esa-sceva/satcom-chunk-collection huggingface dataset:
# Real Cyclic Load-Bearing Test of a Ceramic-Reinforced Slab
## 1 Introduction
### Determination of the Load-Bearing Capacity  
In order to analytically obtain the load-bearing capacity of a ceramic-reinforced slab, the geometry and strength of components is obviously needed. Assuming that the analysis of these slabs is not easy, the load-bearing capacity may be approximated by using the concept of \"homogenized section\". This method allows for calculating the position of the neutral axis of the cross section by converting all components as if they were only one and thereby calculating the maximum moment under bending. Assuming that both ceramics and concrete only work under compression, the following distribution of  
Figure 1: Different types of concrete solutions for typical Spanish “ceramic slabs”.  
forces can be established in the cross section by considering that deformation along the section always remains plain, as shown in Figure 2.  
The position of the neutral axis (N.A., or axis corresponding to zero deformation, where components turn from being compressed to being tensioned) is determined by a simple balance of forces. \(F_{\text{c1}}\) and \(F_{\text{c2}}\) in Figure 2, corresponding to the capacity under compression of the upper steel bars and the capacity under compression of the upper layer made of concrete and ceramics, respectively, must be equal to \(F_{\text{t1}}\), which is the capacity under tension of the lower steel bars only (since concrete is cracked in that area). Then, by knowing the position of the neutral axis (N.A. in Figure 2), it is possible to calculate the value of these forces and finally obtain the maximum resisted bending moment by multiplying one of these forces by the distance \"\(d\)\" between them (see Equation (1)).  
\[M_{Rd}=F_{t1}\cdot d \tag{1}\]  
Each of these forces may be obtained by multiplying the corresponding area of each component by its capacity under tension or compression, respectively.  
In order to obtain the capacity under shear of these slabs, and assuming that they are not usually reinforced with transverse reinforcements, the shear capacity of concrete will be taken into consideration only according to the following expression:  
\[V_{Rd}=\tau\cdot b_{0}\cdot Z \tag{2}\]  
where \(b_{0}\) is the width of available concrete area in the cross section, adding all vertical ribs and subtracting the existing holes; \(Z\) is the distance between the compression and tension final resulting forces (as an approximation, it could be taken as 90% of the thickness of the slab), and \(\tau\) is the shear strength of concrete, which is generally limited to 0.3 MPa.  
It is worth pointing out that these slabs sometimes included inclined reinforcements along the nerves to absorb part of the shear stresses. Re-bars must be placed vertically or inclined in the cross section in order to be considered against shear. This allows an important increment of the shear capacity without need of extra material.
## 4 Discussion
### Experimental UTM Environment  
The chosen TCP/IP architecture allowed the representation of a prototypical UTM network and offered the necessary flexibility to test the interaction between several real and/or simulated traffic participants. The MQTT protocol enabled compact messages so that all needed UTM functions could be implemented with a bandwidth of a few kB/s in the selected use case. In experiment V5, a failure of the computer running the MQTT broker inevitably led to the shutdown of the UTM network and thus the loss of communication to all UTM traffic participants. However, ADS-B data could still be processed on board the UAVs. MQTT detects client disconnections, but switching to an alternative in the event of a broker failure has not yet been provided for. A redundant design of the UTM server component should be aimed for in future projects in order to guarantee the functionality of the network even in the event of partial system failures.
# Enhancing the Iterative Smoothed Particle Hydrodynamics Method
## 3 The Iterative Approximation  
### The Iterative Formulation (ISPH)  
Let \(\mathbf{f}\) be the vector collecting the function values at the data in \(\Xi\), \(\mathbf{k}(\mathbf{x})\) the vector with the kernel values at \(\Xi\) referring to the evaluation point \(\mathbf{x}\), \(\mathbf{\Omega}\) the diagonal matrix with the significant entries equal to the measures \(d\Omega_{j}\), the SPH approximant (2) can be expressed as  
\[f_{h}(\mathbf{x})=\mathbf{k}(\mathbf{x})\mathbf{\Omega}\mathbf{f}. \tag{5}\]  
By assuming  
\[f_{h}^{(0)}(\mathbf{x})\equiv f_{h}(\mathbf{x}) \tag{6}\]  
the sequence \(\{f_{h}^{(n)}(\mathbf{x})\}\) is provided by iterating on the residuals.  
Let \(\mathbf{A}\) a matrix with entries \(\mathbf{A}_{i,j}=\mathsf{K}(\xi_{i},\xi_{j};h)d\Omega_{j}\), \(i,j=1,...,N\), and  
\[\mathbf{s}^{(0)}=\mathbf{f}-\mathbf{A}\mathbf{f}\]  
for \(n\in\mathcal{N}\), \(n>0\) the term of the succession is obtained as  
\[f_{h}^{(n)}(\mathbf{x})=f_{h}^{(n-1)}(\mathbf{x})+\mathbf{k}(\mathbf{x}) \mathbf{\Omega}\mathbf{s}^{(n-1)}=\mathbf{k}(\mathbf{x})\mathbf{\Omega}\sum_ {k=0}^{n}(\mathbf{I}-\mathbf{A})^{k}\mathbf{f} \tag{7}\]  
and  
\[\mathbf{s}^{(n)}=\mathbf{s}^{(n-1)}-\mathbf{A}\mathbf{s}^{(n-1)}.\]  
For \(n\rightarrow\infty\), the summation in (7) approximates \(\mathbf{A}^{-1}\) if and only if \(\|\mathbf{I}-\mathbf{A}\|_{2}<1\)[20]; therefore, under this condition the \(\{f_{h}^{(n)}(\mathbf{x})\}\) converges to the interpolant \(P_{h}(\mathbf{x})\)The solution for \(P_{h}(\mathbf{x})\) is admitted with strictly definite positive kernel functions on pair-wise distinct data sites and this is the only assumption for the data location [21, 22]. It is not necessary to ask for evenly spaced data in the construction of \(\{f_{h}^{(n)}(\mathbf{x})\}\).
# Performance Evaluation of UAV-Enabled LoRa Networks for Disaster Management Applications
### Motivation and Contribution  
The integration of UAVs and LPWAN protocols in disaster scenarios may offer a new cost-effective and energy-efficient way of tackling many problems arising during the operations of the rescuers. For instance, consider a forest in which a wildfire has been detected. Wildfires generally represent a worldwide problem, but America and Europe are the two continents where the incidence of forest firesis higher. The costs, in terms of economic losses and human deaths, are also considerable [13]. Wildfires typically affect rural or suburban areas where the network coverage of traditional communication networks (e.g., cellular networks) is scarce, intermittent or completely lacking. This situation is worsened by the huge propagation loss introduced by foliage and trees in the signal propagation path. In this situation UAVs can be used to establish a mesh network acting as a relay between the Base Station (BS) installed in the command post, the place where operations are managed, and the sensors carried by firemen, therefore providing more precise situational awareness to the rescuers. The mobility of the firefighters represents another huge challenge since the UAVs mesh network needs to adjust its position while maintaining the connectivity to the BS and to firemen on the ground. Unfortunately, the related literature on such works is still scarce, leaving many issues open to further investigation. In this work, our objective is to design and evaluate a UAV mesh network system, called UAV-enabled LoRa network. In this regard, the distinguishing aspects of our work and a brief comparison with the state-of-the-art solutions are given in Table 1. The main contribution of this work can be summarized as follows:  
* A novel UAV-enabled LoRa network architecture is proposed for the disaster management network. The proposed solution is easy to deploy and does not incur much cost for the network.
* A decentralized topology control algorithm for the UAVs has been provided. The proposed topology control algorithm allows UAVs to preserve connectivity with the BS, despite the mobility of the Ground Nodes (GNs).
* Exhaustive performance evaluation of the UAV-enabled LoRa network has been performed in ns-3. The selected QoS metrics such as average packet reception rate, average buffered packets, and total delay have been used to assess the performance of the network.
# Hybrid Deep Learning Model for Cataract Diagnosis Assistance
## 7 Conclusions  
In this study, a hybrid model based on deep learning is proposed to assist cataract diagnosis. By introducing the SE module and the prototype network classifier, the feature extraction and classification capabilities are further enhanced on the basis of ResNet50. Experimental results show that the proposed model has achieved excellent performance in the cataract retinal image recognition task, with an accuracy of 0.9875, an AUC value of 0.9984, and an F1 score of 0.9855, which verifies the effectiveness of the model in auxiliary diagnosis. At the same time, the ablation experiments show that the SE module and the prototype network significantly improve the performance of the model, and these characteristics enhance the generalization ability of the model to new samples.  
With the aging of the global population, the incidence of cataracts is increasing year by year. The model developed herein can effectively support doctors to make rapid and accurate diagnosis under the condition of limited resources, and improve the efficiency and coverage of cataract screening. However, future research still needs to further improve the diversity and interpretability of the model to adapt to the differences between different populations, the left and right eyes, and the need for model transparency in the medical field. By being deployed in a clinical setting, the model constructed in this study is expected to provide effective support in actual diagnosis.  
Conceptualization, Z.F. and K.X.; Methodology, K.X.; Software, K.X.; Validation, K.X., L.L. and Z.F.; Formal analysis, Z.F.; Investigation, K.X.; Data curation, L.L.; Writing--original draft, K.X.; Writing--review & editing, K.X., L.L. and Y.W.; Visualization, K.X.; Supervision, Z.F. and Y.W. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (No. 12162020), the Young Scholars Science Foundation of Lanzhou Jiaotong University (No. 2020022), the Sichuan Provincial Natural Science Foundation (No. 2023 NSFSC0428), the Central Government Funds of Guiding Local Scientific and Technological Development (No. 2023 ZYD0004), the Sichuan National Applied Mathematics Center open fund (No. 2024-KFJJ-01-01), the Sichuan Province Science and Technology Innovation Seedling Engineering Cultivation Project (No. MZGC20240034), and the open fund for Key Laboratory of Numerical Simulation of Sichuan Provincial Universities (No. KLNS-2024 SZFZ001).  
Not applicable.  
The datasets were collected from publicly available sources and can all be found within the article, and further inquiries can be directed to the corresponding authors.  
The authors sincerely thank the referees for their valuable comments.  
The authors declare no conflicts of interest.
## Appendix A
The acteoside demonstrated more beneficial interactions with PHE86, ASN87, ASP88, ASN196, ASP198, GLY199, ILE233, and GLN234 via hydrogen bonds with a binding affinity of \(-7.3\) kcal/mol. It also holds alkyl interactions with the ILE197. It is surrounded by hydrophobic residues (LEU54, LYS195, THR236). The toxicity class was also identified as V. The verbascoside interacts with ASN87, ASP88*, ASN196, ASP198, GLY199, ILE233, and GLN234 via carbon-hydrogen* bond with a binding affinity of \(-7.1\) kcal/mol. It also held pi-alkyl interaction with LEU54 and PRO272, as well as pi-sigma bonding with THR236, and was surrounded by the hydrophobic residues (GLN52, PRO85, and ILE235). The predicted toxicity class of verbascoside was V. The standard drug ceftaxone interacted with ASP88, ASN87, GLN115, ASN156*, ASP198, GLY199, GLY232, GLN234*, and ILE233 by hydrogen, carbon-hydrogen, and pi-alkyl bonds. It was also surrounded by hydrophobic residues ASN196 and ILE197, and the predicted toxicity class was VI. Even though the standard drug ceftaxone interacted with the crucial residues GLN234* by carbon-hydrogen bond, the binding affinity was \(-6.3\) kcal/mol. Moreover, the compounds kanzonol V, progeldanamycin, and rhodoxanthin from _H. cordata_ excellently inhibit the RBD domain compared with the other tested phytocompounds (Figure 5D-L,N-P). Kanzonol V strongly interacted with GLU484 and TYR449 by hydrogen bond and showed pi-alkyl interactions with PHE456 and TYR489. Kanzonol V is surrounded by hydrophobic residues LEU452, LEU455, PHE490, and LEU492. The binding affinity is \(-6.8\) kcal/mol. It belonged to the toxicity classes of V. Progeldanamycin has hydrogen bond interactions with TYR449, and SER494, as well as pi-sigma interaction with LEU452, and is also surrounded by the hydrophobic residues ASN450, PHE490, LEU492, and GLN493. The observed binding affinity is \(-6.4\) kcal/mol with the toxicity class IV. Rhodoxanthin shows pi-alkyl interactions with LEU452, TYR489, and PHE490 and was surrounded by the hydrophobic residues ARG346, SER349, TYR351, ASN450, LEU455, PHE456, GLU484, GLY485, PHE486, LEU492, GLN493, and SER494 with the binding affinity of \(-7.5\) kcal/mol. The toxicity class was noticed as VI. However, the ceftaxone did not interact with the specifically defined residues L452R, T478K, and E484Q in the Indian delta variant, and the binding affinity was observed as \(-6.5\) kcal/mol. Figure 4: _Cont._  
Figure 4: The docking pose of the mutated NTD with the most effective phytocompounds is based on the binding affinity and interacting residues (**A,D,G**). The docking poses with ceftaxone, acetoside, and verbascoside, respectively (**B,E,H**). The hydrophobicity of the interacting residues (brown (\(\uparrow\) hydrophobicity)-blue (\(\downarrow\) hydrophobicity), (**C,F,I**). The type of bonds involved in interacting phytocompounds with the mutated NTD residues (**J–L**). The Ligplot interaction for the phytocompounds docked with the mutated NTD residues. Figure 5: _Cont_.
# A Novel Fractional Fourier Transform-Based ASK-OFDM System for Underwater Acoustic Communications
## 5 System Model
### Spectral Efficiency and Data Rate  
Two important parameters considered for performance evaluation of communication systems are the spectral efficiency and data rate particularly for UWA, where the data rate represents a great challenge due to the limited bandwidth.  
The bandwidth for QAM/PSK systems is \(B_{Q}=\frac{N+1}{T}\), where for ASK OFDM systems the bandwidth is \(B_{A}=\frac{N+3}{2T}\)[5]. For QAM/PSK OFDM systems, the spectral efficiency is calculated as \(\alpha_{Q}\)[18]:  
\[\alpha_{Q}=\frac{T}{T+T_{g}}\frac{N_{d}}{N+1}\log_{2}(M) \tag{10}\]  
where \(T_{g}\) is the guard time, and \(N_{d}\) is the number of effective data subcarriers. For ASK-OFDM, the spectral efficiency is given by \(\alpha_{A}\):  
\[\alpha_{A}=\frac{2T}{T+T_{g}}\frac{N_{d}}{N+3}\log_{2}(M) \tag{11}\]  
Thus, the data rate for QAM/PSK OFDM systems is calculated as \(\alpha_{Q}B\), and for ASK-OFDM is \(\alpha_{A}B\). As can be seen, for the same parameters and modulation order, the spectral efficiency of ASK-OFDM will be approximately double the corresponding spectral efficiency of QAM/PSK OFDM systems.  
Figure 3: ASK FrFT-OFDM System model. A schematic diagram for ASK-OFDM based on FrFT for UWA communication.
# QoS-Aware Scheduling Algorithm Enabling Video Services in LTE Networks
## 3 LTE Schedulers
### Modified Largest Weighted Delay First (MLWDF) Scheduler  
This can be defined as a channel-conscious algorithm that supports numerous data users with a variety of QoS needs. MLWDF generally considers fairness and delay, as well as system throughput assurance. The handling of real-time and non-real-time flows is done differently. The PF with regards to real-time flow and non-real-time flow employing a weighing scale can be presented as follows:  
\[M_{i,k}^{M-LDWF}= \alpha_{i}\text{ }D_{HOL,i}*M_{i,k}^{PF}, \tag{2}\] \[\alpha_{i}= -\frac{log\text{ }log\text{ }\delta_{i}}{\tau_{i}}\]  
Here, \(D_{HOL,i}\) signifies the head of line (HOL) packet delay pertaining to the user \(i\) at a time \(t\). The packet delay threshold is denoted as \(\tau_{i}\), which takes into account each real-time flow. \(\delta_{i}\) specifies the highest potential impact caused by the HOL packet delay on the user \(i\) that would be greater than user's \(i\) delay threshold. It should be noted that the HOL packet is regarded to be the time contrast that would exist between the packet's arrived time and the present time [23].  
Figure 1: LTE DL packet scheduler general model.
# Dynamic Spatial-Temporal Self-Attention Network for Traffic Flow Prediction
## 4 Methodology
### Spatial-Temporal Self-Attention Layer
#### 4.2.1 Feature Augmentation Module  
Traffic data are highly complex, and different features have varying influences on the traffic flow. In order to comprehensively model the intrinsic correlations among the features in the traffic data, inspired by the channel attention mechanism [63], this research utilises a feature augmentation module to enhance the interactions between different feature dimensions of the traffic data. This enables the model to learn the importance of different feature dimensions automatically.  
Figure 3 depicts the schematic diagram of the feature augmentation module. The input data are compressed using a multilayer perceptron (MLP) with a single hidden layer. Then, a single activation process is applied to determine the importance and correlation of each feature dimension with respect to the crucial information. Subsequently, the temporal features of the traffic data are enhanced through the concatenation of two one-dimensional convolutions. This process strengthens the interaction of information in the traffic data across both spatial and temporal dimensions.  
Given the embedding vector \(\mathbf{\mathcal{X}_{emb}}\in R^{T\times N\times d}\) of the input data, the feature augmentation process is formally represented as follows:  
\[\mathbf{\mathcal{X}_{MLP}}=\sigma(\delta(\mathbf{W}_{1}\mathbf{\mathcal{X}_{emb}}+\mathbf{b}_{ 1}))\odot\mathbf{\mathcal{X}_{emb}} \tag{3}\]  
\[\mathbf{\mathcal{X}_{Agg}}=\sigma(\mathbf{\mathcal{K}_{k}}\star\rho(\mathbf{\mathcal{K}_{ k}}\star\mathbf{\mathcal{X}_{MLP}}(t)))\odot\mathbf{\mathcal{X}_{MLP}}(t) \tag{4}\]  
Figure 3: The feature augmentation module.  
Figure 2: Spatial–temporal self-attention layer.  
where \(\odot\) denotes the Hadamard product, \(\sigma(\cdot)\) is the _sigmoid_ activation function, and \(\rho(\cdot)\) is the _ReLU_ activation function, \(\mathbf{W}_{1}\), \(\mathbf{b}_{1}\) are the learnable weight parameter matrix and bias matrix, respectively, \(\mathbf{\mathcal{K}}_{\mathbf{k}}\) is the convolution kernel, and \(\star\) denotes the convolution operation. \(t\) represents the time step and \(\mathbf{k}\) denotes the dimensions of \(\mathbf{\mathcal{K}}_{\mathbf{k}}\).
# Combinatorial Olfactory Signaling in Short-Distance Determines Host Plant Recognition in Locust
## 2 Materials and Methods
### Chemical Analysis  
Odor compositions from a short distance were analyzed for host and nonhost plants by gas chromatography-mass spectrometry. Characteristic volatiles were categorized into three groups: host-specific, nonhost-specific, and commonly shared (see Table S1, Figure S1A-C). We also expanded the panel of test compounds used in this study, comprising two host-specific volatiles ((\(E\))-2-pentenal and 2-methyl-2-pentenal), two nonhost volatiles (heptanal and 2-octanone), and two common chemicals ((\(E\))-2-hexenal and pentanal) [31; 32; 33; 34; 35; 36; 37; 38; 39]. The procedure details are as follows. A gas chromatograph (GC) (SHIMADZU 2010 plus) was used to quantify volatiles in the SPME samples. The GC was equipped with a non-polar DB-1 MS column (30 m \(\times\) 0.25 mm inner diameter \(\times\) 0.25 \(\upmu\)m Rice Film Thickness, Agilent Technologies). The GC furnace temperature on the DB-1 MS column was maintained at 40 \({}^{\circ}\)C for 5 min, then increased to 240 \({}^{\circ}\)C at the rate of 5 \({}^{\circ}\)C/min. The injector temperature was maintained at 250 \({}^{\circ}\)C, and the nitrogen flow rate was constant at 1.0 mL/min. The fibers were inserted into the inlet operating in the forkless injection mode and held for 15 min. By comparison of the retention time with synthetic reference materials on the same column, the volatile compounds were identified.
## 2 Related Works
### The HEED Protocol  
The Hybrid Energy-Efficient Distributed Clustering (HEED) protocol [22], proposed by [PERSON] and [PERSON], is a multi-hop clustering protocol which provides energy-efficient clustering. Unlike the LEACH protocol that randomly selects nodes as CHs, the HEED selects the CHs based on residual energy and intra-cluster communication cost. One of the main ideas of HEED is to achieve a balanced distribution of CHs throughout the network. Moreover, the probability of two nodes within each other's communication range becoming CHs at the same time is very small in the HEED protocol. Initially, _C_prob, a percentage of CHs among all nodes, is set to assume that an optimal percentage cannot be computed. The probability of which a node becomes a CH is:  
\[CH_{prob}=C_{prob}\dfrac{E_{residual}}{E_{max}} \tag{2}\]  
where _E_residual is the estimated current energy of the node, and _E_max is a reference maximum energy, which is equal for all nodes. The value of _CH_prob is not allowed to be less than a certain threshold and the threshold is inversely proportional to _E_max. After that, each node executes several iterations to find the CH. On the other hand, CHs forward data to the BS using a multi-hop communication scheme [23].
## 3 Results
### Investigation of Novel Drug-Disease Pairs
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Chemical Name**} & **Drug** & **Disease** & **Disease** & **Functional Similarity Score** \\  & **(Drug Bank IDs)** & **Name** & **(MESH IDs)** & **Functional Similarity Score** \\ \hline Anileridine & DB00913 & Nausea & D009325 & 1.00 \\ Antipyrine & DB01435 & Osteoarthritis & D010003 & 1.00 \\ Bromocriptune & DB01200 & Depressive Disorder, & D003865 & 1.00 \\ Levallorphan & DB00504 & Nausea & D009325 & 1.00 \\ Oxprenolol & DB01580 & Tachycardia, & D013617 & 1.00 \\  & & Supraventricular & & \\ Paliperidone Palmitate & DB01267 & Depressive Disorder, & D003865 & 1.00 \\  & & Major & & \\ \hline Pergolide & DB01186 & Bipolar Disorder & D001714 & 1.00 \\ Salicylsalicylic Acid & DB01399 & Osteoarthritis & D010003 & 1.00 \\ Testosterone & DB00624 & Testicular Diseases & D013733 & 1.00 \\ Tocopherols & DB11251 & Testicular Diseases & D013733 & 1.00 \\ Triazolam & DB00897 & Depressive Disorder, & D003865 & 1.00 \\  & & Major & & \\ Tropicamide & DB00809 & Basal Ganglia & D001480 & 1.00 \\ Zinc & DB14533 & Depressive Disorder, & D003865 & 1.00 \\ Chloride & DB014533 & Major & & \\ Ergotamine & DB00696 & Basal Ganglia & D001480 & 0.96 \\ Ergotamine & DB00696 & Diseases & D003693 & 0.96 \\ Ergotamine & DB00696 & Delirium & D012563 & 0.96 \\ Loratadine & DB00455 & Panic Disorder & D016584 & 0.95 \\ Zopiclone & DB01198 & Epilepsy, Temporal & D004833 & 0.95 \\ Lornoxicam & DB06725 & Osteoarthritis & D010003 & 0.94 \\ \hline Isradipine & DB00270 & Bipolar Disorder & D001714 & 0.94 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The top 20 candidate drug–disease pairs. D001714), as shown in the second highlight. [PERSON] et al. investigated the potential use of isradipine in the treatment of bipolar depression [37]. Clinical trial information of isradipine in the treatment of bipolar depression can be found at www.clinicaltrials.gov (NCT01784666). Therefore, our identified candidate drug-disease associations might be useful in further pharmaceutical analysis of drug repositioning. The list of all predicted drug-disease associations identified using our method is presented in Supplementary Table S1.