a height histogram of two different pieces of furniture. In (**a**), the RGB image of a chest of drawers, and in (**b**), its height histogram. In (**c**,**d**), the RGB image of a bed and its height histogram, respectively.  
Figure 7: Example of a height histogram and the objects segmentation. In (**a**), the RGB from the scene, in (**b**), the height histogram, and (**c**,**d**) show the projections for each peak found on the histogram.  
these planes were necessary, an approach similar to the one proposed in [22] could be used; where they segmented the drawers from a kitchen.  
With this approach, it is also possible to detect small regions or dots that correspond to the legs of the chairs or tables. Despite the small sizes of the regions or dots, they are helpful to characterize the furniture.  
Our proposal can work with full PCD scenes without any requirement of a downsampling, which must be performed in algorithms like RANSAC in order to maintain a low computational cost. Moreover, histograms are computed linearly.
## 2 Methods: Experiment and Numerical Simulation
### Numerical Models  
#### 2.2.1 SMA Model  
The SMA wires that are used in the experiment are the super-elastic 55.9 wt.% Ni balance Ti wire with the diameter of 0.2 mm, produced by Jiangyin Fasten-PLT Materials Science Co., Ltd. (Pelertech), Jiangsu, P.R. China. In Abaqus, the elastic-plastic theory following the [PERSON] (J2) yield criterion was adopted to simulate the mechanical behavior of SMA during the loading stage. The elasticity governing equations:  
\[\sigma_{j}^{i}=\lambda\delta_{ij}\varepsilon_{kk}^{el}+2\mu\varepsilon_{ij}^{el} \tag{1}\]  
The plasticity yield function:  
\[\sqrt{\frac{3}{2}S_{ij}S_{ij}}-\sigma_{y}(\overline{\varepsilon}^{pl})=0 \tag{2}\]  
\[S_{ij}=\sigma_{ij}-\frac{1}{3}\delta_{ij}\sigma_{kk} \tag{3}\]  
The equivalent plastic strain:  
\[\overline{\varepsilon}^{pl}=\int\limits_{0}^{t}\overline{\varepsilon}^{pl}dt \tag{4}\]  
\[\dot{\overline{\varepsilon}}^{pl}=\sqrt{\frac{2}{3}\dot{\overline{\varepsilon }}_{ij}^{pl}\dot{\overline{\varepsilon}}_{ij}^{pl}} \tag{5}\]  
The plastic flow law:  
\[\dot{\varepsilon}_{ij}^{pl}=\frac{2}{3}\frac{S_{ij}}{\sigma_{y}}\overline{ \varepsilon}^{pl} \tag{6}\]  
where \(S_{ij}\) is deviatoric stress, \(\sigma_{y}\) is yield stress, \(\overline{\varepsilon}^{pl}\) is the equivalent plastic strain, and \(\dot{\overline{\varepsilon}}^{pl}\) is the equivalent plastic strain rate. The schematic diagram of the stress-strain curve for Ni-Ti SMAs in the case of breaking is shown in Figure 2, and the yield stress versus equivalent plastic strain for the Ni-Ti SMA wires are tabulated in Table 2. The stress-strain data are input to define the SMA material behavior in the simulation.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Codes/Stacking Sequence** & **Layer Number of SMAs** & **Root Number of SMAs** & **Initial Impact Energy (J)** \\ \hline I: [0/90]\({}_{8}\) & 0 & 0 & \\ II: [(0/90)\({}_{4}\)/SMA/(0/90)\({}_{4}\)] & 1 & 21 & \\ III: [0/SMA/90/(0/90)\({}_{7}\)] & 1 & 21 & \\ IV: [0/SMA/90/(0/90)\({}_{3}\)/SMA/(0/90)\({}_{4}\)] & 2 & 42 & \\ \hline V: [0/90]\({}_{8}\) & 0 & 0 & \\ VI: [(0/90)\({}_{4}\)/SMA/(0/90)\({}_{4}\)] & 1 & 5 & \\ VII: [0/SMA/90/(0/90)\({}_{7}\)] & 1 & 5 & 64 \\ VIII: [0/SMA/90/(0/90)\({}_{3}\)/SMA/(0/90)\({}_{4}\)] & 2 & 10 & \\ \hline \hline \end{tabular}
\end{table}
Table 1: The relevant parameters of hybrid composite laminates and the initial impact energy.
## 3 Results and Discussion
### Impact Characteristics under Cryogenic and High Temperatures
#### 3.2.1 Energy Absorption  
To further study the energy absorption by triaxial braided composite materials due to the impact of CFRP blade projectiles at various temperatures, Figures 14 and 15 show the energy absorbed as a function of impact velocity for various temperatures and the average energy absorbed at three temperatures, respectively. Significantly less energy is absorbed by the triaxial braided composite plate at cryogenic temperatures than at room temperature or high temperature. At cryogenic temperatures, the average energy absorbed in cases of target perforation is 189.3 J, which is 22.4% less than at room temperature. The average energy absorbed at high temperature, on the contrary, increases by 13.4% with respect to that at room temperature. These results indicate that low temperature degrades the impact resistance of the two-dimensional triaxial braided composite material, whereas high temperature improves its impact resistance.
# Overcoming Limitations of LoRa Physical Layer in Image Transmission
## 3 Background
### Limitations to Image Transmission over LoRa  
WSNs (see Figure 3) consist of remote sensing nodes monitoring aspects of their environment, which send collected data to a sink center for processing for a specific application. The biggest design concern for WSNs is energy consumption, which should be reduced in order to offer long-range connections and wide coverage. However, the transfer of data from the sensing nodes to the sink is hindered by the narrow bandwidth of these networks and their limited memory for computational processes [1]. Moreover, LoRa technology constrained to 1% duty-cycle (i.e., 36 s/h) applies to the total transmission time, which means only can transmit data 36 s each one hour of transmission time [22]. This limitation makes transferring data from devices like image sensors very complex, as a large bit rate is required for the communication of image data. Hence, although LoRa has the ability to combine the features of LPWA networks and WSN networks, transferring multimedia data over LoRa is a challenging issue because of the data transmission limitation.  
The bandwidth of transmission is the most important factor that affects the performance of the CSS technique used for LoRa modulation. A LoRa symbol consists of \(2^{SF}\) chirps, with one chirp  
Figure 3: Composition of a wireless sensing network (WSN) [1].  
transmitted per second (chirp rate), equal to a bandwidth of one Hertz Hertz (1984). Moreover, with LoRa modulation the bit rate and symbol rate at a given \(SF\) are proportional to bandwidth.  
The duration of a LoRa symbol, \(T_{s}\), can be calculated from the following equation, while the data bit rate can be calculated using Equation (1) ([PERSON], 1984):  
\[T_{s}=\frac{2^{SF}}{BW} \tag{2}\]  
From Equations (1) and (2) we observe that decreasing the bandwidth will decrease the transmission rate and consequently increase the sensitivity. Hence, to improve the performance of LoRa and increase the communication range, the bandwidth must be limited, such that only a smaller bit rate data can be transferred. In this paper, the lowest \(BW\) configuration (125 kHz) is used to attain the highest sensitivity for long transmissions.
## 5 Conclusions and Recommendations
Based on the numerical simulation and field application results, we recommend conducting the following for the effective implementation of model-constrained Kirchhoff pre-stack depth migration in complex geological settings: (1) Ensure accurate initial velocity modeling through a combination of conventional analysis and constrained velocity inversion to provide a reliable foundation for depth migration; (2) apply robust noise attenuation techniques to enhance the signal-to-noise ratio, particularly in areas with structurally complex and low-quality data; (3) optimize migration parameters such as aperture, anti-aliasing filters, and weighting functions to balance computational efficiency and imaging resolution; and (4) validate the migration results using well and geological data to confirm structural consistency. These strategies help maximize the method's potential in improving imaging accuracy and resolving complex subsurface structures. **Author Contributions:** Conceptualization, L.W. ; Methodology, L.W. ; Formal analysis, S.W., L.Z. and X.H. ; Resources, S.W. and X.H. ; Data curation, L.Z. ; Writing--original draft, L.Z. ; Writing--review & editing, S.W. ; Project administration, L.W. ; Funding acquisition, X.H. All authors have read and agreed to the published version of the manuscript. This work was supported by the National Key Research and Development Program of China (2023 YFC2906501). Not applicable. **Data Availability Statement:** The data presented in this study are available on request from the corresponding author. The authors extend their heartfelt thanks to all the editors and reviewers for their valuable suggestions. Author [PERSON] was employed by the company Anshan China Minmetals Chentaigou Mining Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 3 Experimental Results
### Results
#### 3.4.5 All Datasets Versus Benchmarks  
Again, as mentioned above, we carried out training and testing using all user summaries combined into one label vector. In the existing work, different papers used different approaches for training and testing, with some of them loosely using the term ground truth without further details. Nonetheless, for completeness, in this section, we provide comparisons against the existing work which carry out training and testing using different approaches but with the same datasets.  
Table 3 (A-D) contain the F-scores of our best-performing models from each learning architecture compared against state-of-the-art works in the literature on the SumMe, TVSum, OVP, and VSUMM datasets. With the SumMe dataset in Table 3-A, our random forest model with no dimensionality reduction and with HEVC-based temporal subsampling surpasses the highest scores in the literature.  
With the TVSum dataset in Table 3-B, our LSTM network model with no dimensionality reduction and with HEVC-based temporal subsampling also exceeds the highest scores in the literature. Our second- and third-best models with random forests and 1D-CNNs, without dimensionality reduction and with the HEVC-based temporal subsampling of frames, also exceed the benchmark scores.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **F-Score** & **Method** & **F-Score** & **Method** & **F-Score** & **Method** & **F-Score** \\ \hline RR-STG [44] & 0.637 & MC-VSA [45] & 0.534 & VRHDPS [46] & 0.63 & VSUMM [47] & 0.67 \\ PGL-SUM & 0.654 & [49] & 0.556 & VSUMM [47] & 0.68 & VISCOM [50] & 0.67 \\ SMN [51] & 0.675 & MAVS [16] & 0.583 & VISCOM [50] & 0.72 & VRHDPS [46] & 0.68 \\ Ours & 0.737 & Ours & 0.644 & Ours & 0.84 & Ours & 0.744 \\ (1D-CNN) & 0.74 & Ours (LSTM) & 0.676 & Ours (RF) & 0.869 & Ours (LSTM) & 0.77 \\ Ours (LSTM) & **0.785** & Ours (RF) & **0.737** & Ours (LSTM) & **0.879** & Ours (RF) & **0.808** \\
**(A)** & & **(B)** & & **(C)** & & **(D)** \\ \hline \hline \end{tabular}
\end{table}
Table 3: F-scores of our best performing models from the three proposed learning architectures against benchmark models in the literature on the TVSum (A), SumMe (B), OVP (C), and VSUMM (D) datasets. Sorted ascendingly from top to bottom. Bold scores indicate the highest and underlined scores indicate the second highest.  
With the OVP dataset in Table 3-C, our model with the LSTM network without dimensionality reduction and with the HEVC-based temporal subsampling of frames surpasses the highest scores in the literature. Our second- and third-ranking models with random forests and 1D-CNNs, without dimensionality reduction, and with HEVC-based temporal subsampling also outperform the benchmark scores.  
With the VSUMM dataset in Table 3-D, our model with random forests, without using stepwise regression, and with HEVC-based temporal subsampling tops the best scores in the literature. Our second- and third-best models with LSTM networks and 1D-CNNs, without stepwise regression and with the HEVC-based temporal subsampling of frames, also exceed benchmark scores.
###### Abstract  
To quantify damage to reinforced concrete (RC) column members after an earthquake, an engineer needs to know the maximum applied force that was generated by the earthquake. Therefore, in this work, piezoceramic transducers were used to detect the applied force on an RC column member under dynamic loading. To investigate the use of post-embedded piezoceramic sensors in detecting the force that is applied to RC columns, eight full-size RC column specimens with various failure modes were tested under specific earthquake loadings. Post-embedded piezoceramic sensors were installed at a range of depths (70-80 mm) beneath the surface of a column specimen to examine the relationship between the signals that were obtained from them and the force applied by the dynamic actuator. The signals that were generated by the post-embedded piezoceramic sensors, which correlate with the applied force, are presented. These results indicate that the post-embedded piezoceramic sensors have great potential as tools for measuring the maximum applied force on an RC column in an earthquake. In other words, signals that are obtained from post-embedded piezoceramic sensors on an RC column in an earthquake can be used to determine the applied force and corresponding damage or residual seismic capacity.  
p ezooceramic sensor; reinforced concrete; force; earthquake; damage
## 2 Problem Statement of Multi-Robot Robust Motion Planning
### Robot Model  
In this paper, differential wheeled robots are studied. The planning process is carried out in the SE(2) space and state \(\mathbf{x}_{m,k}\) includes 2-D position (\(x_{m,k}\), \(y_{m,k}\)) and orientation \(\varphi_{m,k}\). The robot model \(f(\cdot)\) can be written as Equation (1).  
\[x_{m,k+1} =x_{m,k}+v_{m,k}\Delta t\cos(\varphi_{m,k}) \tag{1a}\] \[y_{m,k+1} =y_{m,k}+v_{m,k}\Delta t\sin(\varphi_{m,k})\] (1b) \[\varphi_{m,k+1} =\varphi_{m,k}+w_{m,k}\Delta t \tag{1c}\]  
In Equation (1), \(v_{m,k}\) and \(w_{m,k}\) are the velocity and angular velocity of robot \(m\) at time \(k\) respectively. \(\Delta t\) represents the time interval between two control inputs. Based on the robot model (1), the control input of robot \(m\) at time \(k\) is defined as \(\mathbf{u}_{m,k}=[v_{m,k},v_{m,k}]\).  
It is worth mentioning that Equation (1) is the ideal kinematic model of the robot. Under pure rolling conditions, the motion of the robot is exactly in accordance with Equation (1). However, imperfections are unavoidable in real devices [24]. Complex wheel-ground interactions and system noises cause disturbances to the robots. Moreover, these disturbances are modeled as Gaussian random variables, characterized by their mean and covariance matrices. Thus, the model of a given robot \(m\) at time \(k\) are defined as Equation (2).  
\[\mathbf{x}_{m,k+1}=f(\mathbf{x}_{m,k},\mathbf{u}_{m,k})+N_{m,k}^{noise} \tag{2}\]  
In Equation (2), \(\mathbf{x}_{m,k}\in\mathbb{R}^{n_{x}}\) and \(\mathbf{u}_{m,k}\in\mathbb{R}^{n_{x}}\) are the state and control input of robot \(m\) at the time \(k\) respectively. \(\mathbf{N}_{m,k}^{noise}\in\mathbb{R}^{n_{x}}\) is an unknown disturbance with Gaussian probability distribution. Considering the disturbance level in the real environment, \(N_{m,k}^{noise}\) is set to \(diag(0.1\text{ m},\text{ }0.1\text{ m},\text{ }0.5\text{ deg})\). What is more, the simulations are carried out under different level disturbances. Related simulations are in Section 5.1.
## 3 Performance Evaluation
### Off-Body Access Point Network Architecture  
For off-body access point experiments, five EDs (ED1-ED5) were placed on a 172 cm, 73 kg male subject as shown in Figure 3. The experiments were conducted in an indoor environment depicted in Figure 4. AP1, AP2, AP3 and AP4 mark four EDs placed on the walls. The host computer was placed on the desk near AP1 and connected to AP1 via a USB cable. AP2, AP3 and AP4 acted as three additional optional antennas of the AP. This was implemented by fixing the link costs from AP1 to AP2-4 to a constant zero. This means that routing data from ED1-5 to AP1 is possible by routing to AP1, AP2, AP3 and AP4. Since three nodes were used as AP antennas, the number of on-body nodes was reduced from eight to five.  
**Figure 3.** Experimental setup for network architecture with access point off the body. Note that the drawing assumes a single antenna at the AP, but up to four antennas are considered.  
The experimental setup was used to implement the routing algorithm in real-time in response to the changing channel conditions using various values of \(M\) and varying number of antennas at the AP. Four experiments were conducted. In the first, AP2-4 were not used (single antenna at the AP). In the second AP3 was added (two antennas at AP) and so on for AP2 and AP4. In each experiment, the subject walked around a room depicted in Figure 4, while implementing the data gathering procedure described in Section 3.2.
## 5 Discussion
### Arabic Sentiment Analysis
First, Arabic data augmentation could be used to enlarge the training datasets and improve the overall performance. Second, a hybrid system to represent Arabic text at the character level and word level may assist in more recognition improvements. Third, the challenging problem of multi-label text categorization could be addressed for future directions to better understand the Arabic language. Fourth, the sentiment analysis should also address the issue of neutrality or ambivalence as well.
# A Survey on Open Radio Access Networks: Challenges, Research Directions, and Open Source Approaches
## 4 Projects, Activities, and Standards Related to Open RAN
### 5G, 3 GPP, and Open RAN
This is why the open framework network movement was started. To fulfill this necessity, NTT DoCoMo and other MNOs gathered to create the O-RAN Alliance that we explained in the previous section. Figure 7 summarizes the new additional functions and interfaces that the O-RAN Alliance provides to supplement the 3 GPP specifications. Figure 7: Comparison between 3 GPP and O-RAN network functions and interfaces. We will explain the functions and interfaces in Figure 7 in the next subsection. As an introduction to the following section, and to better understand the difference between 3 GPP and O-RAN specifications, Figure 8 shows how the functions and interfaces are positioned in the RAN architecture. We can see that the specifications that O-RAN Alliance provides are more detailed than the more generalized 3 GPP specifications. This is important, particularly in the multivendor environment. For example, the X2 interface becomes essential for multivendor networks to function seamlessly [16]. In addition, we know from the previous section that the 5G RAN needs to support SA and NSA deployment. The current 5G deployments are still NSA and it will be challenging to move to the future 5G SA RAN deployment if the X2 interface is not open [16]. Open RAN's multi-vendor RAN system dream also helps reduce costs for MNOs by introducing more software vendors and diversifying the supply chains. Despite that, Open RAN standards still need to comply with 3 GPP specifications because 3 GPP's specification is the universal standard for the 5G RAN technology. However, Open RAN's principles are not only applicable to the 5G RAN technology. The purpose of Open RAN movement is to define and build RAN solutions based on general-purpose, vendor-neutral hardware and software-defined technology in 2G, 3G, 4G, and 5G [16]. Even though Open RAN can be used for other cellular generations, the current O-RAN Alliance's standardization still focuses on 5G because it is the latest trend that the telecommunication industry and companies follow. We will introduce how Open RAN is deployed in 5G RAN in the next section about O-RAN Alliance architecture. How the interfaces and functional split defined by 3 GPP for 5G NR are integrated into the O-RAN Alliance architecture will be discussed in the following section.