esa-sceva/satcom-chunk-collection huggingface dataset:
# Physical Vulnerability and Landslide Risk Assessment in Tegucigalpa City, Honduras
## 1 Introduction
### Research Background  
Most landslide hazard studies have focused on using statistical techniques to estimate landslide susceptibility--that is, the spatial probability of the occurrence of a landslide--with a recent trend toward the use of Machine Learning (ML) [14; 15; 16], as promising classification results have been obtained using ML algorithms such as the Maximum Entropy Model [17; 18]. Some authors argue that deterministic methods, based on physical laws controlling landslides, may be more suitable for estimating the spatial probability for limited regions such as watersheds and cities, as they can predict new landslides independently of historic inventories [3; 19]. However, the lack of data on soil mechanical properties and their spatial variability limits the application of these methods [3; 16]. Furthermore, there is a significant scarcity of studies estimating the probability of landslide magnitude _P(Mi)_, in particular for slow-moving landslides [2; 3; 15], and landslide vulnerability [2; 5; 14; 20]. This absence of methodologies for estimating intensity and vulnerability contributes to the overall scarcity of quantitative slow-moving landslide risk assessment studies. It can be concluded that there is currently no widely disseminated methodology or platform for quantitative estimation of the probable losses due to slow-moving landslides [14; 15; 16].
# A Hybrid Adaptive Particle Swarm Optimization Algorithm for Enhanced Performance
## 3 Improved PSO Algorithm
### Steps of the Improved Algorithm  
Step 1: Establish the initialization parameters for the enhanced algorithm, which include the particle swarm size designated as population size, the search dimension referred to as dimensions, the upper and lower bounds of the search denoted as upper bound and lower bound, the maximum number of iterations labeled as max iterations, and the maximum and minimum speeds represented as \(v_{max}\) and \(v_{min}\), respectively. The composite chaos initialization of the population particles is executed according to Equations (5) and (6).  
Step 2: Verify the boundary conditions and compute the fitness values based on the initialized positions and velocities of the individual particles. Subsequently, sort the fitness values and update the velocities and positions of the particles in alignment with Equations (2) and (4).  
Step 3: Organize the particle population into subsets based on the magnitude of the fitness values, categorizing 15% of the particles with the lowest fitness values as elite particles, 15% with the highest fitness values as inferior particles, and the remaining 70% as ordinary particles.  
Step 4: In the velocity Formula (4), dynamically adjust the inertia coefficients using the adaptive inertia weight Formula (4).  
Step 5: In the divided subset for the elite particles, apply Formulas (21) and (22) to update the particle velocity individual position, and for ordinary particles, and apply Formula (23) or (24) to update the particle velocity. Use the cross-mutation Formula (25) to obtain the adaptation value of the larger particles to mutate to produce a new individual, and use Formula (26) to calculate the position update of the mutated particles.  
Step 6: Assess the fitness of the particles by updating their velocities and positions. Based on the optimal position and fitness value of both the individual and the group, determine whether the specified conditions are met. If the conditions are satisfied, proceed to Step 7; otherwise, return to Step 6.  
Step 7: Present the optimal solution to the problem. The diagram illustrating the enhanced algorithm is presented below. See Figure 2 for details.
# Object Detection Based on Multiple Information Fusion Net
## 2 Related Work  
Context information: In real life, it is unlikely that an object can exist alone. Visual objects occur in particular environments and usually coexist with other related objects [43]. When the object's appearance feature is insufficient because of small object size, object occlusion, or poor image quality, a proper modeling of context will facilitate object detection and recognition task. Context information has been applied in many methods to enhance the performance of object detection [44; 45; 46; 47; 48; 49], which can be roughly divided into two categories [49; 50]: global information [32; 51] (refers to the image level or scene level information), local information [35; 36] (considers the object relationship or the interaction between the object and its surrounding area). It is proved that both the global and local context information have a positive impact on the object detection. Our proposed MIFNet has the capability of utilizing both global context (scene-level information) and local information (object relationship) to make the object's appearance feature richer.  
Attention mechanism: The attention mechanism in deep learning is inspired by the mode of human attention thinking and has been widely used in natural language processing [52]. In attention module, an individual element can be influenced by aggregating information from other elements and the dependency between elements is modeled without excessive assumptions on their locations and feature distributions. The aggregation weights can be learned automatically, which is driven by the task goal. Recently, attention mechanism has been successfully applied in vision problems [37; 53].  
Attention mechanism can be represented as follows:  
\[\text{Attention }(Q,\ K,\ V)\ =\ \text{softmax}\left(\frac{QK^{T}}{\sqrt{d}}V \right). \tag{1}\]where \(Q\), \(K\), \(V\) are three feature matrices with the same shape. In natural language processing, \(Q\), \(K\), \(V\) represent sets of queries, keys, and values, respectively. In our work, \(Q\) denotes the object feature, \(K\) denotes all surrounding object feature, \(V\) represents the all image feature (with object feature and location feature). In Equation (1), given a query \(q\in Q\) and all keys \(K\), the dot products of \(q\) with \(K\) will be calculated to get the similarity between them. Then we divide dot products by a scaling factor \(\sqrt{d}\) and the softmax function is applied to obtain the weights on all image feature (i.e., it can obtain the influence of each object on the current object). For more detailed information about the attention mechanism, the readers can refer to [38]. In our work, the attention mechanism is utilized to get the relationship between objects.
## 5 Experimental Results
### BDS Results
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Annealing Temperature [\({}^{\circ}\)C]**} & **Thermal Diffusivity** & **Thermal Conductivity** & **Thermal Effusivity** \\  & \(\alpha_{c}\) [cm\({}^{2}\cdot\)s\({}^{-1}\)] & \(\alpha_{c}\) [W\(\cdot\)cm\({}^{-1}\cdot\)K\({}^{-1}\)] & \(\alpha_{c}\) [W\(\cdot\)s\({}^{1/2}\)cm\({}^{-2}\)K\({}^{-1}\)] \\ \hline
25 & 2.55\(\cdot\)10\({}^{-3}\) \(\pm\) 0.1\(\cdot\)10\({}^{-4}\) & 2.99\(\cdot\)10\({}^{-3}\)\(\pm\) 2\(\cdot\)10\({}^{-4}\) & 5.92\(\cdot\)10\({}^{-2}\)\(\pm\) 0.03\(\cdot\)10\({}^{-3}\) \\
90 & 2.61\(\cdot\)10\({}^{-3}\)\(\pm\) 0.1\(\cdot\)10\({}^{-4}\) & 3.27\(\cdot\)10\({}^{-3}\)\(\pm\) 1\(\cdot\)10\({}^{-4}\) & 6.40\(\cdot\)10\({}^{-2}\)\(\pm\) 0.02\(\cdot\)10\({}^{-3}\) \\
120 & 2.65\(\cdot\)10\({}^{-3}\)\(\pm\) 0.1\(\cdot\)10\({}^{-4}\) & 3.32\(\cdot\)10\({}^{-3}\)\(\pm\) 2\(\cdot\)10\({}^{-4}\) & 6.45\(\cdot\)10\({}^{-2}\)\(\pm\) 0.03\(\cdot\)10\({}^{-3}\) \\
150 & 2.66\(\cdot\)10\({}^{-3}\)\(\pm\) 0.2\(\cdot\)10\({}^{-4}\) & 3.34\(\cdot\)10\({}^{-3}\)\(\pm\) 1\(\cdot\)10\({}^{-4}\) & 6.48\(\cdot\)10\({}^{-2}\)\(\pm\) 0.03\(\cdot\)10\({}^{-3}\) \\
180 & 2.73\(\cdot\)10\({}^{-3}\)\(\pm\) 0.2\(\cdot\)10\({}^{-4}\) & 3.56\(\cdot\)10\({}^{-3}\)\(\pm\) 1\(\cdot\)10\({}^{-4}\) & 6.81\(\cdot\)10\({}^{-2}\)\(\pm\) 0.03\(\cdot\)10\({}^{-3}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The thermal diffusivity, thermal conductivity, and thermal effusivity values of the investigated coatings obtained for different annealing temperatures with the use of the BDS method. Figure 14: The amplitude (**a**) and phase (**b**) dependence on the modulation frequency of the PBMA sample annealed at different temperatures. Figure 16: The relationship between the values of thermal conductivity determined by the BDS and PTR techniques. The filled circles are experimental data, and the solid line is a linear regression line. Figure 17: The relationship between the values of thermal diffusivity determined by the BDS and PTR techniques. The filled circles are experimental data, and the solid line is a linear regression line. Figure 15: The relationship between the values of thermal diffusivity determined by the BDS and PTR techniques. The filled circles are experimental data, and the solid line is a linear regression line.
## 4 Results and Discussion
### Breakthrough Time  
The evaluation of water entry into the middle layer during experiments, represented by breakthrough time, aimed to consider the increase in volumetric water content in the coarse layer. However, as previously mentioned, this increase could not be detected in the coarse sand and fine gravel. Contrastingly, in Figure 5, it is observable that as the grain size increases in the middle layer, the breakthrough time and the duration of the volumetric water content increase in the third layer converge. Consequently, the breakthrough time for these layers is considered the moment when the volumetric water content of the third layer starts to increase. The most prolonged barrier performance was observed when the medium sand was used in the middle layer of the cover (Figure 5).  
Figure 5: Breakthrough time for each soil column infiltration test.  
The coarser materials generally exhibit steeper soil water characteristic curves (SWCCs) due to their lower water retention capacity. Therefore, even small changes in the volumetric water content may result in significant shifts in matric suction. In soil column tests, as grain size increased in the middle layer, more water was retained near the interface (Figure 4). This condition could cause a faster and more substantial change in water potential at the interface, leading to a faster breakthrough in the fine gravel test. This finding is consistent with the results of [25]. It indicates that using medium sand in the test, rather than gravel in the coarser layer, is more effective in maintaining the barrier effect.
# Feasibility Analysis on the Use of Ultrasonic Communications for Body Sensor Networks
## 3 Method  
The ultrasonic communication system was designed in two parts. The transmission part includes the signal generator which generates digital signal, modulation, and ultrasonic transducer. Three principal digital communication modulation schemes were used in the experiments: amplitude shift keying (ASK), frequency shift keying (FSK), and phase shift keying (PSK). In this study, only binary modulation was considered. Binary ASK is in the term of on-off keying (OOK). The receiver part included the ultrasonic transducer as the receiver. The digital signal was modulated and then sent to the ultrasonic transducer. The received ultrasound signal was demodulated to the original digital signal. Finally, the bit error rate (BER) was calculated for evaluation of the three modulation methods. Figure 2 shows the architecture of the ultrasonic communication for BSNs.  
ASK is a form of amplitude modulation that represents digital signals as amplitude variations of the carrier wave. Binary ASK signaling is the simplest form of ASK with two symbol states. A zero-amplitude carrier transmission represents a 0, and a non-zero amplitude indicates a 1. Binary ASK signaling is also OOK. The general analytical expression for the modulated signal \(S_{i}(t)\) is  
\[S_{i}(t)=\sqrt{\frac{2E_{i}(t)}{T}}\cos(\omega_{0}t+\varphi)t\ \leq\ T;\ i=1,\ldots,M, \tag{4}\]  
where \(\mathbf{E}\) is the symbol energy, \(T\) is the symbol time duration, the amplitude term \(\sqrt{2E_{i}(t)/T}\) has \(M\) discrete values, and \(\varphi\) is the phase. In the case of OOK, \(M\) is equal to 2, corresponding to two waveform types.  
FSK is a frequency modulation scheme that conveys data using distinct frequencies to represent symbol states. The simplest FSK is binary FSK (BFSK). BFSK uses a pair of discrete frequencies to transmit binary 0 and 1 information. The general analytical expression for a modulated BFSK signal is  
\[S_{i}(t)=\sqrt{\frac{2E}{T}}\cos(\omega_{i}t+\ \varphi)\ 0\ \leq\ t\ \leq\ T;\ i=1,\ldots,M. \tag{5}\]  
PSK conveys data by changing the phase of the carrier. Binary PSK (BPSK) here has only two phases, 0 and \(\pi\). The mathematical expression is  
\[S_{i}(t)=\sqrt{\frac{2E}{T}}\cos(\omega_{0}t+\varphi_{i}(t))\ 0\leq t\leq T;\ i=1,\ldots,M. \tag{6}\]
## 2 Materials and Methods
### Metagenomic Sequencing, Assembly, and Gene Prediction  
Paired-end sequencing was performed on an Illumina NovaSeq 6000 system (Illumina Inc., San Diego, CA, USA) at Majorbio Bio-pharm Technology Co., Ltd. (Shanghai, China) using the paired-end 150 bp strategy. The raw metagenomic datasets were deposited into the NCBI SRA database (Accession number: PRJNA796130).  
The paired-end Illumina reads were filtered to remove adaptors and low-quality reads (length \(<\) 50 bp, quality score \(<\) 20, or ambiguous nucleotides \(>\) 3) by fastp v0.20.1 [20]. Clean reads were assembled using MEGAHIT v1.2.9 (parameters: -k-list 21,29,39,59,79,99,119,141 and -min-contig-len 1000) [21]. Open reading frames (ORFs) were predicted on the resulting contigs by Prodigal v2.6.3 (parameter: -p meta) [22], generating over six million ORFs (Table S1, Supplementary Materials). Finally, Bowtie2 v2.3.3 was used to map clean reads to the ORF and calculate ORF coverage [23].
# Investigation of Cement and Fly Ash on the Improvement of Fine Sand Soil
## 2 Materials and Methods  
### Materials  
In the study, sand samples were sieved between No. 30 and No. 200 sieves and classified as poorly graded sand (SP) according to the United Soil Classification System (USCS) [20]. The grain size distribution curve of the sand is shown in Figure 1. From Figure 1, the uniformity coefficient, the coefficient of curvature and the effective particle size are obtained and noted in Table 1. The other physical properties of the sand, such as the specific gravity, the maximum and minimum void ratio, and the maximum and minimum dry unit weight are also given in Table 1. Table 2 shows the chemical and physical properties (including Specific Gravity and Specific Surface) of the cement and fly ash. The particle size distribution curves of the additive materials are given in Figure 1. CEM II/A-M(P-L) 42.5R Portland Composite cement was used as an activator at a fixed rate of 3% by weight. The fly ash added to the test samples as a stabilizer has CaO = 29.28% \(>\) 10% and SiO\({}_{2}\) + Al\({}_{2}\)O\({}_{3}\) + Fe\({}_{2}\)O\({}_{3}\) = 50.92% \(>\) 50%; therefore, it is defined as C class fly ash according to ASTM C618 standard [21].
## 3 FE Analysis
### Mesh Sensitivity  
Several studies have demonstrated that mesh size significantly affects the accuracy of finite element simulations [25; 26]. Mesh density is directly proportional to mesh size. However, using finer meshes can result in longer computation times due to the tremendous number of elements. ABAQUS recommends employing elements with aspect ratios close to one [17]. While there is no prescribed minimum mesh size for a given finite element analysis, it is essential to refine the mesh appropriately to achieve accurate results matching those of experiments. Ultimately, the selection of mesh size depends on the user's evaluation of accuracy and convergence. This study assessed mesh sensitivity by employing mesh sizes of 50, 30, 20, and 10 mm for the beam. The mesh length of reinforcements was equal to the mesh size of the concrete in each case, while the mesh length of the stirrups was 10 mm, and the mesh size of the roller supports and pins was 5 mm.
# A Cost-Sensitive Diagnosis Method Based on the Operation and Maintenance Data of UAV
## 4 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Classifier** & **Accuracy** & **Precision** & **Recall** & **F1-Score** & **AUC** & **Total Cost** & **Training Time (s)** \\ \hline GBDT & 0.8437506 & 0.84375410 & 0.655405843 & 0.70726071 & 0.84783751 & 1747.55741 & 4.0573371 \\ XGBoost & **0.8584342** & 0.83591799 & **0.67201018** & **0.72292679** & **0.85433047** & 1733.41393 & 6.0064519 \\ LightGBM & 0.8406285 & **0.86094413** & 0.6565622 & 0.71640506 & 0.84902602 & 1767.96444 & **1.70085878** \\ FCNN & 0.8375030 & 0.79448272 & 0.64286232 & 0.68237482 & 0.79586347 & 1792.10667 & 6.3989562 \\ CNN & 0.8390625 & 0.84398075 & 0.65459342 & 0.70687547 & 0.80700821 & 1712.54131 & 9.9070521 \\ MC-LGB & 0.8208274 & 0.84215531 & 0.65531832 & 0.70772384 & 0.84405759 & **1591.51146** & 6.2960547 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Accuracy, precision, recall, F1-score, MCC, AUC, and training time of classifiers on the KPG dataset. To some extent, the AUC result exhibits the responsiveness of each diagnosis model to imbalanced data, which can be visualized from the Receiver Operating Characteristic (ROC) curves in Figure 3a,b. This explains that the proposed MC-LGB model can pay attention to all operation states without showing preference for any fault mode and keep up with the best XGBoost model. For the training time of the models, shown in Figure 4a, the proposed MC-LGB model does not exceed other models in the occupation of computing resources by employing the lighting LightGBM as meta-classifiers, which greatly improves the diagnosis availability of the MetaCost-based model. Due to the serious imbalance of the UAV operation data and the differences among fault modes, we paid more attention to the total misdiagnosis cost, which reflects the adverse effect of model biasing in some classes. Figure 4b shows the total cost of the classifiers on the KPG dataset. In the present study, we demonstrated that the proposed MC-LGB model can significantly reduce the total misdiagnosis cost from over 1700 to 1592, even at a similar accuracy level. By preferentially improving the diagnosis ability of high criticality fault modes, the diagnosis model can reduce the cost of inevitable misdiagnoses, under the limitation of existing classification abilities. In brief, the proposed two-step ensemble cost-sensitive method (MC-LGB) effectively complements the shortcomings of the original MetaCost framework. The MC-LGB model  
Figure 4: (**a**) Training time of classifiers on the KPG dataset and (**b**) total cost of classifiers on the KPG dataset. Figure 3: (**a**) ROC curves of classifiers on the KPG dataset and (**b**) AUC of classifiers on the KPG dataset. can play an essential role in the future when dealing with a demand for more complex UAV fault diagnosis.
# Assessing the Impact of Patient Characteristics on Genetic Clinical Pathways: A Regression Approach
## 3 Results
### Statistical Analyses
#### 3.5.4 Pseudo-Poisson Maximum Likelihood and OLS: The Length of the Clinical Pathway--Not-EMA Sample  
Contrary to the previous analysis, the estimates with pathway length as the dependent variable (Table 20) do not appear to be robust to the subsample considered. Despite this, both age and citizenship appear significant, the former being negatively correlated with length, the latter being positively correlated. In particular, the significance of the foreign dummy appears interesting. It may be that due to language problems, cultural differences, and difficulties in reconstructing the medical records of a foreign individual, the clinical course is more complex. The RESET test does not allow us to accept the null hypothesis that the model is well specified, in this case. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Variables**} & **(1)** & **(2)** & **(3)** & **(4)** & **(5)** & **(6)** \\  & **PPML** & **PPML** & **PPML** & **OLS** & **OLS reset** & **OLS** \\  & & & & & & & **Bootstrap** \\ \hline Age & \(-0.0153\) * & \(-0.0104\) & \(-0.0119\) & \(-0.226\) * & 4.086 * & \(-0.226\) * \\  & \((-1.942)\) & \((-0.985)\) & \((-1.098)\) & \((-1.704)\) & (1.713) & \((-1.696)\) \\ Weeks of pregnancy & \(-0.00247\) & \(-0.000820\) & \(0.000752\) & \(0.0130\) & \(-0.229\) & \(0.0130\) \\  & \((-0.253)\) & \((-0.0820)\) & \((0.0756)\) & \((0.0783)\) & \((-1.218)\) & (0.0811) \\ Foreign & \(0.232\) * & \(0.242\) ** & \(0.261\) ** & \(4.532\) ** & \(-82.27\) * & \(4.532\) ** \\  & \((1.914)\) & \((2.039)\) & \((2.233)\) & \((2.128)\) & \((-1.713)\) & (2.194) \\ Live births & - & \(-0.0818\) & \(-0.0819\) & \(-1.279\) & \(22.66\) * & \(-1.279\) \\  & - & \((-1.434)\) & \((-1.390)\) & \((-1.433)\) & (1.714) & \((-1.413)\) \\ Miscarriages & - & \(0.00873\) & \(0.0107\) & \(0.199\) & \(-3.477\) & \(0.199\) \\  & & \((0.119)\) & \((0.144)\) & \((0.162)\) & \((-1.413)\) & (0.164) \\ VPI & - & \(0.178\) & \(0.162\) & \(2.879\) & \(-51.48\) * & \(2.879\) \\  & - & \((1.040)\) & \((0.943)\) & \((0.824)\) & \((-1.738)\) & (0.817) \\ General practitioner & - & - & \(0.138\) & \(2.008\) & \(-35.47\) * & \(2.008\) \\  & & & \((0.642)\) & \((0.619)\) & \((-1.743)\) & (0.621) \\ Hospital specialist & - & - & \(0.130\) & \(1.872\) & \(-32.99\) * & \(1.872\) \\  & & & \((1.026)\) & \((1.008)\) & \((-1.694)\) & (1.022) \\ \hline \hline \end{tabular}
\end{table} 
Table 20: Clinical path length estimates with different techniques—not-EMA sample.
## 4 Platform Architecture and User Interface
### Gateway  
The gateway is the core management service and is utilized as an intermediary between the user interface and the modules responsible for urban feature detection. It is in charge of most of the platform's actions, such as user creation and authentication, action authorization, video upload, and others. The gateway also acts as an orchestrator between the modules. Specifically, it stores the uploaded multimedia from the user and informs the appropriate modules to start the processing, depersonalization, and detection procedures. Upon completion of the processes, the resulting JSON file is returned to the gateway and successively back to the user interface. The aforementioned JSON file can be shared among users as well as utilized for visualization purposes. The communication between the modules is performed with a message broker.