esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Selection of Sorbent Material and Solvent for Desorption  
Both extraction and desorption are very important steps in the development of the SPME method. Therefore, at the very beginning, the selection of the coating sorbent and solvent used for desorption was done simultaneously. One must take into account that an appropriate desorption sorbent is crucial in SPME coupled with HPLC, not only to desorb the analytes but also because it determines the possibility of multiple sorbent usage.  
To select the best coating sorbent, DES-1, DES-2, DES-3 and DES-4 (each in molar ratios 1:1, 1:2, 1:3) were tested. The composition of these sorbents indicates their slight polarity which should increase the sorption of derivatized formaldehyde. To select the best desorption solvent, mixtures of methanol/water and acetonitrile/water were used, but desorption in the mixture of acetonitrile/water. For that reason, in the next stage four mixtures were tested that is 90:10, \(v/v\), \(\mathrm{ACN}/\mathrm{H}_{2}\mathrm{O}\); 80:20, \(v/v\), \(\mathrm{ACN}/\mathrm{H}_{2}\mathrm{O}\); 70:30, \(v/v\), \(\mathrm{ACN}/\mathrm{H}_{2}\mathrm{O}\); 60:40, \(v/v\), \(\mathrm{ACN}/\mathrm{H}_{2}\mathrm{O}\).  
The mass and thickness were kept almost constant for all coatings. The tests were conducted using a 200 mL sample containing 125 ng mL\({}^{-1}\) of formaldehyde (after derivatization) with 0.05 g mL\({}^{-1}\) NaCl added, and the extraction time and desorption time were 30 min and 12 min, respectively. Figure 3 shows that the best results were obtained for DES-3(1:3) with a mixture of \(\mathrm{ACN}/\mathrm{H}_{2}\mathrm{O}\) in a ratio of 90:10 (\(v/v\)) as a desorption solvent. The structure of DES-3 indicates its more polar properties than that of the other tested DESs. It shows that interactions between the analyte and the sorbent may be considerably influenced by their polar nature. Thus, for further tests, DES-3 and \(\mathrm{ACN}/\mathrm{H}_{2}\mathrm{O}\) in a ratio of 90:10 (\(v/v\)) were selected.
# End-to-End Multi-Modal Speaker Change Detection with Pre-Trained Models
## 4 Experiments  
### Model Setup  
The model utilizes pre-trained Wav2 Vec2 and Bert, specifically wav2 vec2-base and bert-base-cased variants. Training employs the AdamW optimizer with a learning rate scheduler, ensuring stable updates and effective convergence. The base model components (Wav2 Vec2 and Bert) use a learning rate of \(1\times 10^{-5}\), while the classifier has a higher learning rate of \(1\times 10^{-3}\). A weight decay of 0.01 is applied for regularization. To enhance stability, a warmup ratio of 0.1 is used, meaning the learning rate gradually increases during the first 10% of total training steps before following a linear decay. Training is conducted for 10 epochs, with the total number of training steps computed as the product of the number of epochs and the length of the training dataloader. Additionally, a random seed is set to ensure reproducibility across experiments.  
The classifier is designed to process the features with a hidden size of 768. The head of self-attention is set to 8. The classifier consists of three fully connected layers with 512 and 256 hidden units, each followed by layer normalization, ReLU activation, and dropout (0.4), leading to a final output layer for classification.  
Each of the three configurations, multi-modal, text-only, and audio-only, supports two training modes: (i) a freeze mode (F); (ii) a non-freeze mode (NF). In freeze mode, all Wav2 Vec2 and Bert parameters are fixed, and only the classification layers are trained. In non-freeze mode, the configurations differ as follows: both base models with their classification layers are trained. For the focal loss of the model, \(\alpha\) is set to 0.31, and \(\gamma\) is set to 2.0. For convenience in naming, we use eMD to refer to the proposed approach. Using the different settings described above, we define two versions of **eMD**: (i) A multi-modal model without self-attention and cross-attention layers, which consists only of the three fully connected layers mentioned earlier for classification. This model is denoted as **eMD**. (ii) A model with self-attention layers, which enhances feature interaction. This version is denoted as **eMD\({}_{att}\)**.
# Model-Based Real-Time Motion Tracking Using Dynamical Inverse Kinematics
## 3 Method
### Constrained Inverse Differential Kinematics  
The inverse differential kinematics is the problem of inverting the differential kinematics presented in Equation (4b) in order to find the configuration state velocity \(\
u(t)\) for a given set of task space velocities. In order to compute the control input \(\
u(t_{k})\) from (9), it is required to solve the inverse differential kinematics for the corrected velocity vector \(\
u^{*}(t_{k})\). Different strategies to solve the inverse differential kinematics can be found in literature [17; 18; 35; 36], among the possible solutions, the most common approach is to use Jacobian generalized inverse [37]. In order to take into account also the model constraints, however, a Quadratic Progamming (QP) solver is preferred since it allows to introduce a set of constraints to the problem [38]. Hence, the inverse differential kinematics solution can be defined as the following QP optimization problem:  
\[\underset{\
u(t_{k})}{\text{minimize}} \left\|\
u^{*}(t_{k})-J(q(t_{k-1}))\
u(t_{k})\right\|_{2}\] (10a) subject to \[G\delta(t_{k})\leq g. \tag{10b}\]While the joint velocities constraints can be used in the Equation (10b), joint position constraints, of the form  
\[As(t_{k})\leq b^{q}, \tag{11}\]  
can not be directly enforced in (10b) since they are independent from joint velocities \(\dot{s}(t_{k})\). For this reason, we propose a strategy that aims at converting joint space constraints (11) to velocity constraints such as (10b).
###### Abstract
Instead of replacing existing paradigms outright, our work demonstrates how semantic processing can be incrementally introduced into current speech communication systems, ensuring practical viability. By seamlessly integrating semantic-aware speech processing into IoT ecosystems, our framework elevates voice-driven interactions in critical domains such as industrial automation, healthcare monitoring, and smart city infrastructure. Designed for practical deployment, the system operates harmoniously with existing edge networks, avoiding costly hardware upgrades while delivering reliable performance in resource-limited environments--an essential feature for scalable IoT solutions. The rest of this paper is organized as follows: Section 2 describes the system model, Section 3 details the experimental setup and implementation, Section 4 presents the results and performance analysis, and Section 5 concludes the study with key findings and future research directions.
# Enhanced Intrusion Detection Systems Performance with UNSW-NB15 Data Analysis
## 4 Dataset Description and Properties
### Dataset Features  
As shown in Table 1, further explanations of the important features, its description and data are highlighted. Proto, service and state are the only categorical features present in the UNSW_NB15 dataset. sybytes and dbytes attributes are used to create a new feature called network_byte. For more details on the dataset description, please refer to [31]. The dataset has 49 attributes in total, which are split into three different data types: binary, nominal, and categorical. The attributes are a combination of both Packet-based and Flow-based features. Furthermore, the dataset consists of nine different attacks in addition to the normal class.  
The dataset is available in 4 CSV files, below are the details:  
Figure 1 illustrates the step-by-step process used to build a robust machine learning model for detecting cyber-attacks. The following steps were carried out.  
* Data collection and cleaning: The dataset was collected from the four files listed in Table 2. Target features were identified, null values were checked, and numerical and categorical features were identified.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Feature Name** & **Data Type** & **Description** \\ \hline proto & nominal & Transaction protocol \\ \hline state & nominal & Represents the state \\ \hline service & nominal & Indicates the used services such as http, ftp, \\  & & smtp, ssh, dns, ftp-data, ic and none (-) \\ \hline sybytes & Integer & Number of bytes used from source to destination \\ \hline dbytes & Integer & Number of bytes from destination to source \\ \hline attack\_cat & nominal & Attack types \\ \hline label & binary & Indicates to normal (0) or attack (1) records \\ \hline \hline \end{tabular}
\end{table}
Table 1: UNSW-NB15 Features.  
Figure 1: Overview of Data Processing and ML Implementation of UNSW_NB15 Dataset.  
* Exploratory Data Analysis: A heatmap was used to identify and remove highly correlated values.
* Feature Engineering and Data Preparation: New features were added, and the data was standardized using the standard scalar and Onehotencode functions.
* Train and Test Data Split: A training set (70%) and a testing set (30%) were generated from the dataset.
* Training and Testing of the Models: On the training set, methods for logistic regression, decision trees, random forests, and linear SVM were applied; on the testing set, their efficacy was assessed.
## 5 Conclusions  
In this paper, we investigate high performance beamforming for communications between UAVs and cellular networks. The UAV's signal is estimated by a proposed DoA estimation algorithm and its variations are also predicted by using position information. The proposed algorithm maintains precise DoA estimations, and is of low complexity. Meanwhile, by using DoA information from the Command&Control Links instead of from pilot signals, the proposed algorithm incurs much less training overhead to the network. The algorithm is applicable to beamforming, based on which a beamforming scheme with real-time steering vector updates is also proposed. As proved by simulation results, by applying the proposed beamforming scheme higher DoA accuracy and more stable beamforming gains are obtained compared with traditional beamforming methods. In the meantime, training overhead is proved to be reduced significantly.  
Conceptualization, T.Z. and C.L.; methodology, T.Z. and C.L.; software, T.Z.; validation, T.Z. and D.G.; formal analysis, T.Z.; investigation, T.Z. and C.L.; resources, C.L. and J.Z.; data curation, T.Z.; writing-original draft preparation, T.Z., writing-review and editing, T.Z., C.L., J.Z., D.G., N.C. and P.C.-d.-l.-H., visualization, T.Z. and C.L.; supervision, C.L. and J.Z.; project administration, C.L.; funding acquisition, C.L. and J.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### X-Ray Diffraction
As the average crystallite size in the C\({}_{60}\)-8h thin film is larger (41.54 nm), the strain is smaller (\(-0.001\)), showing an acceptable resulting film. Figure 5: _Cont._  
The lattice parameter (a) of the unit cell is evaluated according to the following formula [55; 56]:  
\[d_{hkl}=\frac{a}{\sqrt{(h^{2}+k^{2}+l^{2})}} \tag{4}\]  
where \(d_{hkl}\) is the interplanar spacing calculated from Bragg's law (\(2d_{hkl}\sin\theta=n\lambda\)) [44]. The calculated lattice parameter values are given in Table 2. The trivial change in the lattice parameter (a (A)) values depicted in Table 2 can be due to the ultrasonic agitation. A slight compression occurred in C\({}_{60}\)-5h, which resulted from the average crystallite size becoming smaller (34.35 nm) compared to the powder, where \(D_{Sh}\) is 37.51 nm. To represent the variation in the crystallite size and the lattice parameters of the resulting films compared to the powder, Figure 6 shows the agreement between these two parameters. The C\({}_{60}\) thin film IR spectra revealed several pertinent absorption peaks at 525, 575, 721, 800, 1180, 1373, and 1464, ascribed to C-C vibrational modes. The peak at 1736.89 is assigned to the C=O vibration mode [57], which may result from environmental exposure, as revealed by the EDS data shown in Figure 8, and those at 2842.11, 2914.72, and 2956.43 cm\({}^{-1}\) are attributed to C\(-\)H stretching bands [58]. All of these peaks were found to be in agreement with previous studies [57,59]. The C=C bonds, as indicated in the samples (Figure 7), are responsible for \(\pi\)-orbitals, which at their turn are delocalized over the entire molecule, leading to the formation of a continuous \(\pi\)-electron system. Forming molecular orbitals that are occupied by \(\pi\)-electrons enables electrons to move freely within the molecule, influencing its electronic properties.
###### Abstract
This could help researchers in the field of intrusion detection to define their goals more exactly, and determine experimental settings as well as techniques to face these challenges more clearly. Thus, the contribution of the paper is as follows:  
* The review of the architectures of the federated learning systems, including supported data partitions and requirements to the clients' availability and their computational resources. * The review of the datasets that were used to evaluate the system and approaches to model federated settings. * Comparative analysis of the proposed systems. While there are multiple surveys on federated machine learning [6; 15; 18; 29] that focus on different issues of the federated learning, there is a lack of surveys related to the intrusion detection systems based on federated learning. For example, in [15], the authors presented the most comprehensive survey on the open theoretical problems and challenges in FL. [PERSON] et al. [18] investigate possible usage scenarios of FL in healthcare systems, define typical FL settings, and discuss the research questions specific for this application domain. In [30], the authors focus on security and privacy issues in federated learning, and [PERSON] et al., in [29], evaluate the privacy-preserving mechanisms in FL systems and their applicability to driver and/or vehicle activity recognition. The closest survey to this one is [31]. The authors also review FL-based approaches to intrusion detection in IoT. However, unlike them, we focus on IDS architectural solutions and evaluate them in the context of the FL characteristics such as communication scheme and data partition, we evaluate the used datasets and achieved accuracy, and discuss what performance metrics are used. Thus, the _novelty_ of the paper consists of the analysis of existing research, advantages, and open challenges of federated learning for intrusion detection. The rest of the paper is organized as follows. A brief description of the federated learning systems is presented in Section 2. The typical architectural solutions for intrusion detection systems are given in Section 3. Section 4 discusses the research methodology and comparative criteria for the FL-based IDS systems and presents a detailed description of the selected approaches and their comparative analysis. The paper ends with a summary of the most important results of the research and guidance for future research.
# Towards Secure Searchable Electronic Health Records Using Consortium Blockchain
## 5 Discussion
### Our Protocol Ensures Access Control  
In our proposed protocol, the patient transmits his or her private key along with the keyword as a trapdoor to enable the third party to query the consortium blockchain using smart contracts. It is used to find matched keywords that have been encrypted with the doctor's public key. As a result, the patient has control over the data search. Meanwhile, following each EHR request, the system management produces a re-encryption key and transfers it to a private blockchain for proxy re-encryption. Only an authorized third party can decrypt the re-encrypted ciphertext in this manner. As a result, the patient has control over who can access his or her data.
## 5 Conclusions  
This paper presents an NIBP calibrator that aims to assess oscillometric testing results without being affected by the empirical proprietary algorithms used by the different BAPMD manufacturers. By adjusting the pulse envelope signal used for the evaluation of the systolic and diastolic pressures, users can simulate different blood pressure parameters and test how different NIBP monitors respond to those signals. Another interesting characteristic of the proposed NIBP calibrator is related to the feedback topology of the proposed prototype that enables easy implementation of auto-calibration and auto-testing capabilities, which are essential in assuring accurate measurements as well as detecting faulty equipment conditions of a BP calibrator. This feedback topology enables dual capabilities of pressure stimulation and pressure measurement, with it also being possible to mimic the pressure patterns or pressure signatures previously acquired and to perform not only the BAPMD test but also the complete test of the pressure measuring channel, which includes the pneumatic channel, devices connecting the tubing and pneumatic loads, as is the case of the BP measuring cuff. This capability is important not only for other biomedical applications, such as the non-nutritive sucking measurement and stimulation of premature babies, but also for other applications where the accuracy of the measurement of time-variable pressure signals is essential. Simulation and experimental results that were obtained validate the theoretical expectations and show a very acceptable level of accuracy and performance of the presented NIBP calibrator prototype. Future work must be targeted at validating the proposed prototype using a larger number of BP monitors and calibrators from different certified manufacturers and at validating their algorithms using patient data, real human data and available blood pressure datasets to obtain a more complete characterization of the proposed prototype, identify potential limitations and study the best way to surpass them.  
Conceptualization, J.M.C.D.P., O.P. and G.R.; methodology, J.M.C.D.P.; software, J.M.C.D.P.; validation, J.M.C.D.P. and O.P.; formal analysis, J.M.C.D.P. and O.P.; investigation, J.M.C.D.P. and O.P.; resources, J.M.C.D.P., O.P. and G.R.; data curation, J.M.C.D.P., O.P. and G.R.; writing--J.M.C.D.P., O.P. and G.R.; writing--review and editing, J.M.C.D.P.; visualization, J.M.C.D.P., O.P. and G.R.; supervision J.M.C.D.P.; project administration, J.M.C.D.P. and O.P.; funding acquisition, J.M.C.D.P. and O.P. All authors have read and agreed to the published version of the manuscript.  
This work is funded by FCT/MECI through national funds and co-funded EU funds under UID/50008: Instituto de Telecomunicacoes.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
# Evaluating V2X-Based Vehicle Control under Unreliable Network Conditions, Focusing on Safety Risk
## 4 Results and Discussion
### High Relative Velocity Scenario  
Finally, we investigated a scenario in which the remote vehicle slows down suddenly due to an unforeseen event on the road. Due to the high delta velocity, none of the modulation techniques were able to react in time to prevent the accident, as illustrated in Figure 9. SRI was high from the beginning of the simulation due to the high velocity differences and, in the case of the 64 QAM modulation, the network degradation. Following the collision, which occurred approximately 11 s into the simulation, the presented plots are no longer valid. However, this illustrates that even the QPSK modulation was no longer within the reception distance.  
Figure 8: Results from SRI-based CACC for moderate relative velocity (13.89 m/s = 50 km/h).  
At last, we can assess the effectiveness of our proposed control in the context of a high delta velocity scenario. As previously observed, SRI was already above our target value at the beginning of the simulation due to the high velocities and the degraded network. Consequently, the control reduced the HV velocity to 10 m/s, as illustrated in Figure 10. With this method, we chose a more careful option, and the vehicle was able to adapt to the speed of the remote vehicle and keep a safe distance with each modulation.  
Figure 9: Results from simple CACC for high relative velocity (22.22 m/s = 80 km/h).
###### Abstract  
Solar induced chlorophyll fluorescence has been shown to be increasingly an useful proxy for the estimation of gross primary productivity (GPP), at a range of spatial scales. Here, we explore the seasonality in a continuous time series of canopy solar induced fluorescence (hereafter SiF) and its relation to canopy gross primary production (GPP), canopy light use efficiency (LUE), and direct estimates of leaf level photochemical efficiency in an evergreen canopy. SiF was calculated using infilling in two bands from the incoming and reflected radiance using a pair of Ocean Optics USB2000+ spectrometers operated in a dual field of view mode, sampling at a 30 min time step using custom written automated software, from early spring through until autumn in 2011. The optical system was mounted on a tower of 18 m height adjacent to an eddy covariance system, to observe a boreal forest ecosystem dominated by Scots pine. (_Pinus sylvestris_) A Walz MONITORING-PAM, multi fluorimeter system, was simultaneously mounted within the canopy adjacent to the footprint sampled by the optical system. Following correction of the SiF data for O\({}_{2}\) and structural effects, SiF, SiF yield, LUE, the photochemicsl reflectance index (PRI), and the normalized difference vegetation index (NDVI) exhibited a seasonal pattern that followed GPP sampled by the eddy covariance system. Due to the complexities of solar azimuth and zenith angle (SZA) over the season on the SiF signal, correlations between SiF, SiF yield, GPP, and LUE were assessed on SZA <50\({}^{\circ}\) and under strictly clear sky conditions. Correlations found, even under these screened scenarios, resulted around \(\sim\)r\({}^{2}\) = 0.3. The diurnal responses of SiF, SiF yield, PAM estimates of effective quantum yield (\(\Delta F/F_{m}\)), and meteorological parameters demonstrated some agreement over the diurnal cycle. The challenges inherent in SiF retrievals in boreal evergreen ecosystems are discussed.  
**Keywords: solar-induced chlorophyll fluorescence (SiF); seasonal dynamics; photosynthetic efficiency; proximal remote sensing; coniferous forest; gross primary productivity (GPP); light-use efficiency (LUE); Fraunhofer Line Discriminator (FLD); flux tower**
# Early Evidence That Soil Dryness Causes Widespread Decline in Grassland Productivity in China
## 2 Materials and Methods  
### Materials  
#### 2.1.1 GPP Datasets  
We used the model outputs from four global vegetation dynamics models (CARAIB, LPJ GUESS, LPJmL, ORCHIDEE DGVM) as GPP (monthly, 0.5\({}^{\circ}\)) products. The model GPP products are all derived from the harmonized general circulation model IPSL-CM5A-LR as meteorological forcing data, considering historical (1901-2005) and further (2006-2099) scenarios. The results of the model simulations of GPP are all implemented strictly in accordance with the ISIMIP 2b standard protocol, so differences between model outputs are only related to the complexity of the model. Considering that a single model only provides valuable and usable insights at the regional scale, to effectively eliminate intra-model variability and reduce uncertainty in GPP products, we conducted pooled averaging of multi-model GPPs for further processing. Since only LPJmL of the four models of ISIMIP 2b provides mainstream high-emission RCP8.5 climate projections, the future scenarios consider only medium to high GHG emission scenarios (RCP6.0).