esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Theoretical Background
### Knowledge Absorption  
To grow, develop, and be innovative, companies need knowledge. They can create it themselves or acquire it from the environment. The knowledge acquired should be used to create competitive advantage; thus its absorption is a crucial process. Hence, absorption capacity (also called diffusion) is considered a measure of the implementation of technological solutions. The absorption capacity most often concerns the implementation of new production processes, the use of new technologies, learning, etc.  
A company with higher absorptive capabilities possesses better learning abilities and foresees opportunities beyond its horizon [30]. Therefore, the concept of absorption capacity is considered one of the most important concepts to have emerged in the field of organizational research in recent years.  
There are many different definitions of absorption capacity in the relevant literature. In the initial studies, the absorption capacity of a company was treated in one-dimensional terms. Nowadays, researchers agree that this concept is multidimensional, although there is still no consensus as to how many dimensions it covers and what their specificity is [31].  
Nevertheless, as the complexity of the concept is broadly agreed on [32], many theoreticians in this area structure absorptive capacity dividing it into dimensions and components [33] where dimensions include acquisition, assimilation, transformation and exploitation of knowledge [34], as listed and defined below [35].  
1. Ability to acquire knowledge (Aac): understood as the ability to locate and identify sources, and also its valuation and acquisition as a basis for operational activities.
2. Ability to assimilate knowledge (Aas): understood as the ability to assimilate it, consisting of ordering, correct analysis, interpretation, and understanding.
3. Ability to transform knowledge (Att): understood as the ability to combine new knowledge with knowledge already possessed, through adding or eliminating specific components of knowledge or by interpreting knowledge in a new innovative way.
4. Ability to use knowledge (Aus): understood as the ability to incorporate knowledge into operational activities and improve processes and competences.  
Based on the structure of the concept, knowledge absorption requires an active approach, openness to interaction with business and scientific environments, and knowledge flows and diffusion within the company itself. Moreover, the findings from the study by [PERSON] et al. [35] show that firms' involvement in innovation collaborations with various external parties enriches their knowledge base [36] and develops a better ability to assimilate and exploit external knowledge [37], to facilitate the understanding of new concepts and effectively implement innovation [38].  
For the purposes of analyzing the absorption capacity, conceptual models of the conditions, components, and results of the absorption of knowledge, along with the relations between them, are very useful.
# Forewarning Model for Glacial Lake Outburst in Southeast Tibet
###### Abstract  
The southeast region of Tibet experiences frequent glacial lake outburst disasters, and disaster warning systems are thus crucial for disaster prevention and mitigation in the area. In this study, based on remote sensing images and historical data, 20 glacial lakes in southeast Tibet were selected as samples for risk analysis. A probability model of glacial lake outburst floods (GLOFs) in southeast Tibet was established using logistic regression for seven selected prediction indexes. By calculating the sensitivity and specificity of the model, the probability of identifying GLOFs was found to be 60%, with an identification degree of 86%. The under the ROC (receiver operating characteristic) curve index was prominently larger than 0.5, indicating the applicability of logistic regression for predicting GLOFs in southeast Tibet. The probability equation of the model shows that the area of the glacial lake, the distance of the glacial lake from the glacier, the slope of the glacier, the slope of the glacier tongue, and the dam backwater slope have a great influence on the probability of GLOFs. The results can provide a reference for the local governments to prevent disasters and reduce the damage of GLOFs.  
disaster warning; glacial lake outburst; logical regression; southeast Tibet +
Footnote †: journal: Applied Sciences  
1
Footnote 1: School of Science, Tibet University, Lhasa 850001, China  
2
Footnote 2: Tibet Institute of Plateau Atmospheric and Environmental Science, Lhasa 850001, China  
3
Footnote 3: Tibet Key Laboratory of Plateau Atmospheric and Environmental Science Research, Lhasa 850001, China  
4
Footnote 4: Tibet Climate Center, Lhasa 850001, China  
5
Footnote 5: Correspondence: [EMAIL_ADDRESS]
# Robust Clustering Routing Method for Wireless Sensor Networks Considering the Locust Search Scheme
## 4 The New Clustering Routing Approach
### Operation Step
#### 4.2.1 Determination of Optimal Cluster Heads  
In our scheme, the LS-II method identifies the set of CHs. In the identification, two main concepts are adopted: the residual energy of CH and the distance from a CH to the BS.  
The proposed scheme identifies a sensor node as CH when its residual energy is quite high in comparison with the rest of the sensors. According to the energy consumption model, cluster heads consume more energy than generical nodes. Therefore, sensor nodes that present the highest residual energy levels should be considered as CHs in order to balance their effects.  
Determining the optimal node structure allows saving energy. Under such conditions, the identification of CHs is achieved through a reduction of the distance among nodes. For this operation, it is considered that crosslinks are eliminated since they generate interference through the data transmission.  
Different from other schemes, the number of CH nodes is not represented by a fixed constant. Instead, the number of CHs is constantly modified in order to obtain the best network structure in each round. Likewise, the determination of the set of CHs is not random or probabilistic produced, such as in the DEEC or LEACH approaches. Instead of this, in our scheme, the LS-II technique automatically selects the best sensor nodes to become CHs in each round. This mechanism allows the use of the proposed scheme for a wide range of WSN applications without considering this constraint imposed for other protocols.  
Then, each sensor node is assigned to the closest cluster head once the optimal set of clusters has been determined. However, there is an exception: If the distance of the sensor node is shorter to the BS than the distance to the CH, then the sensor node is not assigned to this group. Therefore, the information from this node is transmitted to the base station. Once this process is finished, a candidate network structure is used for a transmission simulation in order to evaluate its performance. The process for determining the best network structure is discussed in the following section.
## 2 Methodology  
### Equations of Motion for a Structure under Multiple Support Excitation  
When a structure undergoes prescribed motions at its different supports, the total displacement vector \(\mathbf{x}^{t}\) of the degrees of freedom within the structure, excluding the supports, is decomposed into two parts [46]:  
\[\mathbf{x}^{t}\mathbf{=}\mathbf{x}^{s}\mathbf{+}\mathbf{x}. \tag{1}\]  
According to [PERSON] [46], \(\mathbf{x}\) and \(\mathbf{x}^{s}\) satisfy the following equations, respectively,  
\[\mathbf{M}\ddot{\mathbf{x}}\mathbf{+}\dot{\mathbf{C}}\mathbf{x+}\mathbf{K} \mathbf{x}\mathbf{=}-\mathbf{M}\mathbf{\ddot{x}}\mathbf{\ddot{x}}, \tag{2}\]  
and  
\[\mathbf{x}^{s}\mathbf{=}\mathbf{\Gamma}\mathbf{x}^{\mathbf{g}}, \tag{3}\]  
where \(\mathbf{M}\), \(\mathbf{C}\), and \(\mathbf{K}\) are the mass, damping, and stiffness matrices, respectively; \(\mathbf{\Gamma}\) is the influence matrix that describes the impact of prescribed support displacement vector \(\mathbf{x}^{g}\) on the structural displacement.  
Equation (2) can be discretized to the following state equation in terms of state variable \(\mathbf{z}\mathbf{=}(\mathbf{x}^{\mathbf{T}}\mathbf{\dot{x}}^{\mathbf{T}})^{ \mathbf{T}}\)[47]:  
\[\mathbf{z}_{k+1}=\mathbf{A}\mathbf{z}_{k}+\mathbf{B}\ \mathbf{f}_{k}, \tag{4}\]  
where \(\mathbf{z}_{k}\mathbf{=}\mathbf{z}(k\Delta t)\), \(\mathbf{f}_{k}=-\mathbf{M}\mathbf{\ddot{x}}\mathbf{\ddot{x}}(k\Delta t)\), and \(\Delta t\) is a time increment. In employing Equations (1)-(3), the observation equation for the total acceleration responses at measured degrees of freedom is expressed as  
\[\mathbf{y}_{k}=\mathbf{E}\mathbf{z}_{k}, \tag{5}\]  
where \(\mathbf{E}=\mathbf{L}\big{[}-\mathbf{M}^{-1}\mathbf{K}\quad-\mathbf{M}^{-1} \mathbf{C}\big{]}\), and \(\mathbf{L}\) is an observation matrix of measured degrees of freedom, with components equal to 0 or 1.  
In the case of uniform base excitation, \(\mathbf{\Gamma}\) becomes a column vector with all components equal to 1, and \(\mathbf{x}^{8}\) has only one component. In such instances, relative acceleration responses are commonly employed. As a result, Equation (5) is adjusted to  
\[\mathbf{y}_{k}=\mathbf{E}\mathbf{z}_{k}+\mathbf{D}\mathbf{f}_{k}, \tag{6}\]  
where \(\mathbf{D}=\mathbf{L}\mathbf{M}^{-1}\).  
Equations (4) and (6) form the discrete-time deterministic state-space model that serves as the foundation for the proposed wavelet-based subspace identification approach in this study. It is assumed that the structure has a total of \(\overline{N}\) degrees of freedom, with \(\overline{m}\) of them being measured. As a result, vectors \(\mathbf{z}_{k}\) and \(\mathbf{y}_{k}\) have \(2\overline{N}\) and \(\overline{m}\) components, respectively.
# Aerial Tele-Manipulation with Passive Tool via Parallel Position/Force Control +
## 4 Stability Analysis
### Stability of Teleoperator in Contact Interaction  
During the contact interaction, from (4) we can define the dynamics of the master robot and its controller in the frequency domain as: \(G_{r}(s)=M_{m}s^{2}+K_{mD}s,\ C_{m}(s)=K_{mP}\). The haptic feedback in the master side (PD term) could also be expressed as: \(C_{h}(s)=K_{hP}+K_{hD}s\). We have shown that the force interaction dynamics and force controller in the remote side could be expressed as: \(G_{s}(s)=diag(M_{f}s^{2}+B_{f}s+K_{f})^{-1},\quad C_{m}(s)=K_{2}+K_{1}\frac{1} {s}\). We define the transfer function of the master robot and its controller as: \(G_{m}(s)=C_{m}G_{r}(I+C_{m}G_{r})^{-1}\). The internal stability of the system requires that the roots of the denominator of \(G_{m}\) all have real negative parts, for which \(K_{mP},K_{mD}>0\) suffice. The overall transfer function of the master side, with force input and position output, is defined as:  
\[G_{1}(s)=G_{m}(I+G_{m}C_{h})^{-1}. \tag{40}\]  
The characteristic polynomial of \(G_{1}\) is \((M_{m}K_{mP}K_{hD})s^{3}+(M_{m}K_{mP}(1+K_{hP})+K_{mP}K_{mD}K_{hD})s^{2}+(K_{ mD}K_{mP}K_{hP}+K_{mP}K_{mD})s+1\). For internal stability, the controller coefficients must conform with the following constraint:  
\[k_{mD}k_{m}p_{khp}+k_{m}k_{mD}>\frac{\sigma_{max}(M_{m})k_{hp}}{\sigma_{min}(M _{m})(1+k_{hp})+k_{mD}k_{hD}}. \tag{41}\]  
The transfer function of the slave side, with force input and force output, is obtained as: \(G_{2}(s)=G_{s}C_{s}(I+G_{s}C_{s})^{-1}\). Its internal stability constraints are expressed by (34) and (39). The teleoperator output \(f\) (see Figure 6), can be obtained as:  
\[f(s)=G_{2}K_{f}^{f}G_{1}(I+G_{2}K_{f}^{f}G_{1}K_{f}^{b})^{-1}f_{h}(s)\] \[+(I+G_{s}C_{s})^{-1}h(s). \tag{42}\]The constraint (34), (39) and (41) gives all the poles of \(G_{1}\) and \(G_{2}\) a negative real part; therefore, \(G_{1}\) and \(G_{2}\) are strictly positive real and thus passive systems, and the negative feedback interconnection of a passive system is a passive system [32]; thus, (42), that is, the teleoperator in the contact interaction, is stable.
## 2 Materials and Methods
### Microorganisms and Enzyme  
A total of four strains of filamentous fungi were used in the study. The two Zygomycetes strains included _Rhizopus oryzae_ CCUG 28,958 and _Mucor indicus_ CCUG 2242 (Culture Collection University of Gothenburg, Gothenburg, Sweden). Two Ascomycetes strains included _Neurospora intermedia_ CBS 131.92 and _Aspergillus oryzae_ CBS 819.72 (Centraalbureau voor Schimmelcultures, Utrecht, The Netherlands). All strains were maintained on Petri dishes with potato dextrose agar (PDA; BTL, Lodz, Poland) medium (20 g/L glucose, 4 g/L potato extract, 15 g/L agar) at 4 \({}^{\circ}\)C until use. Fresh PDA plates were prepared by incubation of spore suspension at 30 \({}^{\circ}\)C for 3-5 days. Fungal spore inoculums were prepared by flooding fresh plates with 20 mL sterile distilled water and releasing the spores with an L-shape glass sterile spreader. The spore count in the spore suspensions were determined microscopically using Burker chambers. Commercial \(\alpha\)-amylase preparation (Liquozyme SC DS) with declared activity of 240 kilo novo units per g was donated by Novozymes (Bagsverd, Denmark). According to the enzyme manufacturer's recommendations, the dosage of the preparation should range 0.013-0.025% \(w/w\) for corn grain processing. For the purpose of this study, an average dosage of 0.019% \(w/w\) was assumed.
## 5 Evaluation and Results
### Access Outcome Analysis and Performance Metrics
#### 5.2.4 Performance Metrics  
We report key performance metrics, including latency and throughput, to evaluate the system's operational feasibility. These results demonstrate the responsiveness, scalability, and technical robustness of the ABAC framework under simulated enterprise conditions.  
Latency Observations:  
* Min Latency: 28 ms
* Max Latency: 61 ms
* Avg Latency: \(\sim\)39.7 ms  
Throughput Observations:  
* Over 100 tx/min
* No failures; TLS securely maintained  
Performance Drivers  
* Docker local execution minimized latency
* Modular smart contract structure
* Sequential batch processing
## 6 Algorithm Evaluation  
To emulate the measurement data, the simulated \(E\)-field vector radiated by the antennas presented in Section 3 was sampled at the location of a virtual EUmmWVx-probe. The sampled field vector was then projected on the plane of sensors integrated in the EUmmWVx-probe, meaning that the components characterized by the field probe were correctly extracted from the simulations. Next, the amplitude was squared, thereby removing phase information, as would be the case for real measurements. To model measurement noise, Gaussian noise was added to the squared \(E\)-field amplitudes on the emulated sensors; the standard deviation of this noise was quantified relative to the peak \(E\)-field amplitude over the whole measurement plane at 2 mm (in dB). For example, a noise level of \(-20\) dB means the standard deviation of the noise is 10% of the peak \(E\)-field signal. The investigated noise levels ranged from \(-30\) dB to \(-18\) dB, in addition to the case without noise.  
For the reference fields, the FDTD results including full phase information were used. The first and second measurement planes were placed at 2 mm and at 2 mm + 0.25 \(\lambda\) from the AUT plane, where the recommended plane separation distance was used from the PTP-algorithm [21]. The size of the measurement plane was chosen based on the results of Section 5 so that at least 99.5% of the power was captured by the plane, which led to plane sizes ranging from \(18\times 18\) to \(30\times 30\) samples. The grid step size was 0.25 \(\lambda\) (except for the 10 GHz antennas at 2 mm distance, where it was 0.15 \(\lambda\)).  
The PTP algorithm [21] yielded an initial estimation for \(\mathbf{M}_{x,0}\) and \(\mathbf{M}_{y,0}\). The location and dimensions of the third measurement plane were optimized by applying (2) on this initial estimate of \(\mathbf{M}_{x,0}\), \(\mathbf{M}_{y,0}\) to obtain a preliminary estimate of the field on the third plane (evaluated on a \(128\times 128\) grid). The location and dimension of the \"measured\" third plane were then chosen to cover the field up to \(-18\) dB of the peak of the preliminary estimate.  
The full reconstruction pipeline was run on these data, including reconstruction of the polarization ellipses by means of [28] from the \(E\)-field amplitude projected on the sensors, computation of equivalent surface currents at the measurement plane using an additional third measurement plane according to (4), and forward transformation to an evaluation surface by means of (2). Figure 5 shows the magnitude of the generated \(E\)-field on the first and third measurement plane used in (4) for different noise levels. FT results were compared to reference values extracted from simulation by means of (7). Finally, the mean and standard deviation over 20 simulations (corresponding to 20 different samplings of random noise) were computed.  
Figure 5: _Cont._
# Various Generative Adversarial Networks Model for Synthetic Prohibitory Sign Image Generation
## 2 Materials and Methods
### Least Squares Generative Adversarial Networks (LSGAN)  
The discriminator in LSGANs uses the least squares as its cost function [31; 32]. In other applications, LSGANs are used to generate samples that can represent the real data. There are two advantages of Least Squares Generative Adversarial Networks (LSGANs) over regular GANs. First, LSGANs can produce more extraordinary quality images than conventional GANs. Second, LSGANs perform more stably during the learning process [33; 34]. Training GANs is a complex problem in practice because of the instability of GANs' learning.  
Recently, research papers have pointed out that the uncertainty of GANs' learning is affected by the objective function [35]. In particular, decreasing the typical GAN objective functions can affect gradient loss problems, which makes it difficult to update the generator. This barrier can be relieved by LSGAN since the penalization of samples dependent on the boundary distances may create further gradients when the generator is modified. In comparison, training instability for standard GANs is focused technically on the method-searching action of the objective function, and LSGANs display fewer mode-seeking behaviors. The cost function of an LSGAN is shown in Formulas (4) and (5) [36].  
\[\underset{D}{min}V\ \text{LSGAN}\ (D)=\ \frac{1}{2}\ E\ \Big{[}(D(X_{real,i})-1)^{2} \Big{]}+\frac{1}{2}\ E\ \Big{[}\Big{(}D\Big{(}G(X_{fake,i})\Big{)}^{2}\Big{]} \tag{4}\]  
\[\underset{G}{min}V\ \text{LSGAN}\ (G)=\ \frac{1}{2}\ D\ \Big{[}\Big{(}G\Big{(}X_{fake,i} \Big{)}-1\Big{)}^{2}\Big{]} \tag{5}\]  
LSGANs can generate new data with high similarity to the original data through the mutual benefits of discriminator and generator in the model [37]. Therefore, this paper chooses LSGAN to augment the dataset and generate more realistic data.  
Figure 1: The generative network \(G\) (**left**) and the discriminative network \(D\) (**right**) topology.
# Mitigating the Scintillation Effect on GNSS Signals Using MP and ROTI
## 2 Materials and Methods
### Methodology
After that, a weight method dependent on satellite elevations is typically used:  
\[\mathrm{R}= \begin{bmatrix}\mathrm{P}_{w}&&&&\\ &\mathrm{L}_{w}&&\\ &&\ddots&\\ &&&\mathrm{P}_{w}&\\ &&&\mathrm{L}_{w}\end{bmatrix} _{\mathrm{2n}\times\mathrm{2n}} \tag{10}\]  
\[\mathrm{P}_{w}=\mathrm{P}_{w,0}\cdot\frac{1.001}{\sqrt{0.002001+\sin^{2}(\mathrm{ ele})}} \tag{11}\]\[\mathrm{L_{w}}=\mathrm{L_{w,0}}\cdot\frac{1.001}{\sqrt{0.002001+\sin^{2}(\mathrm{ ele})}} \tag{12}\]  
Next, the code and phase measurements were respectively weighted by MPF and ROTI or S4 and \(\sigma_{\phi}\) for down-weighting the signal affected by scintillation:  
\[\mathrm{R}= \begin{bmatrix}\mathrm{P_{w,scin}}&&&&\\ &\mathrm{L_{w,scin}}&&\\ &&\ddots&\\ &&&\mathrm{P_{w,scin}}\\ &&&\mathrm{L_{w,scin}}\end{bmatrix} _{2\mathrm{n}\times 2\mathrm{n}} \tag{13}\]  
\[\mathrm{P_{w,scin}}=\mathrm{P_{w}}\cdot\mathrm{MPF}\;\mathrm{or}\;\mathrm{P_{ w,scin}}=\mathrm{P_{w}}\cdot\mathrm{S4} \tag{14}\]  
\[\mathrm{L_{w,scin}}=\mathrm{L_{w}}\cdot\mathrm{ROTI}\;\mathrm{or}\;\mathrm{L_{ w,scin}}=\mathrm{L_{w}}\cdot\sigma_{\phi} \tag{15}\]  
where \(P_{w,scin}\) and \(L_{w,scin}\) were the weights for down-weighting the scintillation effect respectively on code and phase measurements. The proposal of these two weights is the primary novelty in the weight strategy proposed in this paper. After the weighting process, the same procedures as previous strategies were repeated for the weight strategy after inputting into PPPH.
## 3 Skin Lesion Experiments and Analysis
### Visual Comparison  
To further assess the effectiveness and robustness of our proposed FuzzyTransNet, this study conducted additional visual comparison experiments on representative images from the ISIC2018 dataset. Figure 5 presents visual comparisons of the segmentation results on three typical skin samples using the classic algorithms U-Net [6], U-Net++ [7], and TransFuse [32], as well as the proposed FuzzyTransNet. When compared to the ground truth, our algorithm's segmentation results exhibit greater similarity to the ground-truth images compared to the other algorithms. In the second row of Figure 5, it is evident that the three classic models exhibit more errors in segmenting fuzzy lesion boundaries. Conversely, the FuzzyTransNet model results in a clearer and more complete contour, underscoring the effectiveness of incorporating the fuzzy block for processing.  
To further substantiate the efficacy of our proposed algorithm, we employed the Grad-CAM (gradient-weighted class activation mapping) [37] technique for visualizing the class activation maps. Concurrently, we conducted a comparative analysis with representative medical image segmentation algorithms (U-Net [6], U-Net++ [7], and TransFuse [32]) to vividly demonstrate the advantages of our approach. The visual comparative results are presented in Figure 6. In the heatmaps in the first row, it can be seen that FuzzyTransNet exhibits a superior focus on lesion areas compared to the other models. In the second row of the segmentation results, it is evident that TransFuse [32] demonstrates less distinct attention to lesion boundaries, resulting in a less clear delineation of the lesion region. Conversely, our proposed method exhibits a more explicit emphasis on lesion boundaries, thus indirectly corroborating the effectiveness of the fuzzy block introduced in this study.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Index** & **Backbones** & **Sequential** & **Parallel** & **Fusion** & **ISIC2017** & **ISIC2018** \\ \hline E.1 & R34 + DeiT-S & ✓ & - & - & 0.849 & 0.852 \\ E.2 & R50 + DeiT-S & ✓ & - & - & 0.853 & 0.855 \\ E.3 & R50 + Swin V2 & ✓ & - & - & 0.859 & 0.862 \\ E.4 & R34 + DeiT-S & - & ✓ & ✓ & 0.872 & 0.869 \\ E.5 & R50 + Swin V1 & - & ✓ & ✓ & 0.874 & 0.875 \\ E.6 & R50 + Swin V2 & - & ✓ & ✓ & 0.877 & 0.879 \\ E.7 & E.6 + Fuzzy block & - & ✓ & ✓ & **0.884** & **0.887** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Ablation study on parallel-in-branch design. R34: ResNet34; R50: ResNet50; Swin V1: Swin Transformer; Swin V2: Swin Transformer V2; DeiT-S: DeiT-Small.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Index** & **R34 + DeiT-S** & **R50 + Swin V2** & **Fuzzy Block** & **Data Augmentation** & **Jaccard** & **Dice** & **Accuracy** \\ \hline TransFuse & ✓ & - & - & - & 0.791 & 0.868 & 0.942 \\ F.2 & - & ✓ & - & - & 0.797 & 0.879 & 0.951 \\ F.3 & - & ✓ & ✓ & - & 0.801 & 0.887 & 0.958 \\
**Ours** & - & ✓ & ✓ & ✓ & **0.801** & **0.889** & **0.961** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Component-wise analysis.