esa-sceva/satcom-chunk-collection huggingface dataset:
# Electroglottograph-Based Speech Emotion Recognition via Cross-Modal Distillation
## 3 Experiments and Results
### Experiments on Cross-Modal Emotion Distillation  
Based on the aforementioned comparative experiments on the teacher model and the student model, we conducted cross-modal emotion distillation with the following conditions:  
1. A pre-trained teacher model based on ResNet18 with log-Mel-spectrograms as input;
2. A 4-layer Bi-LSTM student model with \(F_{0}\) as input;
3. The cross-modal emotion distillation was conducted on the feature level.  
To find suitable settings for the hyper-parameters, we conducted a series of comparative experiments. Setting \(\tau\) from 1 to 4, we found that the most suitable setting for \(\tau\) was 2, according to Figure 6.  
To further discover the influence of cross-modal emotion distillation on the results on the condition of \(\tau=2\), we modified the hyper-parameter of \(\alpha\), which indicates the proportion of the distillation loss in total loss. Figure 7 indicates that the implementation of cross-modal emotion distillation obviously improves the performance of the model. From \(\alpha=0.2\) to 0.9, the accuracy of the validation dataset increased to varying degrees. The highest validation accuracy was reached when \(\alpha=0.6\), then dropped slowly with the proportion of the categorical cross entropy that was declining. According to the experiment results, we set \(\alpha=0.6\).  
The results of our framework are listed in Table 4. It can be observed that the training accuracy is obviously improved by 23.2% and the validation accuracy by 7.82% with the aid of cross-modal emotion distillation. In other words, under the condition of some utterances that are not expressive enough, our framework with CMED obtains better performance and realizes a result comparable to the human subjective evaluation.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Model**} & **Unweighted Training** & **Unweighted Validation** \\  & **Accuracy (\%)** & **Accuracy (\%)** \\ \hline Teacher Model & 99.22 & 81.25 \\ Student Model & 66.57 & 58.98 \\ Student Model with CMED & 89.77 & 66.80 \\ Ground Truth 1  & 70.00 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of the experiments with cross-modal emotion distillation.  
Figure 6: Experiments on different settings of the hyper-parameter \(\tau\).
## 2 Materials and Methods
### Data Collection Period  
To capture all variations, eight months of continuous data were gathered throughout the winter months of October, November, and December in 2022, as well as the summer months of May, June, July, August, and September in 2023. During the eight-month study period, data were collected daily for both inflow and outflow of each water quality parameter, resulting in the monthly average of 8 data points per parameter. This dataset was used for Pearson and Spearman correlation analyses to ensure robust statistical evaluation.  
The Regional Environmental Protection Agency (ARPA) website was used to gather daily cumulative climatic air temperature and rainfall data (Italian: Agenzia regionale per la protezione ambientale). The analysis of wastewater samples taken from influent and effluent streams was performed using methods that complied with the APAT IRSA CNR 29/2003 standards for pH, total phosphorus, suspended solids, nitrite nitrogen, nitrate nitrogen, ammoniacal nitrogen, and total nitrogen ([PERSON], 2016). Additionally, BOD\({}_{5}\) and COD were analyzed following the Standard Methods for the Examination of Water and Wastewater, 22 nd edition (Kal  
The settled sludge is returned to the aerobic zone, while the treated water moves to coagulation, sand filtration, and UV disinfection before being discharged as effluent. The remaining sludge from the clarifier undergoes thickening, digestion, and dewatering before disposal. This schematic overview reflects the full treatment process and the flow of influent and effluent throughout the system.
## 6 Mutual Interference Compensation
For example, to obtain a desired set of cyclic orthogonal codes with length 8, one can select only three rows in possible combinations like 2, 3, 5; 2, 4, 7; 2, 4, 8, _etc._ (Table 1). Now let us introduce the interfering illumination to Equation (3) of the background noise expressed as a continuous periodic signal:  
\[I_{MAI}\left(\tau\right)=\int\limits_{0}^{T}(b(t)+I_{MAI}u(t+\tau))v(t)dt=0+ \delta,\forall\,\tau \tag{9}\]  
With properly selected code sets, the interfering illumination \(I_{MAI}\) is completely suppressed at any delay \(\tau\) according to Equation (8). Background noise \(b(t)\) is eliminated as well, since Walsh-Hadamard codes are balanced. Graphical representation of mutual interference compensation is depicted in Figure 4.  
It can be seen that in case when \(u\) and \(c\) are cyclic orthogonal, the interfering illumination intensity \(I_{MAI}\) has been eliminated.
## 3 Building Blocks of Vehicular Networks
### Onboard Units  
The computing onboard of a vehicle is secured by an onboard unit (OBU). This typically includes several components, including single board computers for computing, read/write memory for information storage and retrieval, and network devices tasked with establishing a connection to the rest of the network [35]. The main objectives of the OBU are collecting data from onboard sensors, controlling actuators, transmitting messages, and ensuring data security.  
Further onboard computing is provided by the application unit (AU), which is currently limited to use in ground vehicles. The AU is designed to run specific applications. It is defined as a device inside the vehicle communicating via an OBU, which also may function in the role of an AU, or it can be a personal device such as a smartphone [18]. Although the definition of an AU found in ground vehicle networks expects it to be onboard [18], it is possible to find components similar in function for UAVs. An example may be found in small quadrocopters where a smartphone runs an application that directly interacts with a UAV onboard unit, thereby enabling manual control of the vehicle and providing information about its state or invoking autonomous actions, which are then carried out by the onboard unit on a quadrocopter.
# Middleware for Plug and Play Integration of Heterogeneous Sensor Resources into the Sensor Web
## 3 SWE-Based Acquisition Chain
### SOS Proxy  
The main functionality of the SOS Proxy is to decouple the SOS transactions from constrained platforms without Internet gateway, such as satellite-based platforms. It acts as a transparent intermediary layer, which takes O&M files from the platform's communications channel (i.e., satellite link) and forwards them to an SOS Server. As the SOS Proxy is completely transparent to the rest of the architecture, it does not have an associated SensorML description.  
Depending on the nature of the observation platform, the SOS Proxy can be deployed in the platform itself (i.e., a platform with direct Internet access through a GSM modem), otherwise it can be deployed in a shore station server (i.e., platform with proprietary satellite communications). Open source implementations of this software component are available in Java and Bash [33; 34].
## 5 Operational Scenarios Used for the Validation of the Proposed System  
First, the power flow balance is assumed, i.e., regardless of the state of the distributed generation sources, battery, and load:  
\[\mathrm{P_{PV}}+\mathrm{P_{WT}}\pm\mathrm{P_{ESS}}-\mathrm{P_{load}}\pm \mathrm{P_{grid}}=0 \tag{22}\]  
The power generated photovoltaic panel = \(\mathrm{P_{PV}}\); the power generated by the mini-wind generator = \(\mathrm{P_{WT}}\), the power injected or absorbed by the storage system = \(\mathrm{P_{ESS}}\), the total load power = \(\mathrm{P_{load}}\), and the power injected into or absorbed from the electrical network of the university = \(\mathrm{P_{grid}}\).  
Depending on the microgrid operational condition (interconnected or isolated), the instantaneous generated power, the total load power, and battery state-of-charge, the converters must behave according to the modes of operation described.  
For interconnected conditions, it is assumed that the battery does not inject any power into the electrical network; in addition, the PV and WT are maintained at their maximum power points.  
Interconnected mode, DG \(>\mathrm{P_{load}}\):  
\[\mathrm{P_{PV}}+\mathrm{P_{WT}}=\mathrm{P_{load}}+\mathrm{P_{grid}};\mathrm{ P_{ESS}}=0; \tag{23}\]  
Figure 12: Bode diagrams of the DC bus voltage open-loop control system.  
* Interconnected mode, DG = Pload: \[\text{P}_{\text{PV}}+\text{P}_{\text{WT}}=\text{P}_{\text{load}};\text{P}_{ \text{grid}}=0;\text{P}_{\text{ESS}}=0;\] (24)
* Interconnected mode, DG < Pload: \[\text{P}_{\text{PV}}+\text{P}_{\text{WT}}+\text{P}_{\text{grid}}=\text{P}_{ \text{load}};\text{P}_{\text{ESS}}=0.\] (25)  
For isolated conditions, no power can be transferred to or absorbed from the electricity grid. Therefore, if the balance between generation and load demand does not occur, the battery must supply or absorb the difference. However, the DG units must reduce their generated power to meet the load demand if the battery is completely charged. Thus, the following conditions must be verified:  
* Isolated mode, DG > Pload, battery not fully charged: \[\text{P}_{\text{PV}}+\text{P}_{\text{WT}}-\text{P}_{\text{ESS}}=\text{P}_{ \text{load}};\text{P}_{\text{grid}}=0,\text{PV},\text{ and wind units under MPPT};\] (26)
* Isolated mode, DG > Pload, battery fully charged: \[\text{P}_{\text{PV}}+\text{P}_{\text{WT}}=\text{P}_{\text{load}};\text{P}_{ \text{grid}}=0;\text{P}_{\text{ESS}}=0,\text{PV},\text{ and wind units under reduced power mode};\] (27)
* Isolated mode, DG = Pload: \[\text{P}_{\text{PV}}+\text{P}_{\text{WT}}=\text{P}_{\text{load}};\text{P}_{ \text{grid}}=0;\text{P}_{\text{ESS}}=0,\text{PV},\text{ and wind units under MPPT};\] (28)
* Isolated mode, DG < Pload: \[\text{P}_{\text{PV}}+\text{P}_{\text{WT}}+\text{P}_{\text{ESS}}=\text{P}_{ \text{load}};\text{P}_{\text{grid}}=0,\text{PV},\text{ and wind units under MPPT}.\] (29)
# DNCL: Hybrid DOA Estimation and NMDS Cooperative Multi-Target Localization for RFID
## 6 Conclusions  
In this paper, we propose the DNCL system, based on hybrid direction-of-arrival estimation and nonmetric multidimensional scaling for RFID localization. DNCL uses the tag phase information obtained from a simple 1D scan performed by a single antenna to achieve spatial coordinate localization of tagged objects. The virtual antenna array performs rank-recovery DOA estimation for the acquired phase information. We propose determining the characteristics of each tag using a linear model of the angle profile and introduced the NMDS algorithm to achieve spatial localization with high accuracy and strong robustness. The results of the series of experiments were performed in the indoor environment and showed that the proposed method can achieve high accuracy positioning of passive tags. Looking ahead, we will attempt to further improve the method for practical application of DNCL to solve more complex situations and hope that our work can be applied to warehouse management-related industries.  
Conceptualization, Y.M.; methodology, Y.L., C.T.; software, Y.L.; validation, Y.M., Y.L. and D.S.; investigation, Y.L., C.T.; writing--original draft preparation, Y.L.; writing--review and editing, Y.M., B.Y. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{c} **Localization** \\ **Scheme** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Number of** \\ **Antennas** \\ \end{tabular} } & \multirow{2}{*}{**Information**} & \multirow{2}{*}{**Method**} & \multirow{2}{*}{**System Setting**} & \multicolumn{2}{c}{\
\begin{tabular}{c} **Distance** \\ **between** \\ **Antennas and** \\ **Tags** \\ \end{tabular} } & \multirow{2}{*}{**Localization Accuracy**} \\  & & & & & **Tags** & \\ \hline SARFID [(42)] & two antennas & phase & Hologram-based & coordinate system & 1.95 m & 6.3 cm (X-axis) \\ STPP [(43)] & one antenna & phase & Proximity-based & 0.3 m & below 6 cm (X-axis) \\ Tagoram [(21)] & four antennas & phase & Hologram-based & coordinate system & \textbackslash{} & 6.35 cm (X-axis) \\ ReLoc [(35)] & two antennas & phase \& RSSI & Proximity-based & tilted angle \& 1.2 m & 7 cm (X-axis), 93.8\% (level ordering) \\ HyLoc [(23)] & one antenna & phase &
\begin{tabular}{c} Hyperbolic- \\ based \\ \end{tabular} & coordinate system & 0.5 m & 5.43 cm (X-axis) \\ DNCL (our proposal) & one antenna & phase & DOA-based & coordinate system & 2.5 m & 4.73 cm (X-axis), 6.76 cm (Z-axis), 8.47 cm (combined) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of the common RFID localization methods.
# LoRA-Tuned Multimodal RAG System for Technical Manual QA: A Case Study on Hyundai Staria
## 3 Data Construction Methodology
### Data Quality Management Measures  
To construct high-quality question-answering datasets, this study introduced a multistage quality review system based on quantitative criteria and procedure-centered approaches. This review procedure includes three stages: manual review, GPT-based automatic evaluation, and structural verification, with all QA items included in the final dataset after going through this process.  
In the first review stage, manual filtering was performed based on consistency with actual maintenance content in documents, accuracy of technical terms, and clarity of expression. Responses containing semantic inconsistency, contextual disconnection, typos, or inappropriate schematic references were excluded, and similar sentences or duplicate responses were merged or deleted.  
In the second review, we used GPT-4 based models to automatically evaluate sentence quality, response logical structure, and explanation naturalness. This model calculated grades for each response based on predefined criteria (keywords included in questions, response logical structure, clarity), with responses below certain standards manually rewritten.  
The third review was a data format consistency review stage, confirming that question, context, answer, or qa list structures were correctly entered according to JSONL structure. Structural problems such as line break errors within sentences, nested keys, and missing fields were detected by automatic scripts and corrected in batches.  
Additionally, to ensure accuracy of image-text mapping configured for documents containing visual information, two independent reviewers conducted cross-evaluation on 50 randomly extracted pairs. Review results showed a disagreement rate of 3.7%, with final mapping accuracy measured at 96.3%. Disagreement cases were adjusted through third researcher judgment.  
Through this systematic quality management procedure, the constructed dataset secured reliability and consistency sufficient for use not only in RAG learning but also as an evaluation benchmark, and applicability in actual maintenance situations was also reviewed.
## Materials and Methods
### Mass Spectral Data Processing  
The identification of compounds was achieved by comparing the mass spectra of the chromatographic peaks with those of the Wiley 275 MS database. For semi-quantification, compound concentration was expressed as ng/g based on the ratio: peak area of analyte/peak area of internal standard (4-methyl-2-pentanone), considering the density (0.8 g/mL) of the internal standard. For validation purposes, volatile compounds having only \(\geq\)80% similarity with the Wiley mass spectral library were tentatively identified using GC-MS spectra. For the determination of linear retention indices, a mixture of n-alkanes (C\({}_{6}\)-C\({}_{20}\)) dissolved in n-hexane was employed. The mixture was supplied by Sigma Aldrich (Darmstadt, Germany). The calculation was carried out for components eluting between n-octane and n-eicosane using Equation (1).
## 5 Conclusions  
In the present study, four types of laminated membrane coupling stiffness are studied through the finite element method. To this end, torsion, axial, radial and angular stiffness are studied. Moreover, the torsional stiffness is verified through the static torsional test, and the influence of flange elasticity and friction coefficient between laminated plates on the stiffness characteristics of coupling is studied. Based on the results, the authors concluded that:  
1. The finite element model of the laminated membrane coupling with multi-layer laminated contact is established, and its torsional stiffness is analyzed. Based on the electro-hydraulic servo shaft torsional fatigue testing machine, the static torsional test of the laminated membrane coupling is carried out. Obtained results from the numerical simulation are in excellent agreement with those from the experiment. Therefore, the accuracy of finite element simulation is approved.  
Figure 23: Influence of the friction coefficient on the axial stiffness.  
Figure 24: Influence of the friction coefficient on the radial stiffness.  
Figure 25: Influence of the friction coefficient on the diagonal stiffness.  
Based on the finite element simulation, torsional, axial, radial and angular stiffness of the laminated membrane coupling and laminated group are analyzed. Obtained results show that the order of magnitude of torsional and radial stiffness is far greater than that of axial and angular stiffness. It indicates that laminated membrane couplings are rigid in torsional and radial directions so that only axial and angular displacements are compensated. Moreover, it is found that all stiffness values of couplings are smaller than that of laminated groups. Because, the existence of the flange is equivalent to that of laminated groups in all directions with series connection. The influence degree of the flange on all the stiffness of coupling is in order of torsional, axial, angular and radial stiffness.
3. Through the finite element simulation, the influence of the bolt preload and friction coefficient between the laminations on the four types of coupling stiffness is studied. Obtained results show that increasing the preload will increase the torsion, axial, radial and angular stiffness, while increasing the friction coefficient will reduce the torsion, axial, radial and angular stiffness. It is found that variation of both factors has the greatest impact on the axial stiffness.  
Conceptualization, M.L. and Y.Z.; methodology, M.L.; validation, P.L. and R.Z.; writing--original draft preparation, M.L.; writing--review and editing, Y.Z.; All authors have read and agreed to the published version of the manuscript.  
This research is supported by the National Natural Science Foundation of China (Grant No. 51775265), and the National Key Laboratory of Science and Technology on Helicopter Transmission (Nanjing University of Aeronautics and Astronautics) (Grant No. HTL-A-19G09).  
The authors declare no conflict of interest.
# To What Extent Are the Effects of UV Radiation on Grapes Conserved in the Resulting Wines?
## 2 Results
### Summarizing the Effects of UV Radiation on Wines by PCA  
In the PCA performed using the phenolic compounds and VOCs of wines (Figure 3), the accumulated variance by the first two axes was 51% (28% for axis I, and 23% for axis II). Loading factors for the positive part of axis I were mainly flavonoids and anthocyanins, together with one fatty acid (nonanoic acid) and one alcohol (2-methyl-1-butanol), and for the negative part, several esters. Loading factors for the positive part of axis II were flavonols, anthocyanins, phenolic acids, and stilbenes, and for the negative part, some esters (different from those acting as loading factors for axis I) and fatty acids. Loading factors were evidently concentrated towards the positive parts of both axes, as can be seen from the density of arrows. Wine samples were clearly ordinated according to the radiation regime to which grapes had been exposed, and replicates of each regime clustered more or less together, confirming the consistency of the results. P samples, elaborated with non-UV-exposed grapes, were placed in the fourth quadrant, clearly separated from the remaining samples. This separation was due to the relatively low concentrations of flavonoids and anthocyanins, and the relatively high concentrations of fatty acids, in P samples. Among the wines made from UV-exposed grapes, PA samples were clustered on the more negative part of axis II because of their high concentrations of several esters, and their relatively low concentrations of flavonoids. PB, PAB, and PAB\(\dagger\) samples, all of which had been made with grapes exposed to UV-B or a combination of UV-B and UV-A, were distributed along the positive (and less negative) part of axis I, in the first and second  
Figure 2: Relative abundance (percentages) of families of volatile organic compounds (VOCs) in wines made from grapes exposed to five different radiation regimes: P (only PAR), PA (PAR + UV-A), PB (PAR + UV-B), PAB (PAR + UV-A + UV-B), and PAB\(\dagger\) (PAR + UV-A + enhanced UV-B). For each variable, different letters indicate significant differences between radiation regimes (post hoc Tukey test after a one-way ANOVA test using radiation regime as main factor). Means \(\pm\) SE are shown. quadrants of the plot. PB samples were closer to PA samples than PAB and PAB\(\uparrow\) samples. Clearly, the higher concentrations of flavonoids, anthocyanins, and hydrocarbons, displaced both PAB and PAB\(\uparrow\) samples towards the positive part of axes I and II. PAB and PAB\(\uparrow\) replicates were not totally separated by PCA, but remained somewhat mixed. Figure 3: Ordination, through principal components analysis (PCA), of wines made from grapes exposed to five different radiation regimes: P (only PAR), PA (PAR + UV-A), PB (PAR + UV-B), PAB (PAR + UV-A + UV-B), and PAB\(\uparrow\) (PAR + UV-A + enhanced UV-B). PCA was based on the content of individual phenolic compounds and volatile organic compounds (VOCs). Biological replicates of treatments were used for ordination. Significant loading factors are shown as arrows.
###### Abstract  
Data receiving frontends using avalanche photodiodes are used in optical free-space communications for their effective sensitivity, large detection area, and uncomplex operation. Precise control of the high voltage necessary to trigger the avalanche effect inside the photodiode depends on the semiconductor's excess noise factor, temperature, received signal power, background light, and also the subsequent thermal noise behavior of the transimpedance amplifier. Several prerequisites must be regarded and are explained in this document. We focus on the application of using avalanche photodiodes as data receivers for the on/off-keying of modulated bit streams with a 50% duty cycle. Also, experimental verification of the performance of the receiver with background light is demonstrated.  
optical avalanche photodiode receiver frontend; RFE; InGaAs-APD; bias voltage control; temperature control; Q-factor; background light; free-space optical communication; FSO +
Footnote †: journal: 7  
0000-0001-6977-330][PERSON]  
0000-0002-3391-6677]German Aerospace Center (DLR), D-82234 Wessling, Germany; [EMAIL_ADDRESS]