esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Modal Emotion Recognition Using Speech Features and Text-Embedding
## 3 Proposed Method  
### Speech-Based Model  
Verbal components convey one-third of human communication; therefore, using speech signals and text data simultaneously can be helpful in accurate and natural recognition [21; 22]. Thus, we constructed a practical feature set to improve emotion recognition accuracy, complementing a text-based model. We surveyed acoustic features used for many speech-emotion recognition studies, and composed an optimal feature set by analyzing and combining the correlation of each feature. According to a previous study [23], harmonic structures expressed peaceful and positive emotions, such as happy, comfortable, and other, whereas dissonant intervals were closely related to sad and negative emotions. To quantify the harmonic structures, we extracted the harmonic features using the feature extraction tool Essentia, which is an open-source library tool for audio and music analysis; a detailed description of the harmonic features can be found in [24]. We used harmonic features reflecting the harmony of speech, which have not been extensively used in previous studies. We selected specialized features for emotion recognition through individual analysis, and then found the optimal feature set by re-combining features. The analysis showed that the harmonic feature set influenced the distinguishing of emotions, increasing the accuracy compared to that when not using the harmonic features.  
In total, 43 features were extracted and used in this study:  
* 13 MFCCs;
* 11 spectral-domain: spectral centroid, spectral bandwidth, seven spectral contrasts, spectral flatness, and spectral roll-off;
* 12 chroma: 12 dimensional chroma vectors;
* 7 harmonic features: inharmonicity, three tristimulus, harmonic energy, noise energy, and noisiness.  
After windowing the speech signals, the signals were converted to acoustic features, and the features were input into the LSTM layers. The output of the LSTM layer was connected with the fully connected layers, and the last layer inferred the probability of each emotion through the softmax function. The whole speech-based model is illustrated in Figure 3. Weight-decay and dropout methods were used for regularization.  
Figure 3: Structure of the model with acoustic features from speech data.
## 2 Materials and Methods
### Soil DNA Extraction and PCR Amplification  
Total DNA of each rhizosphere soil samples was extracted using DNeasy PowerSoil kit (QIAGEN, Hilden, Germany) following manufacturer instructions. After extraction, quantification and quality of DNA were measured by PicoGreen and Nanodrop (Thermo Scientific, Rockford, IL, USA). Each sequenced sample was prepared according to the Illumina 16S Metagenomics Sequencing Library protocols (Macrogen, Seoul, Korea). In amplicon PCR, V3-V4 region of the 16S rRNA gene of bacteria was targeted using the 16S V3-V4 primers [23]. The 16S V3-V4 primer sequences are as follows: 16S amplicon PCR forward primer, 5'- TCGTCGGCAGCGTCAGATGTGTAAAAGAGCACAGCCTACGGGNGGCWGAG-3', 16S amplicon PCR reverse primer, 5'-GTCTCGTGGGCTCGAGATGTGTAAGAGACAGAGACTACHVGGGTATCTAATCC -3'. Input gDNA was amplified with 16S V3-V4 primers, and a subsequent limited-cycle amplification step was performed to add multiplexing indices and Illumina sequencing adapters. The conditions for amplicon PCR were as follows. First PCR: initial denaturation at 95 \({}^{\circ}\)C for 3 min, followed by 25 cycles of denaturation at 95 \({}^{\circ}\)C for 30 s, annealing at 55 \({}^{\circ}\)C for 30 s and extension at 72 \({}^{\circ}\)C for 30 s, and a final extension at 72 \({}^{\circ}\)C for 5 min. The condition for index PCR was as follows. Second PCR: initial denaturation at 95 \({}^{\circ}\)C for 3 min, followed by 8 cycles of denaturation at 95 \({}^{\circ}\)C for 30 s, annealing at 55 \({}^{\circ}\)C for 30 s, and extension at 72 \({}^{\circ}\)C for 30 s, and a final extension at 72 \({}^{\circ}\)C for 5 min. The final products were normalized and pooled using the PicoGreen, and the size of libraries were verified using the TapeStation DNA screen tape D1000 (Agilent, Santa Clara, CA, USA).
# Fusion of Computer Vision and AI in Collaborative Robotics: A Review and Future Prospects
## 8 Discussion
### Human Trust, Transparency, and Ethical AI  
Trust is foundational to human-cobot collaboration. We shall limit the discussion to non-technical end-users (rather than to developers) to avoid confusion and scope creep, while addressing the concerns of a much larger population. Trust shapes whether humans feel safe, confident, and willing to share tasks or space with robots. Well-calibrated trust improves teamwork, reduces errors, and boosts productivity, while mistrust or over-reliance can cause accidents or underuse. Building trust requires explainable AI, transparency in perception-action loops, and ethical design to ensure that cobot behavior is predictable, interpretable, and aligned with human values. The explainability of AI-driven decisions, transparency in perception-action loops, and adherence to ethical principles are essential. However, current systems often treat explainability as an afterthought rather than a design imperative. Based on the principles of \"Ethics-by-Design\" presented in Section 7.3, we propose the following framework for Vision-Enabled Cobots:  
Ethics-by-Design Framework for Vision-Enabled Cobots  
To ensure fairness, privacy, and transparency in collaborative robotics, vision systems should follow an ethics-by-design approach with these practical pillars:  
1. Stakeholder Analysis:  
* Engage operators, designers, and ethicists early.
* Identify potential biases, vulnerabilities, and social impacts.
2. Data Privacy and Minimization:  
* Collect only necessary visual data.
* Apply on-device processing when possible to reduce cloud exposure.
* Anonymize human data (e.g., blur faces, remove identifiers).
3. Bias and Fairness Auditing:  
* Evaluate training datasets for representation gaps.
* Test models across diverse user groups to avoid discriminatory performance.
* Retrain with balanced data if disparities emerge.
4. Explainable AI (XAI) Integration:  
* Provide human-understandable reasons for vision-driven decisions.
* Use visual overlays or verbal cues to explain robot intentions.
5. Safety and Consent:  
* Implement opt-in/opt-out mechanisms for visual monitoring.
* Ensure clear signaling when cameras are active.
* Provide manual overrides for emergency stops.
6. Continuous Monitoring and Feedback:  
* Track system performance and ethical compliance post-deployment.
* Gather operator feedback to refine policies.
* Establish accountability lines for failures or breaches.  
By embedding these steps throughout their design and deployment, vision-based cobots can promote trust, protect user rights, and align with broader societal values.  
Future systems must embed ethical reasoning and user-centered transparency at all levels of decision-making.
## 5 The Potential of Computer Vision Research in UAV Datasets
### Leveraging UAV Datasets for Computer Vision Applications
#### 5.1.2 Emergency Response and Disaster Management  
RescueNet provides detailed pixel-level annotations and high-resolution images for 10 classes, including buildings, roads, pools, and trees. It is designed for post-disaster damage assessment using UAV imagery. It supports semantic segmentation using state-of-the-art models, enhancing natural disaster response and recovery strategies. AIDER focuses on classifying disaster events, utilizing images of traffic accidents, building collapses, fires, and floods to support real-time disaster management applications by training convolutional neural networks.
## 2 Coordinate Frames  
Figure 1 illustrates three major coordinate frames on the Earth ellipsoid. The inertial coordinate frame (_i_ frame) _O-X'Y'Z'_ is the fundamental reference frame for inertial navigation and an IMU enclosure usually measures the specific force as well as the angular rate relative to the \(i\) frame. Its origin is on the Earth's center of mass \(O\) and it is stationary relative to the fixed stars. We usually express the ultimate position solution, latitude \(\varphi\), longitude \(\lambda\) and height \(h\), in the Earth frame (_e_ frame) _O-X'Y'Z'_. Its origin is the Earth's center of mass \(O\), with _Z'_ axis forming along polar axis and _X'_ axis lying on the intersection of the equatorial plane and prime meridian plane. The _Y'_ axis is also on the equatorial plane and satisfies the right-hand rule. The origin and Z-axes of the \(i\) and \(e\) frames are coincident, respectively. In this paper, the \(e\) frame-based position solutions are based on the WGS-84 ellipsoid. To describe the velocity as well as the orientation of a ground vehicle, the right-handed navigation frame (_n_ frame) _P-X'Y'Z'_ is utilized and locally placed at the user's position \(P\). The _Z'_ axis is collinear with the local normal line of the reference ellipsoid pointing downwards while _X'_ and _Y'_ axes indicate the local north and east direction, respectively.  
The specific force _f  
enclosure. If the IMU is mounted parallel to the vehicle frame, we allocate the \(X^{b}\) axis pointing forward, in view of the vehicle, and \(Z^{b}\) axis aligning with local gravity direction. The \(Y^{b}\) axis satisfies the right-hand rule and indicates the right-side direction of the vehicle. Based on the pinhole projection model in our research, the image feature points are expressed in the camera frame (\(c\) frame) which is extended from the 2D image plane. The \(c\) frame representation is shown in Figure 2. The \(c\) frame's origin is at the camera center \(C\), and \(X^{c}\) as well as \(Y^{c}\) axes point towards left and upward directions of the image plane, correspondingly. The \(Z^{c}\) axis lies along the principal axis and is orthogonal to the image plane. \(f\) denotes the focal length of the camera.  
In this paper, the vehicle dynamics model is established in terms of the \(b\) frame instead of other frames. According to [PERSON] [15], the \(b\) frame based motion estimate has less modeling complexity and is prone to eliminating the influence of angular and position installation errors.
# A New LoRa-like Transceiver Suited for LEO Satellite Communications +
## 3 Proposed Transceiver
### Proposed Synchronization Signal Based on the Use of DCSS  
The DCSS transmitter is basically similar to the LoRa one, since the same structures of linear chirps are used. However, additional differential processing is implemented before the chirps are generated. This can be easily implemented, which guarantees the cost-effectiveness of our proposed transmitter.  
Indeed, in our DCSS transmitter, the preamble and the SFD symbols are no longer needed to estimate \(L\) and \(C\), using the system of two equations as in (18), since the latter modulation is robust to frequency desynchronization and tolerates some timing misalignment that does not induce important ISI. Therefore, in this work, we propose an original method to estimate time offset regardless of the frequency offset. To this end, we use the \(N_{p}\) up-chirps of the preamble for the signal detection and the estimation of the fractional offsets. We also maintain the _sync word_ to verify the accuracy of the time synchronization and one down-chirp symbol, as an SFD, to adjust the receiver's timing alignment. This structure of preamble is very similar to the ones deployed in LoRa PHY layer, except for the number of symbols of the SFD, which is reduced to one symbol.  
If we note \(x_{pre}(t)\), the complex envelope of the proposed synchronization signal and \(s(t)\), the continuous-time version of (3) where the transmitted symbols are \(\left\{D_{p}\right\}_{p\geq 0^{\prime}}\), the signal transmitted by a DCSS node can be written as follows:  
\[x(t)=x_{pre}(t)\mathds{1}_{\left[0,T_{p}\right)}(t)+s(t-T_{p})\mathds{1}_{ \left[T_{p},T_{p}+N_{n}\times T\right)}(t) \tag{22}\]  
where \(T_{p}\) is the duration of the latter synchronization sequence, which is equal to \((N_{p}+N_{\textsc{slv}}+1)T\). A spectrogram example of the transmitted signal is shown on Figure 2.  
Figure 2: Spectrogram of the transmitted signal.
# Dual-Polarization Conversion and Coding Metasurface for Wideband Radar Cross-Section Reduction
## 5 Configuration and Analysis of RCS Reduction with Coding Metasurface
increasing with the incident angle up to 40\({}^{\circ}\). The achievement of a stable reduction in the radar cross-section across different angles is attained above 10 dB. At incident angles of 0\({}^{\circ}\), 20\({}^{\circ}\), 30\({}^{\circ}\), and 40\({}^{\circ}\), the corresponding bandwidth of the radar cross-section reduction shifts to the high-frequency band. At an incident angle of 10\({}^{\circ}\), it moves to the lower-frequency band. This means that the coding metasurface model exhibits good wide-angle characteristics.
## 4 Discussion
### Limb Differences  
Although the present data evidenced similar slopes in TOI and HHb in the forearm and the thigh during LBNP, conclusions about a similar vasoconstrictive action in the upper and lower limbs would not be correct, as the larger blood volume of the lower limbs is expected to slow down the deoxygenation of the whole sample volume compared to upper limbs. In fact, vasoconstriction and the ensuing reduction in blood flow increase Hb desaturation in the capillaries, which in turn reduces the Hb saturation in the venous compartment (and in the whole sample volume); this latter process is slower if the size of the venous compartment is larger. As discussed above, a slower TOI and HHb time course is, in fact, observed in the leg. As a corollary of this consideration, having a similar slope in arms and legs implies that a stronger constriction takes place in the legs. The issue of a possibly stronger sympathetic constriction in lower limbs has long been investigated and is supported by a number of studies [24, 39, 40] but not all [30]. In particular, dilatation in the (contralateral) arm and constriction in the leg were observed during a unilateral handgrip exercise [39, 40], while a greater gain of the blood flow response to changes in CVP was reported for the leg compared to the arm by [PERSON] [24] during mild LBNP. In the latter study, the authors were cautious in the interpretation of the result, observing that it could have been caused by the larger muscle/skin ratio in the calf compared to the forearm. This concern is now resolved, considering that the specific focus of the TOI measurement is on the deep muscle tissue, with virtually no effects from the skin. The present data thus reinforce the notion of stronger sympathetic constrictor effectiveness in the lower compared to the upper limbs. Recent investigations on head muscles showed a similar relation between head and upper limb muscles: the former exhibiting a less constrictor action or even dilatation during sympathetic activation tests [10, 19]. Interestingly, dilator responses to endothelium-dependent and -independent vasodilators were reported to be blunted in the legs compared to the arms [41] and the myogenic response elicited by application of negative pressure to single limbs produced a stronger vasoconstriction in the legs than in the arms [29]. All these data support the concept that the constrictor capacity of blood vessels is differentiated in the different body areas according to a vertical gradient, adequate to match the hydrostatic gradient that develops with an erect posture: the constrictor capacity is higher where blood pressure is higher [19, 42].
# Low-Carbon Operation Strategy of Park-Level Integrated Energy System with Firefly Algorithm
## 3 Mathematical Model of Low-Carbon Integrated Energy Systems
### Mathematical Model of Micro Gas Turbines  
Micro gas turbines utilize the combustion of natural gas to drive the rotation of generator blades, generating electrical energy. Simultaneously, inevitably, thermal energy is produced. The collection of this thermal energy for heating purposes can enhance the overall energy efficiency of the gas turbine system. In an ideal scenario, the thermal and electrical power output of a gas turbine is directly proportional to the consumption rate of natural gas, as expressed by the following formula:  
\[P_{EGT}=\eta_{E}F_{GT} \tag{2}\]  
\[P_{HGT}=\eta_{H}F_{GT} \tag{3}\]  
\[\eta_{E}+\eta_{H}+\eta_{loss}=1 \tag{4}\]  
In this equation, P\({}_{\rm{EGT}}\) and P\({}_{\rm{HGT}}\) represent the electrical power and thermal power generated by the micro gas turbine, respectively, kW. \(\eta_{E}\), \(\eta_{H}\), and \(\eta_{loss}\) denote the electrical efficiency, thermal efficiency, and thermal loss rate of the gas turbine, while F\({}_{\rm{GT}}\) represents the combustion rate of natural gas, KW.
## 4 Case Study
### Machine-Learning Results
#### 4.1.1 Regression Model  
The regression model that predicts the RUL of a specific machine, the RMSE and MAE for the three study cases are shown in Figures 5-7, respectively. The best algorithm for the normal and double cases is the Decision Forest Regression.  
For the half case, the Boosted Decision Tree showed the best results. The Poisson Regression and Fast Forest Quantile Regression results is close to the optimum ones. The worst results are those of NN algorithm in all three cases, though the MAE is slightly improved in the double case. These results are due to one main reason which is that NNs algorithm is known to perform better with large data sizes in regression models. Some of the mentioned algorithms such as the Bayesian Linear Regression and Linear Regression are tested only in the normal case because their parameters are associated with linearization and hence it was decided not to use them to avoid over-fitting. As for the computational time, it increased significantly in the double case, while for the half case, it decreased compared to the normal case. When doubling the value of the parameters of the algorithms in question, the computational time suffered greatly with it increasing from 3 to around 10 min with no significant improvement in RMSE. In conclusion, the best parameters to select are the half case using Boosted Decision Tree algorithm.
## 3 Results and Discussion  
### Flat Position  
The observation of the molten pool behavior captured the longitudinal direction with 5000 FPS. The OPOD in metal transfer for an approximately square current waveform and the molten pool behavior of OPOD in GMAW are shown in Figure 3a. It can be more clearly observed that the welding current and voltage varied with time. The dynamic molten pool images varied at a high frequency pulse period of 151.5 Hz. The base current and the peak current were 35 and 345 A, respectively, and the pulse duration was 0.0012 s, as shown in Figure 3b. [PERSON] et al. [21] confirmed that variation in the effective radius and welding signals induce different molten pool flow patterns because the smaller arc size of the arc plasma produces a higher arc force density. On the other hand, the average constant effective radius and welding signal cannot induce a deeper penetration. Hence, it is important to confirm that the OPOD transfer mode occurs in P-GMAW. The duration of the entire mechanism for detachment of a droplet from the electrode tip in the P-GMAW process primarily combines the following four stages: (1) Heating and melting are shown in Figure 3a at 3.0046 s; (2) formation and growth of droplet are shown from 3.0052 to 3.0064 s; (3) necking at the root of the growing drop is shown from 3.007 to 3.0076 s; and (4) detachment of the droplet is shown at 3.0082 s.  
Figure 2: Schematic of the experimental setup for synchronization of arc imaging with welding signal. GMAW—gas arc metal welding.
## 4 Experimental Setup
### Experiments Results
#### 4.1.3 Result on LIRCMOP Test Suit  
The comparative results of SA-MPCMOEA and other algorithms on the LIRCMOP [33] high-dimensional complex constrained multi-objective optimization problems are summarized in Table 3. As shown in LIRCMOP1-LIRCMOP4, where feasible regions are extremely limited, all compared algorithms fail to converge due to their inability to traverse  
Figure 4: The distribution of all solutions evaluated via real-function assessments by SA-MPCMOEA in the CF2 problem.  
Figure 5: The distribution of all solutions evaluated via real-function assessments by ABSAEA in the CF2 problem.  
Figure 3: The distribution of solutions by SA-MPCMOEA and ABSAEA in the CF1 problem.  
infeasible regions before exhausting computational resources. In contrast, SA-MPCMOEA successfully obtains feasible solutions by leveraging its dynamic assessment strategy, which enables the surrogate model to efficiently screen candidate solutions and prioritize promising regions, thereby minimizing unnecessary real function evaluations and validating the robustness of its constraint-handling mechanism in extreme scenarios. For more complex problems (LIRCMOP5-LIRCMOP12), SA-MPCMOEA significantly outperforms conventional algorithms in most cases, although it slightly underperforms RVEAa on LIRCMOP5 and LIRCMOP6. These results highlight SA-MPCMOEA's superior search efficiency in high-dimensional discontinuous feasible domains, particularly in balancing exploration-exploitation trade-offs under severe constraint conflicts.
## 4 Explosive Capability Assessments
### Squat Jump Test  
The sixth test reviewed in this article is the squat jump test, which also offers a tool for specifically measuring basketball players' vertical explosive power. For this measurement assessment, the athletes assume a low squat position, refrain from any movement, and then jump up as high as possible. During the test, the players are usually asked to place their hands on their hips or behind their backs to prevent momentum from their upper limbs that could impact this assessment [2; 18].  
The squat jump test serves as a valuable tool for evaluating basketball players' vertical explosive power, a critical attribute in the sport. By assessing the height achieved during the jump, coaches and trainers gain insights into the athletes' ability to generate force and power with their lower body. This information facilitates the evaluation of performance and enables targeted training interventions to enhance vertical explosive power [2; 82; 100].  
Placing the hands on the hips or behind the back during the squat jump test eliminates the involvement of upper limb momentum, ensuring that the measurement accurately reflects the lower body's power generation. This focused assessment allows coaches and trainers to isolate and assess athletes' lower body strength and power, specifically in relation to vertical jumping ability [23; 82].  
Furthermore, the squat jump test provides valuable information for designing individualized training programs. By evaluating performance in this test, coaches can identify areas for improvement and tailor training strategies to enhance athletes' vertical explosive power. The test allows for ongoing monitoring of progress and the effectiveness of training interventions, facilitating evidence-based decision-making in optimizing performance [2; 82; 100].  
In summary, the squat jump test plays a crucial role in assessing basketball players' vertical explosive power in ball games, particularly in basketball. By measuring the height achieved during the jump and ensuring minimal upper limb involvement, coaches and trainers can evaluate performance, identify areas for improvement, and design targeted training programs to enhance vertical explosive power. The test's focus on isolating lower body strength and power aids in the development of specific training interventions [18; 35; 61; 101]. Ultimately, the squat jump test contributes to optimizing athletic performance and enhancing vertical jumping ability in the game of basketball [2; 82].