esa-sceva/satcom-chunk-collection huggingface dataset:
# An Unsupervised Deep Learning System for Acoustic Scene Analysis
## 3 Experiments
### Main Results  
Because the comparison of SSC, OLRSC, and JOLRSC methods in [9; 15] used the TUT Acoustic Scenes 2017 dataset as well, we simply copied their results from them. The comparison results are presented in Table 2. Since only the performance on _ACC_ were reported in [9; 15], we report the _ACC_ comparison results accordingly. From the Table 2, we observe that the proposed MAESC has a better performance than SSC and OLRSC, which manifests that the deep auto-encoder network is a more powerful dimensionality reduction method than the low-rank subspace method in OLRSC.  
With joint clustering, we also observe that the proposed JAESC also outperforms the JOLRSC method, which proves that the proposed joint clustering scheme is also more efficient than that adopted by JOLRSC. Specifically, JOLRSC conducts joint clustering on the original feature and its low-rank subspace feature in subspace. Once the original feature of high dimensionality is used for clustering, computational cost will rapidly increase. On the contrary, the proposed JAESC conducts joint clustering on four low-dimensional features, which leads to a lower computational cost than JOLRSC.  
As aforementioned, two channels of stereo audio have complementary information to some extent. It can be utilized by binaural representation and joint clustering. To further study the complementary information between the four channels of the binaural representation, we present the results of each channel in Table 3. We can find that four channels of binaural representation yield different performances because they contain different information. The average channel (i.e., MAESC) leads to the best performance among the four channels. The results also show that jointly using all four channels leads to significantly better performance than using any of the four channels separately in terms of both _ACC_ and _NMI_, which demonstrates that the four channels contain much complementary information. Moreover, the result also proves that a binaural representation can boost the performance of unsupervised tasks.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Methods** & **SSC**[15] & **OLRSC**[9] & **MAESC (Ours)** & **JOLRSC**[9] & **JAESC (Ours)** \\ \hline _ACC_ (\%) & 25.31 & 43.64 & **45.60** & 45.84 & **49.47** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performances of the other methods on the TUT Acoustic Scenes 2017 dataset.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & **Left** & **Right** & **Average** & **Side** & **JAESC** \\ \hline _ACC_ (\%) & 42.95 & 41.09 & 45.60 & 43.59 & **49.47** \\ _NMI_ (\%) & 45.13 & 46.00 & 48.01 & 45.40 & **53.20** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performances of the different channels of stereo audio with the proposed methods. “Left”, “Right”, “Average”, and “Side” refer to four channels of binaural representation. “JAESC” refers to joint clustering with four channels.
# Development and Operation of Track Condition Monitoring System Using In-Service Train
## 2 Track Condition Monitoring System  
### Overview of Track Condition Monitoring System  
Figure 1 shows an overview of the proposed system. The system can be categorised into a measurement section for measuring the vibration acceleration of in-service vehicles and an analysis section for analysing the vibration acceleration data acquired. In the measurement section, an onboard sensing device comprising a three-axis sensor, rate gyroscope, and GNSS receiver was installed on the in-service vehicle.  
The configuration enabled constant measurement of the car-body vibration acceleration by supplying power from the vehicle as it was operating. The vibration acceleration data acquired were transmitted to the server via a mobile phone network. A diagnosis based on the vibration acceleration data transmitted to the server is presented in the analysis section. Continuous monitoring of the track condition using this system enabled the detection of irregularities, such as track faults, and the provision of feedback to railway operators for maintenance planning.
# Spatial and Temporal Dependency of NDVI Satellite Imagery in Predicting Bird Diversity over France
## 4 Discussion
This finding is confirmed by the fact that the consensus approach that combine multiple time periods of NDVI has no real effect on models performance. This is also in line with previous works which demonstrated that NDVI-based measure of seasonal vegetation productivity is a stronger predictor than annual NDVI [17; 51].
## Results  
### Differences in Body Composition According to the Type of Fluid and Intake Period  
Table 2 shows the results of the analysis of body composition according to the type of fluids and intake period. First, in the time interval between R and P0 (P0-R), the comparison between CON and 3 RINK revealed significant changes in BFM (t = 2.280, \(p\) = 0.035), ICW (t = \(-2.656\), \(p\) = 0.016), and ECW (t = \(-2.964\), \(p\) = 0.008) but no differences in the other variables. In addition, for the time interval between P1 and P0 (P1-P0), there was no difference between the two types of fluids for any of the variables.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline  & **CON (\(\boldsymbol{n}\) = 10)** & **3 RINK (\(\boldsymbol{n}\) = 10)** & \multicolumn{5}{c}{**CON (\(\boldsymbol{n}\) = 10)**} & **3 RINK (\(\boldsymbol{n}\) = 10)** & \\ \cline{2-9}  & **P0-R** & **P0-R** & **t** & \(\boldsymbol{p}\) & **P1-P0** & **P1-P0** & **t** & \(\boldsymbol{p}\) \\ \hline BW (kg) & \(-0.40\pm 0.14\) & \(-0.27\pm 0.13\) & \(-0.667\) & 0.513 & 0.07 \(\pm\) 0.08 & \(0.17\pm 0.08\) & \(-0.896\) & 0.382 \\ SMM (kg) & \(-0.40\pm 0.37\) & \(-0.21\pm 0.15\) & \(-0.473\) & 0.642 & 0.20 \(\pm\) 0.37 & \(0.55\pm 0.18\) & \(-0.842\) & 0.411 \\ BFM (kg) & 0.66 \(\pm\) 0.22 & \(-0.17\pm 0.29\) & 2.280 & 0.035 * & \(-0.68\pm 0.25\) & \(-0.53\pm 0.26\) & \(-0.411\) & 0.686 \\ PBF (\%) & 0.93 \(\pm\) 0.28 & \(1.15\pm 1.30\) & \(-0.165\) & 0.871 & \(-0.90\pm 0.30\) & \(-0.71\pm 0.32\) & \(-0.428\) & 0.673 \\ TBW (L) & \(-0.76\pm 0.17\) & \(-0.08\pm 0.15\) & \(-2.944\) & 0.009 & 0.52 \(\pm\) 0.18 & \(0.5\pm 0.19\) & \(-0.154\) & 0.880 \\ ICW (L) & \(-0.53\pm 0.13\) & \(-0.09\pm 0.10\) & \(-2.656\) & 0.016 * & 0.38 \(\pm\) 0.13 & \(0.33\pm 0.12\) & 0.283 & 0.781 \\ ECW (L) & \(-0.23\pm 0.05\) & \(0.01\pm 0.06\) & \(-2.964\) & 0.008 * & \(-0.86\pm 0.96\) & \(0.18\pm 0.07\) & \(-1.079\) & 0.295 \\ ECW/ICW ratio & 0.53 \(\pm\) 0.08 & \(0.06\pm 0.43\) & 1.072 & 0.298 & \(-0.71\pm 0.98\) & \(0.38\pm 0.16\) & \(-1.094\) & 0.288 \\ Urine (mL) & \(-74.00\pm 32.05\) & \(-85.50\pm 14.38\) & 0.327 & 0.747 & \(10.00\pm 13.19\) & \(-1.00\pm 6.62\) & 0.746 & 0.466 \\ \hline \end{tabular}
\begin{tabular}{c} BW: body weight, SMM: skeletal muscle mass, BFM: body fat mass, PBF: percent body fat, TBW: total body water, ICW: intracellular water, ECW: extracellular water. R = Rest, P0 = Right after exercise, P1 = 1 h after exercise. * * \(p\) < 0.05 between groups. Values are mean and SEM.  
\end{table}
Table 2: Body composition according to the type of fluids and intake period.
# Cause Analysis and Preventive Measures against False Bottoms in Echosounder Data
## 2 Materials and Methods
### The Influence of False Bottoms on Echosounder Detection and Its Causes
#### 2.3.2 The First Ping Echo Signal Is Reflected Twice to Form a False Bottom on the Second Ping
Hence, in such instances, the seabed depth must satisfy the following conditions:  
\[t_{v}+t_{m}>\frac{2d_{r3}}{v}>t_{v} \tag{10}\]  
where \(t_{v}\) is the time between two consecutive ping signals, and \(t_{m}\) is each ping received during one cycle. Multiplying both sides by \(\frac{v}{2}\) at the same time yields the following:  
\[2d_{m}+\frac{vt_{i}}{2}>d_{r3}>d_{m}+\frac{vt_{i}}{2} \tag{11}\]The depth of the false bottom is as follows:  
\[d_{f3}=\frac{vt_{f3}}{2}=\frac{v(t_{r3}-t_{m}-t_{i})}{2}=d_{r3}-d_{m}-\frac{vt_{i} }{2} \tag{12}\]  
From the echosounder image (Figure 2c), it becomes evident that, when the bottom depth (\(d_{r3}\)) satisfies the conditions (\(2d_{m}+\frac{vt_{i}}{2}>d_{r3}>d_{m}+\frac{vt_{i}}{2}\)), there is potential for the formation of a false bottom, where the bottom echo is generated beyond the detection range. In the aforementioned scenario, as the true-bottom depth surpasses the maximum detected bottom, the echo signal at the bottom of the ping is absent in the current ping. Determining the trend of the bottom depth necessitates combining the preceding and subsequent echosounder images. As shown in Figure 2c, the bottom depth continues to deepen until it disappears after surpassing the maximum detection depth. At the interruption point, the true-bottom depth equates to the maximum detection depth, and the depth further increases. In the dataset selected for this study, \(t_{i}\) is relatively small. After exceeding the maximum detection depth, a false bottom begins to emerge at a depth of 0 (Formula (12)). Consequently, in the echosounder image, any continuous image that appears from a depth of 0 after surpassing the maximum detection depth can be classified as a false bottom. Relying solely on observations of false bottoms during the detection process can result in mistaking false-bottom images as either a cluster of fish or the true bottom. Thus, it is imperative to avoid such misinterpretations in practical use. It is important to acknowledge that false bottoms may also arise in other circumstances. Nevertheless, through extensive observations and analyses of numerous echosounder data, the occurrence of false bottoms in these other situations was found to be exceptionally rare. Accordingly, further elucidation of these occurrences will not be provided in this article.
# Virtual Grid-Based Routing for Query-Driven Wireless Sensor Networks
## 2 Related Work
Furthermore, the sink also sends a query to the ring node, mentioning the area of interest. The query is forwarded from the ring nodes to the sensor nodes located within the designated area. Upon sensing the data, the sensor nodes transmit it back to the ring nodes. Subsequently, the ring nodes employ angle-based routing to transmit the aggregated sensed data to the mobile sink. The Query-driven Backbone-based Routing protocol (QBR) [24] establishes a virtual backbone within the central region of the sensor field. This backbone consists of specific nodes known as backbone nodes, some of which form connections with each other, resulting in the creation of a tree-like structure called the backbone tree. These backbone tree nodes play a crucial role in maintaining information about the sink's location. When the sink intends to obtain data, it initiates the process by sending a query message to the closest backbone tree node. This node then forwards the query towards the requested area, extracting location information along the way. In reply to the query, the sensor nodes within the specified region transmit their sensed data to the corresponding backbone tree node. The collected data are subsequently transmitted from the backbone tree node to the mobile sink. Table 1 presents a summary of these protocols, including their structure type, sink movement type, number of sinks, and data transmission mode. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Routing Technique** & **Structure Type** & **Sink Movement Type** & **Number of Sink(s)** & **Data Transmission Mode** \\ \hline HexDD [18] & Grid & Random & Multiple & Query \\ RDDM [19] & Cluster & Random & Multiple & Query \\ QDVGDD [20] & Grid & Fixed & One & Query \\ EDEDA [21] & Grid & Fixed & One & Event \\ TARA [22] & Grid & Pre-defined & One & Event \\ QRRP [23] & Ring & Random & One & Query \\ QBR [24] & Backbone & Random & One & Query \\
**VGRQ (proposed)** & Grid & Random & One & Query \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of routing protocols.
# Game-Theory-Based Task Offloading and Resource Scheduling in Cloud-Edge Collaborative Systems
## 5 Task Unloading Strategy
### GTPSO Algorithm
#### 5.2.2 Policy Update Process  
The task offloading policy obtained from the improved particle swarm optimization algorithm is used as the preprocessing task offloading policy, and the two processes are iterated over each other until convergence to maximize system utility using Algorithm1 to optimize resource allocation under the given offloading policy. Each iteration of the algorithm consists of two processes:  
1. Resource allocation optimization: The user uses Algorithm 1 to optimize resource allocation according to the current offload policy and calculates the corresponding utility values for different offload policies.
2. Policy update competitions: Based on the optimized computational resources, calculate the utility of each user with different uninstallation policies. The users who can improve their utility compete for the policy update opportunity in a distributed form, and the user with the largest utility improvement updates the uninstallation policy, whereas other users keep the original uninstallation policy and wait for the next round of decision updates. Using the finite improvement property of the potential game, only one user with the maximum utility improvement is allowed to update the uninstallation strategy in each iteration. The iteration terminates when the Nash equilibrium is reached and when all users have no incentive to change their uninstallation strategies. The uninstallation policy that maximizes the utility of the system is obtained.  
The process is shown in Algorithm 2.  
```
1:Input: user set \(N=\{1,2,\ldots N\}\), \(M=\{1,2,\ldots M\}\), \(\tau_{u}=(D_{u},C_{u},T_{u})\), \(n\in N\); \(maxGen\), \(v\in[-M,M]\), \(x\in[1,M]\), \(w\), \(g\).
2:For each particle
3: Initialize position \(X_{k},\ V_{k},\ Fitness(V)\), \(P_{best},\ G_{best}\)
4:End For
5:Iteration\(y=1\)
6:DO
7: Update the \(V_{k}\) by (27) and \(X_{k}\) by (28)
8: Update the \(w\) by (29)
9: Evaluate particle \(k\)
10:If\(Fit\ (X_{k})\ <\ Fit\ (P_{best})\)
11:\(P_{best}\)\(=X_{k}\)
12:End if
13:If\(Fit(P_{best})\ <\ Fit(G_{best})\)
14:\(\Theta_{best}=P_{best}\)
15:End if
16:\(k=k+1\)
17:WHILE maximum iterations or optimal solution are not changed
18:Output: Pre task offloading strategy \(S^{*}\)
19:\(t\)\(\leftarrow\)\(t\)\(+\)\(1\)
20:while\(S^{*}(t)\
eq S^{*}(t-1)\)do
21:\(S^{*}=S^{*}(t-1)\), set \(n=1\)
22:while\(n\leq N\)do
23: calculate \(u_{n}^{l}\Big{(}s_{n}^{*}s_{-n}^{*}(t-1)\Big{)}\) by (8)
24: calculate \(f_{n}^{mr}\) and \(u_{n}^{m}\left(s_{n}^{*},s_{-n}^{*}(t-1)\right)\) by algorithm 1 and (9), respectively
25: compute the best response \(\Delta_{n}(t)\)
26:\(n=n+1\)
27:endwhile
28:for each user \(n\), \(n\in N\)do
29:if user \(n\) wins in the \(t-th\) iteration,
30:then update \(s_{n}(t)\)
31:else\(s_{n}(t)\)\(=s_{n}(t-1)\)
32:endfor
33:\(t=t+1\)
34:endwhile
35:Output: Optimal computation resource allocation \(\mathcal{F}^{*}\) and offloading strategy \(S^{*}\)
```  
**Algorithm 2**GTPSO
# Extremely Low-Profile Monopolar Microstrip Antenna with Wide Bandwidth
## 2 Antenna Design and Analysis
### Parametric Analysis
Figure 4d displays the results of analyzing the effect of the feeding space for the coaxial connection on the bottom ground plane. As can be inferred from the results, it can be seen that _If_, the size of the feeding pad, caused the even TM\({}_{02}\) mode to decrease from 5.5 GHz to 5.05 GHz rather than the odd TM\({}_{02}\) mode of 5.9 GHz. In addition, the smaller size of _If_ yielded weak odd TM\({}_{02}\) mode coupling without changing the frequency changing. Therefore, the \(l_{F}\) can adjust the maximum impedance bandwidth to expand the 10-dB impedance bandwidth based on lowering the lower even TM\({}_{02}\) mode frequency and maintaining the upper odd TM\({}_{02}\) mode frequency. From the parametric study, it can be seen that the degenerated TM\({}_{02}\) modes with adjacent frequencies can be optimized from the meshed structure and the thickness of the proposed antenna. Unlike the conventional monopolar microstrip antenna with broad operation bandwidth, the proposed antenna can be designed by optimizing the degenerated TM\({}_{02}\) mode with a simple, single-layered substrate and single resonator structure for the extremely low-profile omni-directional antenna.
## 4 Analysis and Discussion of Results
### Critical Considerations on the Dissipation Phenomenon in the Prototype
The results of this study confirm that flow rates and power impact heat transfer in minichannels. The transition to the microscale introduces new thermal phenomena, such as early turbulence, confinement, and changes in boiling. Spatial confinement influences bubble nucleation and growth [51]. Likewise, this study observed that high Reynolds numbers reduce thermal resistance at high powers (65 W), corroborating the literature [6; 7; 25]; factors such as fluid ingress and non-uniform distribution impact thermal performance in minichannels. Surface roughness and wettability gradients affect heat exchange, as demonstrated in this study [46]. Experimental validation of heat transfer in micro- and minichannels still faces challenges due to geometric accuracy and limitations at the microscale [52]. Due to nanoscale effects and flow instabilities, traditional models have difficulty predicting thermal resistance and convective coefficients [53]. In this study, the application of a full factorial design (FFD) allowed for the statistical evaluation of the main parameters that affect \(G_{TR}\) and \(h\). Differently from the one-factor-at-a-time (OFAT) method [14; 15], the FFD revealed that power was the most relevant factor for \(G_{TR}\), while ambient temperature influenced \(h\)[16]. Optimization strategies such as customized flow rates and power input control demonstrated improvements that were comparable to more complex structural solutions [25]. These findings reinforce the importance of robust experimental methodologies and highlight the need to explore nanostructured surfaces and additive manufacturing for thermal optimization.
# Variational Mode Decomposition Based Time-Varying Force Identification of Stay Cables
## 4 On-Site Bridge Experiment
### Experimental Results and Analysis
#### 4.2.2 Recognition Results for Condition 2  
Condition 2 was in a state where a new support was installed, and the bolts were gradually tightened. In the time zone of 15:00-16:00 on 11 October 2019, a period of 400 s of the acceleration signals were selected for calculation. The acceleration signal and the frequency spectrum are shown in Figure 16. The fundamental frequency basically changed in the range of 0.78 Hz to 0.88 Hz. The cable force results based on the variational mode decomposition and HHT method are shown in Figure 16. The cable force gradually increased from 3.8 \(\times\) 10\({}^{3}\) kN to 4.3 \(\times\) 10\({}^{3}\) kN, but the fluctuation was better than working condition 1 because the beam was gradually restricted, and the growth of the three time periods from 14:00-17:00 was relatively obvious, from 3.8 \(\times\) 10\({}^{3}\) kN to 4.0 \(\times\) 10\({}^{3}\) kN. Time  
Figure 15: Acceleration signal, vibration spectrum and time-varying force of the A15 cable in the time interval of 10:10–10:20 on 11 October 2019.  
\begin{table}
\begin{tabular}{l l l} \hline
**Condition** & **Description** & **Corresponding Time Period** \\ \hline
1 & After removing the damaged support on the downstream side & 11 October 2019 10:10–10:20 \\
2 & Install new support and tighten bolts & 11 October 2019 15:00–16:00 \\
3 & After the new support is installed & 12 October 2019 09:00–09:30 \\ \hline \end{tabular}
\end{table}
Table 5: Working conditions and the corresponding time period.  
period 17:00-18:00 was about to be completed, and the force basically fluctuates within the range of [4.0 \(\times\) 10\({}^{3}\) kN, 4.3 \(\times\) 10\({}^{3}\) kN] except for local points.
# A Survey on LoRaWAN Architecture, Protocol and Technologies
## 2 LoRa & LoRaWAN Technologies  
LoRa is a RF modulation and corresponds to the physical layer in OSI reference model. Whereas LoRaWAN is a MAC layer standard which coordinates the medium. In this section, we will discuss technological aspects of both LoRa and LoRaWAN in detail.
# Facile Method by Bentonite Treated with Heat and Acid to Enhance Pesticide Adsorption
## 3 Results and Discussion
### Adsorption of Pesticides onto Bentonite and Modified Bentonite
#### 3.2.1 The Effect of pH
The result of calcination might cause more exchangeable cations moving to the edge and face of BC\({}_{500}\). Consequently, the cation exchange of atrazine is easier at these sites. In the case of adsorbents activated with acid (BA\({}_{0.5}\)C\({}_{500}\), BA\({}_{0.5}\), and BC\({}_{500}\)A\({}_{0.5}\)), some partial cations are removed. Thus, these adsorbents show lower CEC values, causing a reduction in ion exchange. However, these adsorbents contain high specific surface areas and a feature of Al Lewis-acid site, Si-OH and Si-OH\({}_{2}\)\({}^{+}\) groups, which are available for binding to both forms of atrazine. At pH 2, however, the adsorbent of BC\({}_{500}\)A\({}_{0.5}\) still shows a high adsorption capacity in spite of having a smaller specific surface area (Table 5), suggesting that atrazine binds to BC500A0.5 through both ion exchange and the adsorption process. This may result from a small particle size with highly active sites at the edge and face of BC\({}_{500}\)A\({}_{0.5}\) which enhances the removal of atrazine. The feasible interaction of atrazine bound to the adsorbents is a formation of a Lewis acid-base complex from an Al Lewis-acid site and Lewis base from atrazine (lone pair of electron on nitrogen), including the formation of hydrogen bonds to Si-OH and Si-OH\({}_{2}\)\({}^{+}\) groups. The adsorptions of diuron are shown in Figure 6d. Diuron exists half in neutral form and half in cationic form at approximately pH 4 [45]. The diuron adsorption is high for all adsorbents at low pH arising through the ion exchange process and the adsorption decreases with increasing pH, possibly due to competition between excess OH\({}^{-}\) and adsorbate to interact with active sites. However, the high adsorption of diuron with BC\({}_{500}\) and BA\({}_{0.5}\)C\({}_{500}\) compared to the others may result from an increase in hydrophobicity (as mentioned in TG results) for both adsorbents to adsorb a more neutral form.
## 5 Ontology Access Management Framework Development
### Sensing Resources  
The semantic description of a sensor, its measurements and the sensing process are described in the ontology. The user should be able to capture the measurements and provenance whether by focusing on performance or measurement. Observation and Measurements ontology (O&M) [33; 34], SSN/SOSA ontology [35] and SAREF ontology [36] are recognized frameworks for the semantic modelling of observational data and sensors. The O&M ontology, on the other hand, is restricted because it excludes sensor networks and devices as well as sensing processes. Its main goal is to model \"observations, as well as the made relevant in testing when making observations\" [37]. SAREF is a model which offers definitions for smart appliances and concrete devices in the built environment [38], in addition to the variables they check. Even though the model includes a comprehensive list of smart appliances and features, its constructivist paradigm is too grounded in factual examples, thereby limiting its applicability when unidentified characteristics are present. The classification of sensors and their observations are included in the SSN ontology at a higher abstraction level, making it the most versatile candidate for the development of the USA ontology. A sensor, for example, in SSN may be any object that detects a phenomenon, from a person to a metering system or a computer program.