esa-sceva/satcom-chunk-collection huggingface dataset:
# Advancing Spanish Speech Emotion Recognition: A Comprehensive Benchmark of Pre-Trained Models
###### Abstract  
Feature extraction for speech emotion recognition (SER) has evolved from handcrafted techniques through deep learning methods to embeddings derived from pre-trained models (PTMs). This study presents the first comparative analysis focused on using PTMs for Spanish SER, evaluating six models--Whisper, Wav2 Vec 2.0, WavLM, HuBERT, TRILsson, and CLAP--across six emotional speech databases: EmoMatchSpanishDB, MESD, MEACorpus, EmoWisconsin, INTER1 SP, and EmoFilm. We propose a robust framework combining layer-wise feature extraction with Leave-One-Speaker-Out validation to ensure interpretable model comparisons. Our method significantly outperforms existing state-of-the-art benchmarks, notably achieving scores on metrics such as F1 on EmoMatchSpanishDB (88.32%), INTER1 SP (99.83%), and MEACorpus (92.53%). Layer-wise analyses reveal optimal emotional representation extraction at early layers in 24-layer models and middle layers in larger architectures. Additionally, TRILLsson exhibits remarkable generalization in speaker-independent evaluations, highlighting the necessity of strategic model selection, fine-tuning, and language-specific adaptations to maximize SER performance for Spanish.  
Spanish speech emotion recognition; pre-trained models; Wav2 Vec 2.0; TRILLsson; Spanish emotional speech databases; leave one speaker out; feature extraction
## 4 Performance Evaluation and Discussion
### Performance Evaluation on Testing Videos
#### 4.2.2 Impacts of DoI  
As mentioned in Section 3.3, the correlation between DoI and subjective overall QoE is modest. However, the contribution of DoI on prediction performance is well recognized in some cases, as shown in Patterns #2, #3, #5 and #7, which share a common characteristic where the predicted cumulative QoE correctly meets the overall QoE. Without DoI (\(\lambda_{2}=0\)), the predicted cumulative QoE would have been much lower than the overall QoE. Especially, in Patterns #5 and #7, which contain only one super-short rebuffering event near the beginning of streaming sessions, the memory intensity about this event must have completely vanished, followed by the dominance of the recency effect, resulting in very high overall QoE. However, the contents of these two videos might not be sufficiently interesting to the users, leading to the deterioration in their evaluation. Therefore, when the contribution of DoI is precisely recognized, our proposed model provides an extremely high accurate prediction. However, in Patterns #4 and #6, there exist long duration interruptions in the middle of streaming sessions, creating significantly high intensity memory about those events. As a result, the predicted cumulative QoE dramatically decreases and slowly recovers. However, the insufficiently accurate contribution of DoI has curbed the recovering rate. Consequently, the predicted cumulative QoE cannot catch up with the overall QoE at the end of streaming sessions. This emphasizes the lack of generalization in DoI coefficient \(\lambda_{2}\). We believe that the original reason is the insufficient number of participated subjects in the subjective evaluation in Section 3.3 where each video was watched and evaluated by only 10 subjects. In the future, a larger number of participants must be involved in this experiment.
# Policy-Based Smart Contracts Management for IoT Privacy Preservation
## 3 Proposed Method: Policy-Based Smart Contract Management for IoT Privacy Preservation
### Smart Contract Management Policies
If _ty_ is less than the threshold _th_ and the timestamp _ts_ is within specific time intervals (0, 6, 12, or 18), a routine smart contract is triggered without booking a private channel. In both cases, the timestamp is updated to include the current time and a 5 min interval based on the user preferences. * **Claim Check**: If a claim is being made (_claim \(==\) true_ and a private channel is available (_pc \(==\) true_), a claim smart contract is triggered within the private channel. If a claim is being made (_claim \(==\) true_) but a private channel is not required, a claim smart contract is triggered without using a private channel. * **Registration Check**: If the _register_ is true, a register smart contract is triggered. Our model, though demonstrated with a healthcare scenario such as patient health monitoring, is designed with flexibility to support a wide range of IoT applications. Its privacy policy framework is adaptable, allowing customization of data types, purposes, and criticality levels within the smart contracts to meet diverse use cases across sectors. In our model, the user side represents the entity owning the IoT devices, while the service provider side represents the party receiving the data for analysis or service provision. Any IoT ecosystem that includes these elements can potentially apply our model. For example, in an industrial IoT scenario, policies can be configured to prioritize data from critical machinery sensors, adjusting criticality levels and data-sharing preferences accordingly. Similarly, the model's preference structure--including parameters like trigger times, data identifiers, and private channels--can be adapted to suit environments requiring real-time data processing, controlled access, or automated action triggers. In this context, the industrial IoT devices would represent the user side, and the third-party analysis provider would represent the service provider. The industry could set privacy preferences as needed, configuring policies to secure their data. Additionally, using a private blockchain network enables secure, scalable data management for registered users, with dynamically tailored policy terms to meet specific needs in different IoT ecosystems, whether healthcare or industrial. This adaptability makes our model applicable across diverse IoT settings, supporting various data types, access control requirements, and communication protocols.
## 8 Conclusions and Future Work  
The work conducted in this paper makes use of a cluster of single-board computers in order to build an edge platform. For its performance to be tested, we compared the SBC based platform to a cloud-based cluster and a single-node local machine. All platforms are running Hadoop as the underlying big data analytics framework and are benchmarked against a job that averages random float numbers. The results have shown that we get huge improvements in the response time by introducing edge platforms. From the results described previously, we were able to gain 142% of the execution time by using a single physical machine at the edge, and we gained 23% by using the cluster of SBCs as an edge platform.  
As for future work, we are intending to build a larger SBC cluster and test it with AI and machine learning algorithms. Furthermore, we are planning on building different clusters based on different boards in order to get a clear idea about the performance of different boards and eventually decide on the best solution to deliver edge computing. In addition to that, we have the intention to expand the current testbed to include more buildings and eventually have enough data to implement more AI algorithms.  
Conceptualization, M.R.A. and D.B.; methodology, M.R.A. and D.B.; software, S.B.; validation, M.R.A., D.B., N.E. and K.Z.-d.; formal analysis, M.R.A. and D.B.; investigation, S.B., M.R.A. and D.B.; resources, M.R.A. and D.B.; data curation, S.B.; writing--original draft preparation, S.B.; writing--review and editing, M.R.A., D.B. and K.Z.-d.; visualization, M.R.A., D.B., K.Z.-d. and N.E.; supervision, M.R.A., D.B., K.Z.-d. and N.E.; project administration, M.R.A.; funding acquisition, M.R.A. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the United State Agency for International Development grant number 5-398.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to the confidentiality of the project.  
This work is sponsored by US-NAS/USAID under the PEER Cycle 5 project grant# 5-398, entitled 'Towards Smart Micro-grid: Renewable Energy Integration into Smart Buildings'.  
The authors declare no conflict of interest.
# Multi-Hop Routing Protocols for Oil Pipeline Leak Detection Systems
## 1 Introduction
### Routing in Wireless Sensor Networks
This paper focuses on the multi-hop routing protocols, which can be applied to linear oil pipelines and focuses on LoRa multi-hop topologies along the pipelines. The paper also aims to provide essential technological elements and concepts needed to support a monitoring system for oil leak detection. More specifically, it addresses issues from the point of view of energy efficiency. We define the network parameters and estimate network density and energy. Additionally, we test various multi-hop scenarios at different system parameters such as packet sizes and transmit ranges. The rest of this paper is organized as follows: Related work in multi-hop routing protocols used in the pipeline monitoring networks is described in Section 2. Section 3 presents the LoRa multi-hop technology model, results, and analysis. Section 4 concludes the paper.
## 2 Materials and Methods  
### Vineyard Site, Grapevine Treatments and Samples  
This study was conducted, during the 2019 and 2020 vintages, on _Vitis vinifera_ L. cv. Tempranillovines belonging to an experimental vineyard located at Finca La Grajera, Logrono, La Rioja (Spain). These vines were planted in 1997 using R-110 rootstock and treated according to local viticultural practices. They were trained in a VSP (vertical shoot positioned) trellis system, with a spacing between vines of 2.80 m between rows, and 1.25 m within the same row. For further information, climatic data were obtained from the Agroclimatic Information Service (SIAR), which were collected by an automatic weather station located near the area. During 2019, from the beginning of April to 1 September, the accumulated rainfall was 247.8 L/m\({}^{2}\), and the average temperatures were: 27 \({}^{\circ}\)C the maximum, 13.8 \({}^{\circ}\)C the mean, and 3.7 \({}^{\circ}\)C the minimum. For the year 2020, in the same period, the accumulated rainfall was 217.8 L/m\({}^{2}\), and the average temperatures were: 26.3 \({}^{\circ}\)C the maximum, 13.8 \({}^{\circ}\)C the mean, and 3.7 \({}^{\circ}\)C the minimum. Foliar applications of free methyl jasmonate (Mej) and amorphous calcium phosphate nanoparticles functionalized with Mej (ACP-Mej) were studied. To carry out the field experiments, free Mej aqueous solution (10 mM) and ACP-Mej aqueous suspension (1 mM Mej) were prepared following previous [19; 27; 34]. Tween 80 were used in both cases as wetting agent (1 mL/L). ACP-Mej nanoparticles were synthesized and fully characterized as described in detail elsewhere [31]. All treatments were applied first at veraison and second one week later. The concentration of treatment applied to the leaves of each plant was 200 mL/plant in each of the two applications. For the control only the plants were sprayed with the aqueous solution of Tween 80. Each of the treatments was carried out in triplicate, and each replicate consisted of 10 vines. All treatments were arranged in a complete randomized block design.  
The berries were harvested at their optimum point of technological maturity (weight of 100 berries constant, and 13% (\(\
u/\
u\)) of probable alcohol). Once harvested, they were destemmed and crushed until the must was obtained. The general parameters of all the musts were then measured, and aliquots of each must sample were frozen (\(-20\)\({}^{\circ}\)C) for subsequent analysis of the aromatic composition.
_applied_  
_sciences_  
_Article_  
**Evaluation of the Color Stability, Stainability, and Surface**  
**Roughness of Permanent Composite-Based Milled and**  
**3D Printed CAD/CAM Restorative Materials**  
**after Thermocycling**  
**[PERSON]\({}^{\star}\) and [PERSON]\({}^{\copyright}\)**  
Department of Prosthodomics, Faculty of Dertistry, Karamoglu Mehmeteby University,  
Universite Mahallesi, 2055 Sokak, Yurus Emre Verlekess, 70200 Karamam, Turkey  
\({}^{\star}\) Correspondence: [EMAIL_ADDRESS]; Tel.: +90-338-226-20-82  
**Abstract: With the developments in digital dentistry in recent years, subtractive and additive manufacturing and materials have emerged for the production of dental restoration. Novel permanent composite-based restorative materials are also among these materials. Because of their variety and increased use by clinicians, it is also important to know the properties, such as surface roughness and color stability, that are necessary for the longevity of these new materials. This study aimed to investigate the color stability, stainability, and surface roughness (R\({}_{\text{a}}\)) of additively and subtractively manufactured permanent composite-based restorative materials and compare them with a feldspathic glass ceramic. Two different subtractively manufactured composite-based blocks (Cerasamart 270, Vita Enamic), two different additively manufactured permanent composite-based resins (Crowntec and Permanent Crown Resin), and one feldspathic glass ceramic (Vita Mark II) as a control were compared. A total of 150 specimens were prepared (10 per material for surface roughness and 20 per material for color stability and stainability). The R\({}_{\text{a}}\) values and the color parameters were measured before and after thermocycling. The specimens of the staining test were then divided into two subgroups and stored for 7 days in distilled water (control) or coffee. The color parameters were remeasured after the storage period. The color differences (AE\({}_{00}\)) were evaluated for each measuring range, and these values are interpreted in terms of clinical perceptibility (\(\Delta\)E\({}_{00}\)\(\leq\) 1.30) and clinical acceptability (\(\Delta\)E\({}_{00}\)\(\leq\) 2.25) thresholds. All data were statistically analyzed (\(\alpha\) = 0.05). While Vita Enamic exhibited the highest mean R\({}_{\text{a}}\), Crowntec showed the lowest mean R\({}_{\text{a}}\) after thermocycling. Crowntec had the highest mean AE\({}_{00}\) both after thermocycling and storage in solutions. While Vita Mark II displayed the lowest mean AE\({}_{00}\) after thermocycling. Cerasamart 270 showed the lowest stainability in coffee. All tested materials showed acceptable surface roughness after thermocycling that was equal to or below the plaque accumulation threshold of 0.2 \(\upmu\)m. However, the mean AE\({}_{00}\) of tested materials were lower than clinical acceptability thresholds, except for Crowntec in all measuring ranges and Vita Enamic immersed in coffee.  
**Keywords:** additive manufacturing; subtractive manufacturing; permanent composite resin; color stability; surface roughness
## 4 Simulation Models and Sustainability: Characterization
### Discrete Event Simulation (DES)
The reason for this is that DES requires extensive data which are objective and translatable into numbers. This is easily achieved in case of energy modeling and flow modeling where data are easily available. The focus of DES on the operational modeling of sustainability is well exemplified by a study where material and energy flows are modeled in an industrial setting using DES. The model has been applied by decomposing the factory into infrastructure, process chains, individual processes, and building service systems. The energy and material flows are modeled using machine utilization and energy demands over time. The dynamic inter-dependencies were also taken into consideration [68]. Another recent instance of the application of DES with operational concerns is a study wherein sustainable manufacturing is conceived as being a function of energy management [89]. The study uses real-time online DES to model renewable energy sources with the intention of reducing carbon dioxide emissions and energy costs. The study uses ERIM-P (Energy Resources Intelligent Management-Predictor) data prediction in combination with ERIM-RT real-time data of manufacturing plant and weather conditions. Nonetheless, DES also finds use at the policy-strategic level. A specimen of this is a study that models the product lifecycle using DES. Termed lifecycle simulation, the DES is used to simulate the material flows [90]. The calculation is carried out using DES wherein the total amount of pollutants, waste, energy usage, costs, and profit of the company for a given period are considered. In order to better understand the application of DES for assessing sustainability, various aspects of DES models have been lined up with their efficacy with regards to sustainability assessment in Table 2. As can be discerned, the DES with regards to sustainability is beset with two major limitations. Firstly, DES is limited by scope and abstraction level and, secondly, DES requires large and precise datasets to model a system. Another issue with DES is that, with the increase in the complexity of the modelled system, its complexity increases exponentially; as a consequence, the use of DES has been limited to operational-tactical levels. In case of the first limitation, the issue has been alleviated using hierarchical modeling. The higher levels are modeled using SD or ABS while DES is used for micro/operational levels. The data dependency of DES has also been addressed using ABS and SD [91]. A detailed description of simulation hybrids is presented at the end of this section.
## 3 Energy Management Strategy  
### Previous EMSs  
#### 3.1.1 Rule-Based EMS (RB-EMS)  
In the work presented in [18], the RB-EMS was designed based on an operation mode control (OMC) and cascade control loops to generate a reference PEMFC power, guarantee the power demand of the load traction motor, maintain the BAT SOC at the desired value, and keep DC bus voltage at the rated value. This OMC was separated into three operation modes depending on the BAT SOC: discharge mode (high SOC), charge mode (normal SOC), and fast charge mode (low SOC). Two hysteresis cycles were considered to make changes between these modes. Besides, the PI controllers were applied to produce the duty cycles of the converters that regulated the power of energy sources in accordance with the reference powers defined by the EMS. However, this EMS has simple logical rules that can not consider the system's operating states completely, especially in the case of abrupt changes of load power. In addition, the challenges of fuel economy are not recognized.
# A Novel Battery-Supplied AFE EEG Circuit Capable of Muscle Movement Artifact Suppression
## 3 Materials and Methods: A Novel Proposed EEG Analog Front-End Circuit System
### EEG Sub-Circuit Modeling the Driven Right Leg (DRL) Circuit  
The utility of the DRL circuit is the creation of a feedback loop between the reference electrode and the body. This arrangement incorporates meticulously chosen resistors to protect both the electronics and the user. The circuit utilizes signal inversion and redirects it towards the body in order to ensure that the current remains below the safety threshold of 10 \(\mu\)A, as required by some of the safety rules (i.e., IEC-601-1 in [31]) that previous studies have declared that they took into consideration when designing their works [31; 32]. This sub-circuit is powered by a battery with a maximum voltage of 3.3 V. A 300 k\(\Omega\) resistor is used to regulate the maximum current to 10 \(\mu\)A, although typical operational currents fall within the range of several hundred nA. This configuration showcases our commitment to safety by ensuring that the circuit operates smoothly within predefined limits under normal conditions.  
Additionally, a 3.3 M\(\Omega\) resistor serves as a crucial pathway for the movement of leakage current towards the ground in the event of a malfunction. The primary function of this high-resistance component is to operate with a very low level of current, often within the nA range. This guarantees that the EEG signal remains undistorted when it is reintroduced into the body via the dual buffer amplifiers. A 100 k\(\Omega\) resistor is used to optimize the circuit's efficiency and minimize power consumption. The choice is made to retain the feedback current usage at around 0.5 \(\mu\)A. This strategy not only maintains the longevity of the battery but also ensures minimal energy consumption from the system. The intentional design strategy is opting for a larger resistor rather than a smaller one to attain an optimal equilibrium between power efficiency and operational effectiveness. In Figure 10 is depicted the EEG sub-circuit modeling the driven right leg circuit.
## 3 Results and Discussion
### Effects of Treatments on the Amount of Each Soil Fraction and on their Organic Carbon Content
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**Dry Matter (g kg\({}^{-1}\))**} \\ \hline
**Size Fractions** & **A (>250 um)** & **B (250–53 um)** & **C (53–20 um)** & **D (<20 um)** \\ \hline Depth & *** & ** & ** & *** \\ Tillage & ** & ** & n.s. & ** \\ Fertilization & ** & ** & n.s. & ** \\ Depth & & & & \\
0–30 & 0.28 b (0.12) & 4.67 b (2.30) & 7.04 b (2.01) & 18.05 a (2.54) \\
30–60 & 0.15 a (0.06) & 3.92 ab (2.37) & 6.41 b (0.80) & 19.81 b (2.96) \\
60–90 & 0.08 a (0.05) & 2.62 a (2.94) & 4.92 a (0.68) & 23.23 c (3.15) \\ Tillage & & & & \\ NT & 0.15 a (0.06) & 3.84 ab (2.80) & 6.38 a (1.05) & 19.92 a (3.03) \\ MT & 0.23 b (0.17) & 4.68 b (2.79) & 6.52 a (2.12) & 19.28 a (3.93) \\ CT & 0.13 a (0.07) & 2.69 a (2.03) & 5.47 a (1.20) & 21.88 b (3.16) \\ Fertilization & & & & \\ No & 0.13 a (0.08) & 1.77 a (1.14) & 6.15 a (1.93) & 22.37 b (2.90) \\ Yes & 0.21 b (0.14) & 5.70 b (2.18) & 6.10 a (1.12) & 18.35 a (3.02) \\ \hline \hline \end{tabular}  
* CT: Conventional tillage. MT: Minimum tillage. NT: No tillage. The values in each column followed by a different letter are significantly different according to [PERSON]’s test. n.s.: not significant. *** significant at the \(P\leq 0.001\).  
\end{table} 
Table 2: Analysis of variance and mean values of the amount of each soil fraction subdivided by soil depth, tillage, and fertilization treatment. The standard deviation is in parentheses. Lastly, the fractional OC content did not differ between fertilized and not fertilized plots since the adopted fertilization was only inorganic and rather low.