esa-sceva/satcom-chunk-collection huggingface dataset:
# The Evolution of Machine Learning in Vibration and Acoustics: A Decade of Innovation (2015-2024)
## 4 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset Name** & **Application Domain** & **Signal Type** & **Typical Use Case** & **Availability** \\ \hline CWRU Bearing & Bearing fault & & Fault classification in & Case Western \\ Dataset & diagnosis & Vibration & rolling element & Reserve University \\  & & & bearings & (open) \\ Paderborn Dataset & Gear and motor fault & & & Multi-condition & University of \\  & detection & Vibration, Current & motor monitoring & Paderborn (open) \\  & Machinery health & & Bearing degradation & NASA/US Army \\  & monitoring & & tracking & MFPT (open) \\  & & & Prognostic modeling & Southeast University \\ SEU Bearing Dataset & Bearing & Vibration & and degradation & (open) \\  & run-to-failure & & trend learning & \\  & & & Detection of acoustic & \\  & Acoustic anomaly & Sound & faults in industrial & MIMI Project (open) \\  & detection & (Microphones) & machines & \\  & & & Performance & \\  & & & benchmarking for & \\  & & & prognostic & \\  & & & algorithms & \\ Acoustic Emission & Crack and & & Structural damage & \\ DB & delamination & Acoustic emission & detection in concrete & & Mixed (some \\  & detection & & and composites & open-access) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Summary of widely used benchmark datasets in vibration and acoustic diagnostics. Methodologically, the growing proportion of conceptual and hybrid studies (combining deep networks with classical classifiers or optimization methods) reflects a maturation of the field. Researchers are no longer simply testing ML models but actively designing diagnostic architectures tailored to domain-specific challenges (e.g., data scarcity, sensor limitations, noise) [9, 14, 30, 37]. Another important implication is the slow but observable emergence of open benchmarks and multisensor frameworks, which indicate a shift from isolated laboratory research to scalable, interoperable diagnostic systems. This opens the door to standardized performance comparisons and facilitates industrial deployment. In summary, the observed trends are not merely statistical; they reveal a field that is progressively evolving toward higher levels of technical sophistication, real-world applicability, and integration with industrial data infrastructure.
## 3 Results and Discussion
### Overview and Chloride Penetration Resistance Classification
The high quality class includes concrete with SF and w/b up to 0.45. On the other hand, concretes with high incorporation of LF or high w/b (0.55) are within the low quality class. The classification was affected little by the type of aggregate. A significant overlapping between RCMT classes is observed in the ranges 7.5-12.5 and 12.5-17.5 (\(\times 10^{-12}\) m\({}^{2}\)/s), suggesting that these classes may be merged, both in the IT and the WDT. Therefore, taking into account Figure 17, and comparing to the classification of Gjorv [33] (Table 1), a clearer distinction between concrete  
Figure 16: Comparison between the \(K_{cr}\) and \(D_{cl,RCMT}\) (**a**) and between \(K_{cr,WDT}\) and \(K_{cr,IT}\) (**b**). Figure 17: Classification of chloride penetration resistance from \(D_{cl,RCMT}\), based on \(K_{cr,IT}\) (**a**) and \(K_{cr,WDT}\) (**b**). performance is obtained for the following classes (\(\times 10^{-12}\) m\({}^{2}\)/s): 5-7.5; 7.5-12.5; 12.5-17.5; \(>\)17.5. However, further research is needed to cover a wider sample, especially in the range of high quality concretes, up to 10 \(\times\) 10\({}^{-12}\) m\({}^{2}\)/s. The same procedure was carried out regarding the analysis of the \(D_{d,lT}\) and \(D_{d,WDT}\) ranges within the same RCMT classes (Figure 18). For the above-mentioned reasons, concrete with 30% LF was excluded from the IT results. Overlapping is also observed in concretes of different RCMT grades. However, the RCMT could reasonably sort concrete with different chloride diffusion properties when the classes 7.5-10 and 10-12.5 (\(\times 10^{-12}\) m\({}^{2}\)/s), as well as 12.5-15 and 15-17.5 (\(\times 10^{-12}\) m\({}^{2}\)/s), were merged. The same classes suggested based on the \(K_{cr}\) showed to be also adequate when based on the \(D_{cl}\). Overall, from the results obtained in this study, covering low to high strength concrete produced with different types of binder and aggregate, despite some identified differences, the RCMT could follow the same trend of the long-term IT and WDT, regarding the classification of the chloride penetration resistance of concrete.
## 6 Conclusions and Open Problems  
We have described the challenges faced in securing the communication and services of user-centric wireless systems. Such systems are often decentralized. On one hand, this decentralization enables users to conveniently share, access, and control unprecedented amounts of information, resources, and services. On the other hand, the broadcast and ad-hoc nature of wireless communication creates security and privacy challenges. We started our discussion by looking at location or proximity based, user-centric wireless applications. In particular, we explored the security ramifications of decentralization and contextualization on mobile social networks. We discussed location verification techniques as a method of improving these and other location based services. We looked at security issues that must be dealt with when using wireless technologies in consumer products and active implantable medical devices.  
At present, many methods for protecting location privacy against a malicious localization or tracking system depend on clients and networks co-operating in a security protocol. Network trust cannot always be established, however (e.g. at a public access point). It will be important to develop solutions that allow users to control their location privacy as well as their own communication history without establishing network trust. Furthermore, current methods for protecting location privacy might make the localization process difficult for all localizers--even trusted ones. Contextually supporting pervasive devices with location information while simultaneously preserving location privacy remains an issue.  
Preventing information leaks via side channels remains an issue. Many wireless devices are susceptible to side channel attacks, for example by fingerprinting of a wireless transceiver's physical layer characteristics. Current methods of preventing against information leaks are either expensive or obtrusive. As wireless technologies become more pervasive, more opportunities for observing leaked information will be made available to attackers. It will be necessary to prevent information leakage at all communication layers, including the hardware level.  
Many current cryptographic solutions (e.g. public key cryptosystems and threshold cryptography) consume a great deal of energy, and as such are unsuitable for resource constrained devices. Although a device's resource constraints can often be worked-around (e.g. via the use of a paired, non-resource-constrained device), security measures completely internal to that device would have significant benefits, including lower overall energy usage and fewer system components.
# Deep Spatio-Temporal Graph Attention Network for Street-Level 110 Call Incident Prediction
## 5 Experiment
### Experimental Results
When comparing the two GNN algorithms, the GAT approach employed in this study has demonstrated superior performance over the GCN method when using the existing spatio-temporal features associated with the 110 Call incidents. Specifically, for all the incidents, DSTGAT surpasses DSTGCN by 3.62%, 3.81%, and 3.96% in the ACC, F1-score, and AUC, respectively, while achieving a 5.06% reduction in the RMSE. Although the improvement over DeepFM is more pronounced, the GAT method consistently outperforms the GCN across all the evaluated metrics for all the three types of 110 Call incidents. This finding suggests that in event prediction tasks based on the graph structure of street networks, the introduction of an attention mechanism effectively mitigates the limitation of GCNs, which treat all the neighboring nodes equally. A noteworthy finding is that among the three different types of 110 Call incidents, crime-related incidents consistently demonstrated a predictive advantage across all six models tested in the experiments. This was followed by security-related incidents, and lastly, by assistance-related incidents. The comparison of the results for different types of incidents in DSTGAT is illustrated in Figure 8. Consequently, the inclusion of the latter two types can be regarded as introducing a degree of noise, leading to reduced predictive performance for all the incidents when compared to crime-related incidents. This observation also indirectly supports the validity of the criminological theories employed, highlighting their effectiveness in guiding the analysis and prediction of crime occurrence by considering the crime-related spatio-temporal characteristics. Figure 8: Result comparison for the different types of incidents in DSTGAT.
###### Abstract  
Neoadjuvant chemoradiation (nCRT) is an established procedure in stage union internationale contre le cancer (UICC) II/III rectal carcinomas. Around 53% of the tumours present with good tumor regression after nCRT, and 8%-15% are complete responders. Reliable selection markers would allow the identification of poor or non-responders prior to therapy. Tumor biopsies were harvested from 20 patients with rectal carcinomas, and stored in liquid nitrogen prior to therapy after obtaining patients' informed consent (Erlangen-No.3784). Patients received standardized nCRT with 5-Fluorouracil (nCRT I) or 5-Fluoruracil \(\pm\) Oxaliplatin (nCRT II) according to the CAO/ARO/AIO-04 protocol. After surgery, regression grading (Dworak) of the tumors was performed during histopathological examination of the specimens. Tumors were classified as poor (Dworak 1 + 2) or good (Dworak 3 + 4) responders. Laser capture microdissection (LCM) for tumor enrichment was performed on preoperative biopsies. Differences in expressed proteins between poor and good responders to nCRT I and II were identified by proteomic analysis (Isotope Coded Protein Label, ICPL(tm)) and selected markers were validated by immunohistochemistry. Tumors of 10 patients were classified as histopathologically poor (Dworak 1 or 2) and the other 10 tumor samples as histopathologically good (Dworak 3 or 4) responders to nCRT after surgery. Sufficient material in good quality was harvested for ICPL analysis by LCM from all biopsies. We identified 140 differentially regulated proteins regarding the selection criteria and the response to nCRT. Fourteen of these proteins were synchronously up-regulated at least 1.5-fold after nCRT I or nCRT II (e.g., FLNB, TKT, PKM2, SERINB1, IGHG2). Thirty-five proteins showed a complete reciprocal regulation (up or down) after nCRT I or nCRT II and the rest was regulated either according to nCRT I or II. The protein expression of regulated proteins such as PLEC1, TKT, HADHA and TAGLN was validated successfully by immunohistochemistry. ICPL is a valid method to identify differentially expressed proteins in rectal carcinoma tissue between poor _vs._ good responders to nCRT. The identified protein markers may act as selection criteria for nCRT in the future, but our preliminary findings must be reproduced and validated in a prospective cohort.  
rectal cancer; chemoradiation; proteomic; ICPL
## 3 Proposed Electronically Tunable Mixed-Mode Universal Filter  
The proposed MISO mixed mode filter is presented in Figure 3. It provides all five filter responses in VM, TAM, TIM and CM modes of operation. The minimum component filter employs a single VD-EXCCII, three resistors and two capacitors. The main attributes of the filter are: (i) use of a single active element, (ii) employment of only five passive components, (iii) no need for capacitive matching condition, (iv) availability of VM output from low impedance terminal, (v) availability of TAM and CM output from high impedance terminals, (vi) no requirement for negative/double input signals for response realization,(vii) inbuilt tunability of \(Q\) independent of frequency and (viii) in TIM mode, the filter gain can be adjusted without affecting \(\omega_{0}\) and \(Q\).
## 3 Results and Discussion
### Effect of the Filler on the Barrier Properties of the Coatings
With the exception of olive oil, which tends to enhance the reflectance of the composite coatings, counteracting the mattifying effect of the wax, the three series of samples G5, G10, and G10U demonstrated minimal variations in gloss, certainly lower than those observed in sample G0. Finally, to evaluate the hydrophobic contribution of the wax-based additive, the samples were subjected to contact angle measurements. Table 3 presents the findings, emphasizing a distinct effect brought about by the bio-based additive. Specifically, the inclusion of the wax-based powder leads to a gradual increase in contact angle values. The addition of 5 wt.% and 10 wt.% of the additive results in approximately 8\({}^{\circ}\) and 12\({}^{\circ}\) increases in angle, respectively, thereby enhancing the hydrophobic characteristics of the rice bran wax. Previous studies in the literature have linked hydrophobic-hydrophilic traits to surface roughness levels [83; 84]. Nonetheless, examination of Figure 3b reveals consistent surface morphology across all four sample series. Thus, the significant contact angle disparities observed in samples G5 and G10 are solely attributed to the hydrophobic nature of the wax, a trait extensively established in prior research [34; 79]. Moreover, the outcome exhibited by sample G10U warrants particular attention as it aligns closely with that of sample G10. This similarity underscores the significant role played by the UV absorber, demonstrating its capacity to positively impact the coating without adversely affecting other properties. Indeed, it confirms that the UV absorber does not compromise the functional characteristics of the wax-based additive, ensuring the preservation of its functional features. Nevertheless, given the relatively low contact angle values observed across all four series  
Figure 11: Change in (**a**) color and (**b**) gloss due to the water uptake test. Figure 12: Change in (**a**) coloration and (**b**) development of gloss of the samples after the liquid resistance test. of samples, it becomes challenging to definitively attribute hydrophobic properties to their surfaces [85]. Nonetheless, it is crucial not to underestimate the significant impact of the green additive, which demonstrates the capability to notably elevate the contact angle. In summary, the tests conducted underscored the effective barrier function of the coatings, which is bolstered by the inclusion of the green wax-based additive. This additive plays a crucial role in minimizing water absorption, thus maintaining the chromatic and aesthetic integrity of the composite layer even after prolonged exposure to harsh substances. While the surfaces may not exhibit purely hydrophobic properties, the protective efficacy of the bio-based additive remains clear, and its performance is unaffected by the simultaneous presence of the UV absorber.
# A Multi-Gateway Behaviour Study for Traffic-Oriented LoRaWAN Deployment
###### Abstract  
The advantages of LoRaWAN over conventional networks (GSM, 4G, 5G) in terms of investment and operating costs have been proven for network coverage in urban and rural areas. However, the theoretical coverage compared to the reality on the ground and the quality of service (QoS) provided remain very relative and depend on several technical factors, subject to increased research. Several recent approaches and hardware specifications recommended adding gateways as a solution to improve the LoRaWAN QoS indicators, mainly for high-traffic situations. However, such a solution will not work in all real-life scenarios since many factors must be considered. This article presents a study of the factors impacting the LoRaWAN QoS in the case of the usage of multiple gateways by exploring different scenarios to show how the payload length impacts the whole network's packet delivery ratio (PDR) and how it interacts when enhancing the GW number with and without confirmed traffic. Based on the simulation results, increasing the number of gateways can negatively impact the network's ability to support higher payload packets, especially in a high-traffic scenario. More precisely, we can say that for a low number of GWs, it is more appropriate to use a high payload length since we can achieve a high PDR. Nevertheless, with a high number of GWs, it would be more appropriate to use a low payload length to achieve a good PDR. Similarly, our analyses show that increasing the number of gateways ensures a better PDR but with a significant packet loss at the gateways, which is synonymous with higher energy consumption.  
Internet of Things; LoRa; LoRaWAN; QoS; multi-gateway +
Footnote †: journal: Future Internet
###### Abstract  
In this work, a machine learning technique employing a measurement-driven artificial neural network (ANN) architecture is proposed as a solution to the precise determination of the position and the moment of equivalent electric dipoles for unit characterization. These dipoles are used to match the generated electric field from various sources inside the spacecraft, during space exploration missions. Various methodologies for unit characterization have been proposed in the literature, the most common being the heuristic approaches, least squares variants, method of auxiliary sources, etc. Contrary to the previous time-consuming post-process methodologies, the proposed electric dipole neural network (_EDMnet_) can offer a real-time characterization of the measured unit (Device Under Test) after a proper training stage, especially as a fast pre-compliance method. The network uses the electric field vector, measured at 14 discrete locations, as input and reports the position and moment of the electric dipole that best matches the measured fields. In this work, various ANN architectures are tested and compared in order to select the optimal _EDMnet_ parameters for accurate source identification. It is shown that the size of the artificial training data affects the performance of the network. The proposed _EDMnet_ can provide accuracy in mm-scale, with respect to dipole positioning, greater than 99% in dipole moment prediction.  
dipole modeling; electric field measurements; electromagnetics; machine learning; neural networks; low frequency +
Footnote †: journal: applied sciences
## 3 Materials and Methods
### Mathematical Description of the Objective Function of the Adapted Version of the BPSO Algorithm  
The inputs of Algorithm 1 are: \(p\)--The particle for which the fitness value is computed, \(D\)--The number of dimensions of the search space, \(M\)--A precomputed matrix, which is presented in more details next, \(M_{\Delta}\)--A metric used for the evaluation of that matrix, and \(DLAs\)--The set of daily living activities monitored by the monitoring sensors. The output of the algorithm is \(r\)--The fitness value of the particle \(p\).  
```
Data:\(p\), \(D\), \(M\), \(M_{\Delta}\), \(DLAs\) Result:\(r\)\(r=0\);
1\(x=\) position of particle \(p\);
2\(F_{selected}=\{\}\);
3 for\(d\in D\)do
4 if\(x_{d}==1\)then
5\(F_{selected}=F_{selected}+f_{d}\);
6
7 end if
8
9\(\Delta=0\);
10 for\(a\in DLAs\)do
11\(min_{a}=+\infty\);
12\(max_{a}=-\infty\);
13 for\(f\in F_{selected}\)do
14 if\(M_{f,a}<min_{a}\)then
15\(min_{a}=M_{f,a}\);
16
17 end if
18 if\(M_{f,a}>max_{a}\)then
19\(max_{a}=M_{f,a}\);
20
21 end if
22
23 end for
24 if\(F_{selected}\
eq\emptyset\)then
25\(\Delta=\Delta+|max_{a}-min_{a}+1|\);
26
27 end if
28
29 end for
30\(r=\frac{1}{2}\times\left(\frac{\Delta}{M_{\Delta}}+\frac{|D|-|F_{selected}|}{|D|}\right)\);
31 return \(r\);
```  
**Algorithm 1**The objective function of the adapted BPSO algorithm.  
Initially, the value of \(r\) is \(0\) (line 1), the value of \(x\) is equal to the position of the particle \(p\) (line 2) and \(F_{selected}\) is an empty set (line 3). For each \(d\) from the set \(\{1,...,D\}\), if the value of \(x_{d}\) is equal to \(1\) then the feature \(f_{d}\) is included in the set of selected features \(F_{selected}\) (lines 4-8). The initial value of \(\Delta\) is \(0\) (line 9). For each activity \(a\) from the set of daily living activities \(DLAs\) the steps from (lines 11-23) are repeated. The initial values of \(min_{a}\) and \(max_{a}\) are equal to \(+\infty\) (line 11) and \(-\infty\) (line 12). For each feature \(f\) from the set of features \(F_{selected}\), the values of \(min_{a}\) and \(max_{a}\) are updated in (lines 14-19) as follows: If the value of \(M_{f,a}\) is less than the value of \(min_{a}\) (line 14) then \(min_{a}\) is initialized to the value of \(M_{f,a}\) (line 15) and if the value of \(M_{f,a}\) is greater than the value of \(max_{a}\) (line 17) then \(max_{a}\) is initialized to the value of \(M_{f,a}\) (line 18). If \(F_{selected}\
eq\emptyset\) (line 21) then the value of \(\Delta\) is incremented with \(|max_{a}-min_{a}+1|\) (line 22). In (line 25) the value of \(r\) is updated using the formula:  
\[r=\frac{1}{2}\times\left(\frac{\Delta}{M_{\Delta}}+\frac{|D|-|F_{selected}|}{|D| }\right). \tag{6}\]  
The ideal value of \(r\) is 1 and corresponds to the case when \(\Delta=M_{\Delta}\) and the number of selected features is 0, and the worst value of \(r\) is 0 and corresponds to the case when \(\Delta=0\) and all features are selected. Consequently, \(r\) takes values from the interval \([0,1]\) and the objective of the algorithm is to maximize that value. The algorithm returns the final value of \(r\) in (line 26).