esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Data Analysis
### Structural Model Evaluation (Inner Model)
#### 4.5.1 Coefficient of Determination (R2)  
To measure the structural model, the determination of the coefficient, also identified as R2, was utilized. The value of R square (R2) was employed to quantify the extent of the particular latent exogenous variable's effect on the endogenous latent variable. They determined that the independent variables, namely E-informing, E-ordering, E-reverse auctioning, E-sourcing, and E-tendering, have a high effect on the dependent variable green procurement practices (R2 = 0.868). In addition, Table 7 these results shows that the collective effect of the exogenous variables explains about 87% of the total variance in the endogenous variable, which is green procurement practices to use implement sustainability in procurement practices in ISO 14001-certified organizations.
# A New Method for Rapid Optimization Design of a Subsonic Tandem Blade
## 4 Optimization for Tandem Blades
### Optimization for a Double-Stage Transonic Compressor with Tandem Blades
#### 4.2.1 The Optimization Procedure  
As is known to compressor designers, the challenge of a multistage compressor at low speeds is that the rear stages cannot pass through enough mass flow to prevent the front stages from stalling [22]. Since the tandem stator is located at the rear stage, a tandem airfoil with a wider incidence range is of great importance for the matching characteristic of the double-stage transonic compressor at low speeds. Based on the above analysis, the following two challenges are explained for further understanding of the tandem stator optimization in this paper. Firstly, the radial distribution of tandem stator incidence at design speed and off design speed are presented in Figure 21a, which determines the essential incidence range from hub to tip, varying from 6\({}^{\circ}\) to 10\({}^{\circ}\) correspondingly. That is, a nonoptimized tandem stator with a smaller incidence range may lead to double-stage compressor performance deterioration (difficulty in stage-matching at low speeds or an early corner stall before the rotor achieves its maximum pressure rise at design speed). Secondly, as shown in Figure 21b, the inflow Mach number, along the spanwise, varies from 0.95 to 0.8 at design speed. The shock wave in FB seems inevitable with such a high inflow Mach number; consequently, the optimization using the camber line modification method should be finely balanced on blade loss and incidence range.  
Base on the above analysis, the rear stage tandem stator of a double-stage compressor was optimized (15 blade elements along the spanwise), while the other blade rows were kept fixed. The optimization principles and the method are the same as the 2D tandem blade model, so no additional explanation is needed here.
# Free Vibration Analysis of a Thermally Loaded Porous Functionally Graded Rotor-Bearing System
## 6 Results and Discussions
### The Effect of Temperature Gradient on DNFs for Different Values of k and \(\alpha\)  
The variation in DNFs with the power law index of an FG rotor system with and without porosity for different thermal gradients is plotted in Figure 9,b. Dimensionless natural frequencies decrease with the increase in the temperature gradient, as expected. This is due to the reduction in material properties, especially Young's modulus, at higher temperatures. However, the presence of porosities in the FG shaft result in a reduction in Young's modulus and other material properties, which would lead to the abrupt drop in the DNFs at high temperatures (\(\rm T_{oi}=T_{o}-T_{i}=300\) or \(600\)). Later, the DNFs slightly increase initially with the power law index (k), but the surge in DNFs eventually becomes barely detectable.
# Regularized Zero-Forcing Dirty Paper Precoding in a High-Throughput Satellite Communication System
## 4 Simulation Verification and Analysis
Figure 9 shows the influence of the size of the beam overlap area on the system performance with users distributed at the edge of the beam. It can be seen that when users are distributed at the edge of the beam and the beam overlap factor is small, the throughput performance of the beam will decrease more obviously with the increase in the overlap factor. When the beam overlap factor is large, the beam throughput performance changes slightly with the increase in overlap factor; thus, it tends to be stable. Additionally, the achievable throughput of users distributed at the edge of the beam is less than that of the random user distribution model under the same overlap factor. Figure 8: Influence of overlap factor on throughput performance of precoding schemes with random user location. Figure 9: Influence of overlap factor on throughput performance of precoding schemes with users distributed at the edge of the beam.
# A Geo-Clustering Approach for the Detection of Areas-of-Interest and Their Underlying Semantics +
## 2 Related Work
### Extracting Places' Semantics through Tag Exploitation  
The extraction of semantics of places is another research area that has benefited from the aforementioned data revolution within several social networks. Large, raw geo-tagged datasets have been used towards the automatic extraction of the underlying semantics of AOIs. [PERSON] and [PERSON] [29] used data from Flickr and based on their distributions and by using several methods for the extraction of semantics, namely naive scan, spatial scan, TagMaps TF-IDF and scale structure. They concluded that the hybrid ones, i.e., combinations of the aforementioned methods based on a weighting scheme, achieve the best results. [PERSON] et al. [30] clustered geo-tagged photos and applied a co-occurrence analysis approach on the tags contained within the resulting geo-clusters, as an effort to conceptualize the places within each. [PERSON] et al. [31] used an ontology and classifiers, in order to detect events. Using those events, they ended up classifying Flickr photos and showed that some event classes are relatively easy to learn. On the contrary others may require some kind of special attention or even some level of disambiguation. [PERSON] et al. [32] focused on time and location concepts related to tags. They analyzed the distributions of annotated capturing times and corresponding locations of photos upon the determination whether a harvested tag refers to time or location. Their approach achieved comparable results to humans. [PERSON] et al. [33] analyzed tags that had been collected from geo-tagged photos of a specific area, and upon a TF-IDF-based approach, they extracted a set of the most representative ones. Although their approach focused mainly on the visualization and user interaction aspect, a qualitative evaluation indicated that even such a simplistic approach may indeed provide meaningful results. Finally, [PERSON] and [PERSON] [34] presented an approach for tag assignment to geographic areas, using a TF-IDF scheme and logistic regression, for various levels of detail.
# Mapping Mountain Landforms and Its Dynamics: Study Cases in Tropical Environments
###### Abstract  
High mountain areas are critical for water security and natural hazard dynamics, as well as glacier and ecosystem conservation in a warming world. We present a brief account of the methodological steps for geomorphological mapping in mountain areas, including the required scale, the legends, technology, and software. We analyze the best imagery sources and their combination with fieldwork and geographical information systems (GIS), in performing accurate cartography. In addition, we present two case studies in which we apply several methods described previously. Firstly, we carried out a classical and digital geomorphological mapping of Cerro Chirripo (Talamanca Range). Secondly, we studied the Reserva Biologica Alberto Manuel Brenes (Central Volcanic Range), where we used UAVs to map high-resolution fluvial geomorphology. This methodological framework is suitable for future geomorphological surveys in mountain areas worldwide. Moreover, the case studies can give ideas on the application of these approaches to different mountainous environments.  
monitoring; GIS; geomorphology; mapping; landforms; mountain areas; UAV +
Footnote †: journal: Applied Sciences
## 2 Network Model and Channel Fading Characteristics
### Influence of Environmental Factors on Channel Fading
For such a case, connected link probability (LP) can be defined as \(\Pr(P_{r}\geq P_{th})\), and it can be seen from the model in [33] that is:  
\[P_{th}=P_{T}-P_{L_{0}}-10\alpha\text{log}_{10}\left(\frac{d_{th}}{d_{0}}\right) \tag{4}\]  
where \(P_{th}\) refers to threshold receiving power (receiver sensitivity), and \(d_{th}\) is the distance where the threshold receiving power becomes detectable. Then:  
\[ \begin{array}{l}\Pr(P_{r}\geq P_{th})=\Pr(-10\alpha\text{log}_{10}\left( \frac{d}{d_{th}}\right)+X_{\sigma}\geq 0)\\ \qquad\qquad=\frac{1}{\sqrt{2\pi\sigma}}\sigma_{10\text{log}_{10}\left(\frac{ d}{d_{th}}\right)}^{\zeta}e^{-\frac{\zeta^{2}}{2\sigma^{2}}}dx\\ \qquad\qquad=Q\left(10\frac{\alpha}{\sigma}\text{log}_{10}\left(\frac{d}{d_{ th}}\right)\right)\\ \qquad\qquad=\frac{1}{2}erfc\left(\frac{1}{\sqrt{2}}10\frac{\sigma}{\sigma} \text{log}_{10}\left(\frac{d}{d_{th}}\right)\right)\\ \qquad\qquad=\frac{1}{2}\left(1-erf\left(\frac{10}{\sqrt{2}\xi}\text{log}_{10} \left(\frac{d}{d_{th}}\right)\right)\right)\end{array}  \tag{5}\]  
where \(\zeta=\sigma/\alpha\), and a large \(\zeta\) value indicates high channel variance intensity. It can be seen from Figure 2 that the greater the channel fading intensity is, the lower the success probability of a network nodes connection will be. Figure 2: Model of link connection success rate.
# Deep Reinforcement Learning for the Agile Earth Observation Satellite Scheduling Problem
## 2 Problem Description
### Time-Attitude Adjacency Graph of the AEOSSP  
We model AEOSSP as an adjacency graph, which is a typical class of directed acyclic graphs. When the satellite passes directly over the target (i.e., the overhead moment), the pitch angle of the satellite is 0. We introduce time-attitude coordinates to represent the VTW of the task. As shown in Figure 1, the timeline of satellite operation is the \(x\)-axis, and the roll angle of the satellite is the \(y\)-axis. A task is a node in the graph whose coordinate \((t_{i}^{\text{side}},\theta_{i}^{\text{side}})\) consists of the satellite overhead moment and the roll angle. Each node has seven attributes: \(pri_{i_{r}}\), \(wb_{i_{r}}\), \(we_{i_{r}}\), \(tb_{i_{r}}\), \(te_{i_{r}}\), \(ba_{i_{r}}\), and \(ea_{i_{r}}\). The edge weight \(w_{ij}=\text{trans}\big{(}ea_{i_{r}},ba_{j}\big{)}\) between \(node_{i}\) and \(node_{j}\) indicates the transition time between \(task_{i}\) and \(task_{j}\). The optimization objective is to find a path that begins at virtual node 0 and satisfies all constraints to maximize the sum of the profits of all nodes on the path.
# Experimental Design of a Mobile Landing Platform to Assist Aerial Surveys in Fluvial Environments
## 5 Results
### Manual and Autonomous Mode
#### 5.1.1 Manual Mode: Straight Line  
A straight-line trajectory was performed to observe the efficacy of the vessel's propulsion system and course keeping. Figure 12 shows the results obtained during the test. Three speed profiles where commanded: first an acceleration command, an intermediate section with constant speed, and a final breaking phase. In the intermediate part, slight variations of speed by corrections on the trajectory can be seen.
# Pseudo-Phoneme Label Loss for Text-Independent Speaker Verification
## 5 Experimental Results
### Effectiveness of Hyperparameters  
In this part, we also compare the effects of different \(\lambda\) values on recognition performance using the PPL loss, and the case is equal to using only AM-Softmax loss when \(\lambda\) is 0. The results are shown in Table 5.  
Figure 4: DET curve for VoxCeleb test results of add PPL loss to different loss functions. (**a**) CE & CE + PPL; (**b**) AM & AM + PPL; (**c**) AAM & AAM + PPL.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \(\lambda\) **(C = 32)** & **ERK (\%)** & **minDCF (0.01)** \\ \hline
0.00 & 3.473 & 0.3469 \\
0.05 & 3.332 & 0.3242 \\
0.01 & 3.367 & 0.3520 \\
0.10 & **3.321** & **0.3234** \\
0.20 & 3.346 & 0.3285 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Exploration of the effects with different \(\lambda\) on VoxCeleb. (AM-Softmax + \(\lambda\)PPL).  
When \(\lambda\) is 0, equivalently using AM-Softmax loss results, we can see from the results in the table that adding the PPL loss obtains better performance than using only AM-Softmax loss, and the best performance is obtained when \(\lambda\) is 0.1, indicating that the introduction of speech information into the speaker recognition system needs to be moderate. The introduction of less voice information will not achieve the effect, and too much voice information will also affect the extraction of speaker information.  
We explored the effect of adding PPL loss on the results when using AM-Softmax loss, mainly to see the effects of different class center number settings on the results. The case is equal to using only AM-Softmax loss when C is 0, and the experimental results are shown in Table 6.  
From the above table, we can see that the EER has the best performance when the number of pseudo-phoneme class is 32, and minDCF has the best result when the number of class is 40. We guess that the reason is that the VoxCeleb dataset is an English-based dataset, and there are 39 phonemes in English according to the CMU pronuncing dictionary [52], so it also proves the effectiveness of clustering to obtain the phoneme information.
# Elastic Hop Count Trickle Timer Algorithm in Internet of Things
## 3 Related Work
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Algorithm Name** & **Research Idea** & **Advantage** & **Disadvantage** \\ \hline Original trickle [16] & Balancing of load between nodes in the network. & Managing the flow of DIO messages. & Load balancing limitation and high-power usage. \\ \hline E-trickle [13] & Deleting the listen-only phase from the suppression procedure. & The convergence time decreases for a given number of vertices. & Using different values of k, while the IETF ROLL group recommended not to change the value, which produces a developing imbalance of load and consumes more energy. \\ \hline Trickle-F [20] & Suppression phase of DIO messages by reducing the quantity of routing updates that need to be propagated. & Load balancing is recognized. & High power consumption, long convergence time. \\ \hline Trickle-Plus [22] & Solving the original trickle problems, which are high energy to configure a network or having a long convergence time with low energy. & Low power consumption of a network with short convergence time. & Not mentioned \\ \hline Elastic Trickle [23] & Using neighbor counter to control the trickle timer. & Collision avoidance through transmission phase. & Long time through dynamic selection of nodes. \\ \hline Optimized Dynamic Trickle [24] & Controlling the listen period of trickle timer depending on dropping rate factor. & Deal with network inconsistency status and high PDR. & High convergence time expected since it depends on dropping rate of packets. \\ \hline \end{tabular}
\end{table} 
Table 1: Related studies comparison.
## 5 Discussion
### Database Annotation  
The labelling of the real-life audio database has undergone both manual and automatic processes. The manual labelling of the time boundaries of the audio scenes and events has guaranteed subsequent reliable analyses. However, since it is a burdensome and time consuming task, it may hinder the generation of new databases from field recordings. To alleviate this task, we can consider those approaches focused on the identification of salient audio events regardless of their origin (e.g., [24; 25; 26]) or semi-supervised techniques that combine confidence-based active learning and self-training [40], allowing the combination of automatic labelling (high confidence scores) and human annotation (when low scores are obtained). However, whichever the approach followed to cope with non-stationary background noise, it is envisioned as a very challenging task.  
On the other hand, the acoustic salience of the anomalous noise events with respect to background noise has been automatically quantified in terms of SNR with higher resolution than in [34], where a binary salience descriptor was considered to differentiate between foreground and background noises. The computation of the ANE contextual SNR with respect to RTN or BCK noise levels poses several challenges. The RTN \(LA_{eq}\) within the ANE time interval is computed by considering the surrounding samples, which entails a simplification due to the non-stationary nature of road traffic noise. In addition, during the ANE time interval, the measured \(LA_{eq}\) is also influenced by background noise (this is of especially relevance for those ANE with low SNR). Moreover, real-life noise sources can exhibit relevant level variations along time. For instance, an ambulance passing by with siren generates a long sound with a high noise level when closest to the point of measurement, which then fades in the approaching and receding phases. Ideally, instead of only accounting for the mean SNR value, it would be desirable to obtain SNR labels at different time instants, considering the sound level trajectory of each sound source. This approach could be understood as a time-varying soft-salience labelling. From the ANE salience analysis, we have been provided with some relevant insights on what can be found in real-life urban and suburban environments. Any machine hearing running in real-life conditions should be designed to tackle the diversity of ANE in terms of salience, i.e., being robust to ANE salience variability.  
Finally, it is worth mentioning that both the definition and the categories of anomalous noise events can be a matter of discussion. In this work, they have been agreed with the DYNAMAP project consortium, considering ANE to be those audio events that do not reflect regular road traffic noise [1], i.e., coming from vehicles' engines or derived from the normal contact of their tires with the road surface. Nevertheless, both the definition and the list of ANE might be subject of modification in future works.
# An Actor-Based Formal Model and Runtime Environment for Resource-Bounded IoT Services +
## 4 Operational Semantics
### Transition Rules
#### 4.2.4 Subscribing to an Existing Service  
If a matching service is found, then the directory service tells the client service about this service, and a new contract is signed between the client service and the found service.  
If \(search(\rho,s)\) evaluates to \((\{s_{m}\},c)\), then the transition rule for subscribing to an existing service is as follows:  
\[\begin{split} search(\rho,s)\xrightarrow{\lambda}_{X}(\{s_{m}\},c) \Rightarrow\\ \langle\llbracket I:[R[search(\rho,s)]]_{a_{s_{d}}},\alpha:O]_{s_{d }},\ S\mid C\mid M\rangle\rightarrow\\ \langle\llbracket I:[R[(\{s_{m}\},c)]]_{a_{s_{d}}},\alpha:O]_{s_{d }},\ S\mid C^{\prime}\mid\langle s\Leftarrow s_{m}\rangle,\ M\rangle\end{split} \tag{8}\]  
where \(X=\{a_{s_{d}}\}\) is the context in which \(search(\rho,s)\) is reduced to \((\{s_{m}\},c)\); \(s_{d}\) is the directory service; \(a_{s_{d}}\) is \(s_{d}\)'s coordinator actor; \(\rho\) is the service request; \(s\) is the name of the client service which sent \(\rho\); \(s_{m}\) is the name of an existing service \(s_{m}\) which matches \(\rho\)'s requirements; \(c\) is a new contract created between \(s_{m}\) and \(s\), which has the form \((s_{m},\ s,\ (o_{s_{m}}\ i\ s))\) where \(o_{s_{m}}\in O_{s_{m}}\) is an output port of \(s_{m}\) and \(i_{s}\in I_{s}\) is an input port of \(s\); and \(C^{\prime}=C\ \cup\ \{c\}\). This transition happens if and only if \(search(\rho,s)\) is evaluated to \((\{s_{m}\},c)\). This leads to the creation of message \(\langle s\Leftarrow s_{m}\rangle\) containing \(s_{m}\) which is sent to \(s\), and the creation of a new contract \(c\) created between \(s_{m}\) and \(s\) on the right hand side.
## 6 Conclusions
### Implications for Future Research and Development  
This study highlights the potential of the Internet of Emergency Services (IoES) in improving emergency management and response. However, it also identifies several challenges and limitations associated with the implementation and use of IoES. One implication for future research is the need to address these challenges and limitations. For example, research could focus on developing standards and protocols to ensure interoperability and data sharing among different components of IoES. Additionally, research could explore ways to ensure the security and privacy of sensitive information transmitted through IoES. Another implication is the need for further development and integration of emerging technologies, such as artificial intelligence and blockchain, into IoES. This would require research into the potential benefits and risks of these technologies in emergency management and response