esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Demonstration Setup and Parameters  
The proposed design methodology is applicable to common two-conductor transmission lines made from various materials and can be implemented using standard fabrication processes.  
It should be noted that if the transmission line is used in power applications, or if the inner conductor carries a large DC current as part of the bias path for active devices, non-uniform dimensions may lead to reliability issues in the circuit. Therefore, if the methodology proposed in this paper is applied to such scenarios, the associated risks must be carefully evaluated.  
To validate the effectiveness of the methodology, it is applied to both an SICL and a stripline. The low-temperature co-fired ceramic (LTCC) process is highly suitable for multi-layer, high-density integrated circuits, is well-suited for high-frequency applications, and is capable of realizing cavity structures. These advantages align well with the requirements of this design methodology. In addition, LTCC Ferro A6M (Ferro, Cleveland, OH, USA) was selected as the dielectric substrate due to its stable relative permittivity with minimal variation across the 1-100 GHz frequency range [17; 30]. For the simulations, an average relative permittivity of \(\epsilon_{r}=5.8\) and a loss tangent of 0.0012 are assumed. Copper is utilized as the conductor material.  
For demonstration purposes, simulations are performed using a finite element method (FEM) solver, with the implementation based on the parameters outlined in Table 1.
## 3 Design of Different Girder-to-Deck Interface Connections
### The Reinforcement Spacing of Cases  
The details of the girder-to-deck interface connections are different so the reinforcement spacing of the cases varies and it is investigated in this section. From the calculation theory of interface shear transfer presented in Section 3.1, the reinforcement-spacing demand is calculated in the following order:  
(1) Computing the interface shear-resistance demand (\(V_{ni\text{-}dem}\)):  
The interface shear-resistance demand is calculated from the force so that the flexural moment strength of the girder reaches the plastic moment. The plastic moment of 266,935 kNm is determined from the equation proposed by [PERSON] et al. [11]. Since the 4-point bending analysis is applied, the vertical shear force (\(V_{u}\)) is 16,477.5 kN. Then, the interface shear-resistance demand can be determined according to Equation (2) with additional consideration of the resistance factor (\(\phi\) = 0.9). (2) Computing the reinforcement area demand (\(A_{\text{rf-}dem}\)):  
The reinforcement-area demand is determined as the larger value of: area of reinforcement based on the interface shear-resistance demand (using Equation (6)) and minimum area of reinforcement (using Equation (8)). (3) Computing the reinforcement-spacing demand (\(S_{\text{rf}}\)):  
From the reinforcement-area demand and the details of the girder-to-deck interface connections, the reinforcement-spacing demand is determined. The calculation results of the reinforcement-spacing demand are summarized in Table 2. From the results of the reinforcement-spacing demand, the reinforcement spacing selected for cases 01 to 04 is 100 mm, 125 mm, 225 mm, and 275 mm, respectively. These reinforcement spacings are used for finite-element analysis models presented in Section 4. It can be seen that the reinforcement-spacing demand increased gradually from Case 01 to Case 04. However, the reinforcement-spacing demand can be adjusted for specific design purposes based on influential parameters. The parameters affecting the reinforcement-spacing demand are investigated in Section 5. For more convenience in determining the reinforcement spacing, a design aid chart was generated utilizing the equation for estimating the interface shear resistance, which controls the connection design. To demonstrate the use of the design aid chart, the interface shear-resistance demand of 4500 N/mm of the prototype bridge is used to determine the different alternatives in terms of reinforcement spacing for cases, as shown in Figure 8. From Figure 8, it can be seen that the reinforcement spacing for cases 01 to 04 is 100 mm, 125 mm, 225 mm, and 275 mm, respectively, to satisfy interface shear criteria. With the same reinforcement spacing, the interface shear resistance increases gradually from Case 01 to Case 04. Note that with the design of the reinforcement spacing as above, the interface shear criteria were satisfied for all cases.
# Self-Healing of Semantically Interoperable Smart and Prescriptive Edge Devices in IoT
## 3 SEDGE Proposed Methodology
### Middleware  
Middleware is a software extending the capabilities and common services that the operating system offers to applications. It mostly handles application services, authentication, API management and data management. In addition, it effectively facilitates the creation of applications by developers and serves as the underlying framework that interlinks users, data, and applications. MQTT is probably the most popular messaging protocol for bidirectional communication between IoT components used as middleware. In terms of IoT development, MQTT protocol is more suitable than others, e.g., HTTP, due to ease of use, response time, throughput, lower battery and bandwidth usage. Using the publish/subscribe messaging transport protocol and the use of messaging topics, a client can publish data to a topic on the server where all interested parties can subscribe to receive the data. The protocol specification can be found on the MQTT webpage [34].  
The protocol uses a server which hosts the MQTT broker. All clients connect to the broker to publish/subscribe. In the scope of this work, MQTT is used to connect the system's components locally and offline by taking advantage of its ability to enable communication between different programming languages. The broker is installed as a Linux system [35] service running locally on the edge controller, and binding to the localhost IP. All of the system's components, including the Z-Wave network manager, energy analyzers, all sensor APIs, weather stations when available, and other occasional entities, are programmed to communicate their data to the broker to ensure robustness and easier addition of further components.
# Hybrid LSTM-Attention and CNN Model for Enhanced Speech Emotion Recognition
## 4 Experiments and Discussion  
### Datasets  
To build robust SER (speech emotion recognition) models, vast amounts of annotated training data are essential; however, collecting this data can be both resource-intensive and costly. To validate the performance of the suggested model, tests were conducted using the TESS [39] and RAVDESS [40] datasets. These datasets are openly accessible and commonly utilized in the academic community for examining speech emotion recognition. The efficacy of the proposed model was demonstrated by comparing its results to those of existing models, indicating its superior performance.
# An Aquatic Mobile Sensing USV Swarm with a Link Quality-Based Delay Tolerant Network
## 7 Conclusions  
In this work, a low-cost aquatic mobile sensing platform based on USV swarms was proposed. A simulation environment was proposed based on ROS and Gazebo where one could test proposed routing strategies. A network module was developed where the results of the experiments in real events have the same behavior as in the simulated environment. Regarding the proposed routing strategies, it has been verified that an LQE based on RSSI estimation and Bit Rate has better delivery ratio than that based only on RSSI estimation. It can also be concluded that, from the various proposals, which vary in the way the packet is acknowledged, none is better for all the scenarios. In a static scenario, PAmuLQE-NACK is indeed the best. This is not the case when dealing with mobile scenarios, as strategy can lead to the loss of the packet by expiration. For the mobile scenarios with a small number of USVs, PAmuLQE-B-E2E is the best strategy, because it combines a higher delivery ratio with a lower overhead. For larger numbers of USVs, PAmuLQE-NACK is more suitable since it has a lower overhead and higher delivery ratio.  
As future steps, we aim to further assess our platform with different real mobile scenarios, over larger evaluation periods and considering a more dense and populated network, providing the organization and cooperation of several swarm clusters. Another point to be tackled in the near future is the evaluation of the Cooperative Path Planning and Trajectory Modification modules when link failures occur.  
[PERSON] designed the mobile aquatic network elements, implemented the forwarding strategies and the aquatic simulator, and prepared the evaluation scenarios. [PERSON] and [PERSON] designed the aquatic simulator. [PERSON], [PERSON] and [PERSON] designed the forwarding algorithms, analyzed the results and wrote the paper. [PERSON], [PERSON] and [PERSON] supervised the entire research process.  
This work was funded by the European Regional Development Fund (FEDER), through the Competitiveness and Internationalization Operational Programme (COMPETE 2020) of the Portugal 2020 through the Programa Integrado de IC&DT Centro2020 \"SmartBioR: Valorizacao Inteligente de Recursos Biologicos Marinhos Endogenos num Clima em Mudanca\", Centro-01-0145-FEDER-000018, and by the Project MOBIWISE, POCI-01-0145-FEDER-016426.  
The authors declare no conflict of interest.
## 1 Introduction
Approach for Slope Stability Analysis of Rockfill Embankments Considering Stress-Dependent Spatial Variability in Friction Angle  
Bishop's Simplified Method and Pseudo-Static Method for Seismic Slope Stability Analysis of Embankments  
Embankment dams usually consist of rockfill, transition, filter, foundation, core wall (or concrete slab for concrete-faced rockfill dams), cut-off wall, etc. Typical sections are shown in Figure 1. When the topographical and geological conditions of the analyzed domain are not complex, the LEMs can be efficient in obtaining the accurate safety factor. In virtue of simple formulation, [PERSON]'s simplified method [37] is one of the LEMs commonly used for the stability analysis of earth slopes and embankments. It has been verified that the precision of [PERSON]'s simplified method is satisfactory by comparison with more rigorous methods such as the Morgenstern-Price method [38]. [PERSON]'s simplified method is one of the LEMs prescribed in the Chinese design specification [39] for the slope stability calculation of embankment dams. Therefore, [PERSON]'s simplified method is used to analyze the embankment slope stability in this study. [PERSON]'s simplified method is based on the method of slices and only satisfies the overall moment equilibrium. [PERSON]'s simplified method considers normal interslice forces and ignores interslice shear forces. The forces acting on the slice are shown in Figure 2. The safety factor \(F_{s}\) given by [PERSON]'s simplified method for a circular slip surface is as follows:  
\[F_{s}=\frac{\sum\{[(W+V)\sec\alpha-ub\sec\alpha]\tan\varphi+cb\sec\alpha\}[1/ (1+\tan\alpha\tan\varphi/F_{s})]}{\sum[(W+V)\sin\alpha+M_{c}/R]} \tag{1}\]  
in which \(W\) is the weight of the slice; \(V\) is the vertical seismic inertia force; \(u\) is the pore water pressure at the base of the slice; \(\alpha\) is the inclination angle of the slice base to the horizontal direction; \(b\) is the slice width; \(M_{c}\) is the moment about the center of the slip arc due to the horizontal seismic inertia force; \(R\) is the radius of the slip arc; and \(c\) and \(\varphi\) are the cohesion and friction angle of the soil, respectively. According to the Chinese specifications for the seismic design of hydraulic structures [40], the seismic effect on embankment slope stability is estimated by a pseudo-static method in [PERSON]'s simplified method, where the impact of ground motion on the slope is modeled by horizontal and vertical seismic inertia forces, \(Q\) and \(V\), acting on the slice:  
\[\left\{ \begin{array}{l}Q=a_{h}\xi W\alpha/g\\ V=a_{h}\xi W\alpha/3g\end{array} \right. \tag{2}\]  
in which \(a_{h}\) is the design peak horizontal ground acceleration; \(\xi\) is the reduction coefficient of seismic effects (\(\xi\) = 0.25); and \(\alpha\) is the dynamic distribution coefficient for the seismic inertia force. The values of \(\alpha\) for the embankment higher than 40 m are presented in Figure 2.  
Figure 1: Typical cross section of embankment dams.
## 3 Models and Assumptions
### The Organization Context  
In addition to technical and environmental factors, the adoption of any innovation is affected by a number of specific organizational environmental factors. Such as company size [37], absorptive capacity [48], management barriers [60], organizational readiness [61], human resources [62] and top management support [63]. In this study, we consider top management support and organizational readiness as organizational environmental factors for adopting collaborative robot innovation in organizations.  
Top management support is considered to be an essential factor for organizations to adopt innovations [48; 64; 65]. SMEs have a simple and highly centralized structure, and the role of senior managers is crucial to the enterprise because their decisions affect the activities of all companies [64; 66]. Many studies have shown that the adoption of innovation in SMEs is directly affected by the support of top management. For instance, in a survey of the key predictors of cloud computing technology adoption in SMEs, top management support has a significant impact on the adoption [64]. In an empirical study on the adoption of Green Supply Chain Management (GSCM) technology by SMEs in Malaysia, top management support has also been proven to be a significant factor affecting the adoption [65].  
Organizational readiness is divided into two aspects: the availability of financial resources and human resources. The availability of financial and human resources is necessary for an organization to adopt an innovation [48; 67; 68]. Adopting innovation usually involves vast upfront costs, e.g., investment in R & D, production, sales, staff training, etc. [69; 70]. The literature shows that most enterprises, especially SMEs, lack financial and human resources [71; 72; 73; 74]. Therefore, having the financial and human resources for innovation can stimulate the willingness of SMEs to adopt collaborative robots. Based on the above, this study proposes the following hypotheses:  
**H6.**_The top management support for collaborative robot innovation is positively correlated with the adoption intention of SMEs._  
**H7.**_The organizational readiness for collaborative robot innovation is positively correlated with the adoption intention of SMEs._
# Data Access Based on a Guide Map of the Underwater Wireless Sensor Network
## 4 The DAGM Framework and Method of UWSN Data Storage and Query
### User Query Data Mechanism  
Due to the different times and different application backgrounds, the requirements for data granularity and QoS are different, so in the user query sentence, the data granularity, QoS, and so on must be specified. When the center ring node receives the query sentence, it will find the metadata set according to the specific requirement. Then, it can obtain specific application data from the corresponding data storage node. Finally, the data will be integrated. The form of integration is a hash map table of multiple values. In the hash map table, a key may correspond to multiple values, and each value is a structure body (including the attribute value, the sensor node identification number, storage time, and so on). According to the specific QoS, the data in hash map table is divided into different level and granularity, and sent to the user according to the requirement. All the data-meeting requirements are sent to the user when the user requires the accurate data. When the user requires the summary data, summary data (such as average, max, min) will be generated and returned to user. The user query process is described as Algorithm 4:  
```
user \(m\) generates data query sentences \(DQ\); generate transmission routing \(TR\) and send \(DQ\) to center ring node nearest \(k\); create data receive handle \(DRH\); center ring node processing \(DQ\) (look at Section 4.1); if(receive \(DTR\) and ready to receive data from center ring node) receive data and insert it to \(DRH\); else if(receive query failed from center ring node) release \(DRH\); end if.
```  
**Algorithm 4**User query data on DAGM
## 4 Quadrature Chaos Shift Keying Communication System
### System Design Particularities
Rewriting Equation (9) gives the following criterion for decision making:  
\[\sum_{k=1}^{K}s_{k}x_{k}\geqslant\sum_{k=1}^{K}s_{k}y_{k}+\frac{1}{2}\left[ \sum_{k=1}^{K}x_{k}^{2}-\sum_{k=1}^{K}y_{k}^{2}\right]. \tag{11}\]  
Alternatively, the sums can be replaced by a priori (\(\beta_{X}\) and \(\beta_{Y}\)) and a posteriori (\(\beta_{X^{\prime}}\) and \(\beta_{Y^{\prime}}\)) correlation coefficients, resulting in the criterion in the form \(\beta_{X^{\prime}}\geqslant\beta_{Y^{\prime}}+\frac{1}{2}(\beta_{X}-\beta_{Y})\). If inequality is valid, the system detects a '1' bit; otherwise, a '0' bit is received.
# Microbial Bioremediation and Biodegradation of Petroleum Products--A Mini Review
###### Abstract
The importance of these microorganisms in the biodegradation process arises from the microbial conversion of organic pollutants, which usually occurs because the organisms can use the pollutants for their energy needs, growth, and reproduction [6]. This is predominantly an adaptive process, usually dictated by environmental conditions. The biodegradation process varies widely, with carbon dioxide reported as the end product of the degradation process [5; 6]. To convert or yield a wide range of compounds, including hydrocarbons, polychlorinated biphenyl (PCBs), polyaromatic hydrocarbons (PAHs), radionuclides, and metals, some microorganisms have a surprising, naturally occurring catabolic diversity. Genetic potentials and environmental parameters such as temperature, oxygen, and quantity of nutrients, i.e., nitrogen and phosphorus, moisture, and pH, have been found to control the degree and effectiveness of degradation in soil/water [10]. Recent reviews have either focused on the metabolism of the hydrocarbon degradation process, factors influencing the process, the co-combination of various biological techniques, and some specific petroleum products. In this review, the bioremediation of petroleum hydrocarbon pollutants in water and soil, the role of microorganisms in the biodegradation of petroleum products, and the conversion of complex organic and inorganic pollutants into other simpler compounds by biological agents were discussed with recent applications. Moreover, the physicochemical and biological factors influencing the biodegradation mechanism and the enzymatic systems are highlighted along with bioremediation techniques.
## 5 Validation Tests
### Accuracy Analysis for Temporally Sequential Datasets  
In this section, we present a detailed analysis of the TAEE site, which experienced the most severe multipath errors among the three cases described previously. Daily data received continuously for 24 h at TAEE were divided into 1 h sets, and hourly variations in positioning errors were examined. Figure 13a-c display the hourly horizontal, vertical, and 3D RMSEs resulting from applying the HK model to the TAEE site data. Figure 13 confirms that applying a weight using the HK model significantly improves all horizontal, vertical, and 3D accuracies. However, among the 24 datasets, the data for the fifth and eleventh hours showed unusual phenomena, with reduced accuracy in the horizontal direction. Nonetheless, 3D accuracy improved for all 24 datasets.  
Figure 11: Horizontal, vertical, and 3D positioning RMSE according to the weight model at SOND shown in Figure 2b.  
Figure 12: Horizontal, vertical, and 3D positioning RMSE according to the weight model at TAEE shown in Figure 2c.  
In the test results shown in Figure 13, the 3D positioning accuracies improved for every hourly dataset. However, the corresponding RMSE values varied significantly, ranging from the best case of 4.1 m to the worst error of 46.4 m. To relate this phenomenon with satellite availability, the average number of visible satellites each hour is shown in Figure 13d, from which one can see that a lower number of visible satellites resulted in decreased accuracy. The correlation coefficient between the number of visible satellites and the 3D RMSE was 0.44, indicating a positive correlation. In summary, the positioning accuracy was two to three times better than that of the HK model. However, model performance declined when fewer satellites were visible.
## 5 Discussion
### Feedback of N, P on Plant Productivity  
For the feedback effect of N and P on C, only a few models have been explored and, as such, it is still a gap area. The study focused on the woody species, trees, and shrubs in the region [106; 107]. The meta-analysis to evaluate global patterns and controls of 10 variables associated with ecosystem P cycling under N addition [108]. A model representation of P limitation of two tropical forest sites productivity in Panam [109], but they could not provide a specific algorithm for quantifying the feedback effect of N and/or P on C over the physiological process. In this study, based on the global meta-analysis [71], we investigated and quantified the relationships of Vcmax and Jmax with leaf N, P for six different plant functional types.
## 4 Results
### Evaluation Indicators  
#### 4.3.1 Evaluation Metrics for Accuracy  
The accuracy metrics used in this paper included precision, recall, and mean average precision (mAP). Precision refers to the ratio of correctly detected objects (true positives, TP) to the total number of detected objects, i.e., the ratio of TP to the sum of true positives (TP) and false positives (FP). The formula for calculating precision is provided in Equation (9).  
\[\text{Precision=}\ \frac{\text{k}}{\text{N}}=\ \frac{\text{TP}}{\text{TP+ FP}} \tag{9}\]  
Recall refers to the ratio of correctly detected objects (true positives, TP) to the total number of actual objects, i.e., the ratio of TP to the sum of true positives (TP) and false negatives (FN). The formula for calculating recall is provided in Equation (10).  
\[\text{Recall=}\ \frac{\text{k}}{\text{M}}=\ \frac{\text{TP}}{\text{TP+ FN}} \tag{10}\]  
Mean average precision (mAP) is a comprehensive metric commonly used in multi-class object detection tasks. To calculate mAP, the average precision (AP) for each object category must - rst be determined. Then, the mAP is obtained by averaging the AP values across all categories. The special c formula for calculating AP is provided in Equation (11).  
\[\text{AP=}\ \mathop{\overset{n}{\underset{i=1}{\overset{1}{\text{ }}}}}\limits_{\text{i=1}}\ \text{Recall}\ \text{Precision}_{\text{interp}}\ \text{Recall}\ \text{+}\ 1 \tag{11}\]  
In this context, - Recall represents the recall value corresponding to the - rst interpolated point on the precision-recall curve in ascending order, and - n denotes the number of recall values considered. After calculating the AP values for each object category, the mean average precision (mAP) is obtained by summing these AP values and taking the average. The formula for calculating mAP is shown in Equation (12).  
\[\text{mAP=}\ \frac{\text{\overset{i}{\underset{i=1}{\overset{K}{\text{ }}}}}\text{AP}_{\text{i}}}{\text{K}} \tag{12}\]  
In this study, [EMAIL_ADDRESS] and mAP@.5:0.95 were used as accuracy evaluation metrics. Here, [EMAIL_ADDRESS] is the mAP value calculated at an intersection over union (IoU) threshold of 0.5, while mAP@.5:0.95 is a more comprehensive evaluation metric. It calculates mAP at 11 different IoU thresholds, ranging from 0.5 to 0.95, in increments of 0.05, and averages these mAP values to provide an overall assessment.