esa-sceva/satcom-chunk-collection huggingface dataset:
# A Low Phase Noise Dual-Loop Dual-Output Frequency Synthesizer in SiGe BiCMOS
## 2 Design and Implementation of the Dual-Loop Dual-Output Frequency Synthesizer  
In this article, the dual-loop dual-output frequency synthesizer is proposed for the IEEE802.11 aj (45 GHz) applications, and the block diagram is shown in Figure 3. In the proposed dual-loop synthesizer, the frequency of LO1 is fixed at 12.96 GHz, and the frequency of LO2 varies between 29.835-35.1 GHz for the channel selection. In this way, the output RF carrier frequency (LO2 + LO1) is 42.795-48.06 GHz, while the image frequency (LO2-LO1) is 16.875-22.14 GHz, which is reasonably away from LO1 and LO2. If needed, a bandpass filter is easy to design to suppress unwanted signals. Thus, the frequency allocation scheme is carefully chosen to simplify IF transceiver and filter design, avoiding most spurious mixing product falling into the signal band. Meanwhile, a RF down-conversion mixer is proposed in Loop2, where the Loop1's output (12.96 GHz signal) is also the LO signal for the mixer. In this way, power consumption can be reduced by omitting high-speed prescaler/MMD, and in-band phase noise can be improved, as well. The frequency step also can be reserved as reference frequency with the Integer-N synthesizer.  
Considering the frequency synthesizer implementation issues, such as the VCO phase noise performance, harmonic-frequency suppression, DC power consumption, etc., the LO1 at Ku-band (12.96 GHz) can be generated directly from VCO1, and LO2 at Ka-band from 29.835 to 35.1 GHz can be generated with VCO2, followed by a frequency doubler. In order to cover all the 540/1080 MHz channels in IEEE802.11 aj (45 GHz) standard, the reference frequency of 67.5 MHz is chosen for the dual-loop Integer-N synthesizer. The VCO1, VCO2, doubler, mixer, and prescaler (in Loop1) are designed with SiGe HBT devices for low-phase noise and high-speed performance in Ku/Ka-band, which are designed and optimized with full-wave EM simulator to take into account the parasitic effects. Phase-frequency detector (PFD), charge pump (CP), and low dropout regulators (LDOs) are designed with thick gate-oxide CMOS transistors and MMD1/MMD2. Serial peripheral interface (SPI) circuits are designed with thin gate-oxide CMOS transistors, both offered with the 0.13 \(\upmu\)m SiGe BiCMOS technology. The on-board loop filters (LPF1 and LPF2) are not integrated on the chip for flexibility and saving chip area.  
Figure 3: Block diagram of the proposed dual-loop dual-output frequency synthesizer for IEEE802.11 aj (45 GHz) standard.
## 3 Study Results  
### Kinematic PPP Positioning Accuracy Using Single-Frequency Observations  
The study of kinematic PPP accuracy using single-frequency observations is crucial for robot localization systems as it provides a low-cost solution compared with the high-cost solution using dual-frequency observations [30]. The results of this study show that kinematic PPP positioning accuracy is computed relative to the reference differential solution. Figure 5a presents kinematic PPP coordinate differences using GPS single-frequency observations. Figure 5b presents kinematic PPP coordinate differences using GLONASS single-frequency observations. Figure 5c presents kinematic PPP coordinate differences using mixed GPS/GLONASS single-frequency observations.
## 3 RRSS Protocol
### RRSS Scheme
#### 3.3.1 Residual Energy  
In RRSS, sensor nodes on the bottom only generate data packets and hello packets. The other nodes can forward data packets as well as generate packets. Compared with the nodes in receiving and idle status, the node in sending status consumes more energy, so the energy consumption in receiving and idle status is neglected in our paper.  
For sensor nodes on the bottom, the residual energy can be calculated as Equation (7).  
\[E_{r}=E_{0}-N_{D}E_{D}-N_{H}E_{H} \tag{7}\]  
Here, \(E_{0}\) denotes the initial energy of underwater nodes; \(N_{D}\) and \(N_{H}\) represent the number of data packets and hello packets generated respectively; and \(E_{D}\) and \(E_{H}\) represent the energy consumption of sending one data packet and one hello packet respectively. For those underwater sensor nodes which are not on the bottom, the residual energy can be calculated as Equation (8)  
\[E_{r}=E_{0}-N_{D}E_{D}-N_{H}E_{H}-N_{F}E_{F} \tag{8}\]  
Here, \(N_{F}\) represents the number of forwarding data packets; and \(E_{F}\) represents the energy consumption of forwarding one data packet. Comparing Equation (7) with Equation (8), we can see the shallower the node is, the shorter the node lives.  
In RRSS, to avoid data packets being delivered to the nodes without enough energy, an energy threshold is set in advance. Only nodes with residual energy greater than the energy threshold are qualified to be candidate next-hop nodes.
###### Abstract  
In areas such as remote, rural, and mountainous regions, supplying low-voltage three-phase power has traditionally required distribution line extension and transformer installation. However, these areas often yield low electricity revenues, making cost recovery difficult for utilities. To address this challenge, this paper proposes a Single-to-Three-Phase Converter (STPC) capable of converting single-phase low-voltage input into three-phase output for use in low-voltage distribution systems. The STPC topology employs a single-phase half-bridge AC-DC stage and a three-phase full-bridge inverter stage using SiC-MOSFETs. To validate the system, simulations and experiments were conducted under various load conditions, including unbalanced, nonlinear, and motor loads. The results show that STPC maintains output stability while minimizing impact on the existing grid. The findings demonstrate STPC's feasibility as an alternative to conventional line extension and transformer installation, with potential for application in grid-forming and low-voltage distribution current (LVDC) systems.  
distribution system; three phase; power electronics; power semiconductor; Silicon Carbide (SiC); unbalanced loads; motor loads; nonlinear loads; phase converter; inrush current
# Generalized Replay Spoofing Countermeasure Based on Combining Local Subclassification Models
## 5 Experiments
### Ensemble Learning  
On the basis of the findings of previous experiments, we only considered CQCC features in this experiment. Next, we applied the CA [13] algorithm to estimate the optimal cluster number. The learned fuzzy memberships were next used as the initial values for the SCAD [15] clustering algorithm. Three classifiers were first considered separately: SVM [17], GMM [6], and XGboost [18]. Next, the results of these classifiers were combined using the majority vote ensemble learning strategy. Table 9 depicts the achieved ERR of the considered systems. As shown in Table 9, the proposed approach based on SVM [17] outperformed those based on XGboost [18] and GMM [6] with an ERR equal to 0.154%. Furthermore, the ensemble majority voting strategy further improved performance by achieving an ERR equal to 0.097%.  
Figure 7: The achieved EER for each cluster number when using SCAD and SVM [17] on the ASVspoof 2017 version 2.0 benchmark dataset.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **CQCC** & **MFCC** & **LFCC** \\ \hline \multicolumn{4}{c}{SCAD with SVM} \\ \hline Cluster 1 & 0.999963 & 0.000003 & 0.000034 \\ \hline Cluster 2 & 0.999962 & 0.000003 & 0.000035 \\ \hline \multicolumn{4}{c}{SCAD with GMM} \\ \hline Cluster 1 & 0.999963 & 0.0000032 & 0.000033 \\ \hline Cluster 2 & 0.999962 & 0.0000033 & 0.000034 \\ \hline Cluster 3 & 0.999962 & 0.0000033 & 0.000034 \\ \hline Cluster 4 & 0.999963 & 0.0000032 & 0.000034 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Feature weights estimated for each cluster on the ASVspoof 2017 version 2.0 benchmark dataset.
## 3 Results and Discussion
### Volatiles
#### 3.3.2 Minor Volatiles
Octanoic acid was identified in most of the samples, while \(n\)-decanoic acid was only found in fermentations with free cells at 30 \({}^{\circ}\)C.  
Regarding alcohols, characteristic compounds like 2-phenyl-ethanol (with a characteristic rose aroma), and \(\alpha\)-terpineol (with a distinct iliac scent) were found in all wines. Linalool, providing lime tree notes, and citronellol, providing sweet, citrus, and floral scents [65; 66], were also identified in most cases. In addition, 2,3-butanediol (mostly known for its bittersweet taste) was found in some of the samples, but is most likely of low sensory importance for wine [54; 67]. As for miscellaneous compounds, 1,1-diethoxy-ethane, identified in most of the samples, is known for its fruity-green and refreshing scent, and is most likely the only acetal that may contribute to the wine bouquet [54; 58]. HS-SPME GC/MS results were also subjected to PCA, which showed that the fermentation temperature rather than the state of the kefir culture significantly affected the volatile composition, as distinct groups were obvious in the plot (Figure 2). Figure 2: Principal component analysis (PCA) plot of minor volatiles isolated by low alcohol wines fermented by thermally-dried kefir cells. Fr: low alcohol wine fermented by free kefir culture, Ap: low alcohol wine fermented by immobilized kefir culture on apple pieces, DCM: low alcohol wine fermented by immobilized kefir culture on delignified cellulosic material (DCM), GS: low alcohol wine fermented by immobilized kefir culture on grape skins. The fermentation temperature is indicated at the end of the sample code.
# Rician Beamforming: Despeckle Method via Coarray Projection Stochastic Analysis
## 2 Physical and Mathematical Background
### Physical Model and SAFT-Based Coarray Projection
In turn, \(\mathcal{L}\) is the result of concatenation: \(\mathcal{L}:=\{\mathcal{L}_{0}++\mathcal{L}_{1}++\ldots++\mathcal{L}_{2N-2}\}\), where \(\mathcal{L}_{p}\) denotes the group formed by the combination pairs of elements from \(\mathcal{N}\) summing \(p\). Its cardinality is a particularization of the dice rule. In this case,  
\[\text{card}\left(\mathcal{L}_{p}\right)=\Pi[p]*\Pi[p] \tag{3}\]  
where \(\Pi[p]:=1\) if \(0\leq p<N\); otherwise, 0. Considering the indexing imposed by the coarray, the signal corresponding to analysis point \(\mathbf{p}_{a}\) projected over the same domain is estimated as  
\[s(\chi)|_{\mathbf{p}_{a}}=\left[\mathbb{E}\left[s_{\mathcal{L}_{0}}|_{\mathbf{ p}_{a}}\right],\mathbb{E}\left[s_{\mathcal{L}_{1}}|_{\mathbf{p}_{a}}\right], \ldots,\mathbb{E}\left[s_{\mathcal{L}_{2N-2}}|_{\mathbf{p}_{a}}\right]\right] \tag{4}\]  
Based on (2), discrete estimates for the coarray in (4) and previous approximation \(d_{x_{\text{i,j}}\mathbf{p}_{a},x_{\text{j}}}\approx 2R_{a}-u_{a}\chi+\xi_{a}\), the projection of the coarray can be modelled as:  
\[s(\chi)|_{\mathbf{p}_{a}}\approx\sum_{f=1}^{F}\rho_{f}s\left(\alpha_{f}\chi+ \beta_{f}+\gamma_{\text{i,j,f}}\right) \tag{5}\]  
According to Equation (5), the projection onto the coarray domain for a focused point \(\mathbf{p}_{a}\) can be formulated as the summation of warped and shifted versions of \(\bar{s}(t)\). A little jitter effect for emitter-receiver pair positions must also be considered, represented by \(\gamma_{\text{i,j}}\), being the result of the biases introduced by \(\xi_{f}\) and \(\xi_{a}\). Both \(\alpha\) and \(\beta\) depend on the relative positions between \(\mathbf{p}_{f}\) and \(\mathbf{p}_{a}\). These terms tend to 0 if \(\mathbf{p}_{a}\approx\mathbf{p}_{f}\). Estimating statistics leads to a pernicious situation in a double way. On one hand, ULA-based systems do not contribute to diversity as they are supposed to do: when working on a pulse-echo set-up, all signals belonging to \(\mathcal{L}_{p}\) group are closely related, as they represent the same equivalent spatial frequency. On the other hand, acoustic noise due to low \(\alpha_{f}\) component biases the DAS estimator, \(s(\chi)\) average. From Equation (5), one may conclude the following. * High correlation between different \(\alpha_{f}\) and \(\beta_{f}\) suggests reflector clusters presence in tissue structures. * Very low frequency components in \(s(\chi)\) represent high reflectivity areas close to \(\mathbf{p}_{a}\). Actually, only zero-frequency contributor is related to tissue response at that point.
## 4 Discussion
### Main Results and Contributions of the Study  
The research successfully validated the feasibility of serious game-based telerehabilitation for children with disabilities in active conflict zones, as evidenced by 186 active users and a 58% adherence rate despite logistical constraints. Beyond implementation success, qualitative feedback highlighted strong user acceptance, with families and children reporting positive experiences regarding enjoyment, accessibility, and therapeutic value--underscoring the platform's suitability for high-stress environments.  
This aligns with emerging evidence that digital health interventions can bridge service gaps during emergencies, while extending these findings specifically to pediatric neurorehabilitation. First, the study provides empirical proof that low-resource digital rehabilitation tools can remain operational and effective amid instability while addressing real-world barriers to care. Second, it enriches the literature with user-centered perspectives from vulnerable populations, offering critical insights into engagement drivers and perceived efficacy. Lastly, the work advances actionable design and technical guidelines to enhance adaptability and scalability in conflict-affected regions, informing future innovations in equitable healthcare delivery.  
The quantitative results revealed substantial variability in user experience across different game types, which carries important design lessons. Specifically, action-orientedgames featuring clear goals and progression systems--particularly Crazy Racing and Super Surfer--consistently scored highest for both enjoyment (mean ratings 4.1-4.6/5) and perceived therapeutic benefit (3.9-4.2/5). In contrast, more repetitive or precision-demanding games like Paddle Waddle and Up-You Go received markedly lower ratings (3.3-3.5 for enjoyment). This pattern suggests that rehabilitation games for conflict zones should prioritize intuitive, reward-driven mechanics that provide immediate positive feedback, while minimizing complex motor demands that may frustrate unsupervised users. The significant moderate correlation between enjoyment and perceived therapeutic benefit (\(\rho\) = 0.51, \(p\) < 0.01) further reinforces established motor learning principles about the importance of intrinsic motivation for adherence and outcomes.
## 3 Results and Discussion
### Proposition of a Method of Developing Immersive Metaverses for Industrial Training
#### 3.2.1 Game Engine  
The first step of the proposed method is to choose the game engine for developing the immersive metaverse. Game engines are structures or frameworks used in creating digital games [25]. These tools promote software reuse and facilitate the implementation of recurring tasks in the game, allowing developers to focus on content creation. To create immersive experiences that are believable to the human mind, virtual reality requires complex scenes rendered at high frame rates [26].  
With the evolution of the electronic gaming industry, it is possible to develop immersive virtual reality projects with popular game engines such as Unity and Unreal Engine 4; the latter known for its quality of graphic rendering. Both engines are extensively used by professional studios, flexible, free for personal use, and offer a large amount of free online lessons and resources. For this project, Unreal Engine 4 (UE4) was chosen over Unity because it offers two key advantages: better graphics [27; 28; 29] and simpler code. In contrast to Unity, which uses C# scripts through Visual Studio, UE4 also supports C++ coding through the use of blueprints [28]. Moreover, the UE4 open-source code further adds to its appeal, providing users with the freedom to customize and tailor their experiences [27]. In UE4, a blueprint is a visual representation of code that enables input addition, component spawning, and variable configuration without the use of traditional coding. Additionally, compared to Unity's capabilities, UE4's platform navigation, asset usage, and placement are thought to be more beginner-friendly and intuitive.  
In addition to these benefits, the Unreal Engine 4 also boasts a comprehensive network stack specifically designed for facilitating communication between servers and clients, primarily tailored for multiplayer gaming scenarios [27]. This built-in capability enables seamless collaboration among various users, whether they are located in the same physical space or working remotely. Consequently, it empowers the creation of immersive metaverses that foster cooperative interactions and shared experiences for industrial training purposes, as shown in Figure 3.
## 4 Blind Schnorr Signatures--Results and Discussion
### Linking Without the Shared Key \(\mathsf{ak}\)  
Now we assume that \(\mathcal{S}\) does not know any public keys representing \(\mathcal{D}\) and there is no secret \(\mathsf{ak}\) computed as described before and shared by \(\mathcal{S}\) with \(\mathcal{D}\). Nevertheless, we shall see that malicious \(\mathcal{D}\) and \(\mathcal{S}\) can break the blindness property in an undetectable way. A single asymmetric key will be used.  
The first stage of the attack is the same as in Section 4.1.1: as a result, \(\mathcal{S}\) holds \(r_{0}\) associated with \(\mathcal{D}\), while \(\mathcal{D}\) holds \(R_{0}=g^{r_{0}}\).  
Equipped with the key \(R_{0}\), the device \(\mathcal{D}\) can create \(\alpha,\beta\) so that they are in a relation recognizable by a party holding \(r\) and completely random from the point of view of an Auditor \(\mathcal{J}\). An instantiation of such relation is creating an ElGamal ciphertext of \(R\) (from the current interaction) and then mapping both parts of the ciphertext to \(\mathbb{Z}_{q}\), as has been carried out in Section 4.1.2. The rest of the signing protocol is unchanged (see Figure 10).  
ElGamal ciphertexts \((R_{0}^{k}\cdot R,g^{k})\) are not distinguishable from random pairs of group elements, so, for a proper mapping encode, we can conclude that an Auditor \(\mathcal{J}\) will see no difference between the execution from Figure 10 and the original one.  
Now, let us focus on the linking mechanism. When a signature \((c^{\prime},s^{\prime})\) has to be compared with a transcript \((R,c,s)\), the first step is to derive candidate values \(\alpha=s^{\prime}-s\) mod \(q\) and \(\beta=c-c^{\prime}\) mod \(q\). Then, we can apply the decode function to \(\alpha\) and \(\beta\). Every combination of the results can be treated as an ElGamal ciphertext created with the public key \(R_{0}\). It can be decrypted with \(r_{0}\) and tested whether the result is \(R\). For details, see Figure 11.  
Figure 9: Unblinding the message signed during blind-signature creation.  
Comment  
The above construction can be modified slightly to enable \(\mathcal{D}\) to insert a covert message for \(\mathcal{S}\). Namely, instead of encrypting \(R\), one can encrypt a bit string \(M\) representing an element of the group, where  
\[M=\mathsf{H}(R)_{\{\ell-t+1,\ldots,\ell\}}\|\mathsf{m}^{\prime}\]  
(\(\ell\) is the bit length of the output of the \(\mathsf{H}\) function.)  
Now, the test procedure will not check equality with \(R\) but with the last \(t\) bits of \(\mathsf{H}(R)\). If \(t\) is big enough (say, \(t=40\)), then the false-positive probability is practically negligible, while there are plenty of bit positions left for the message \(\mathsf{m}^{\prime}\).
# Exploring the Bioethanol Production Potential of _Miscanthus_ cultivars
## 3 Results and Discussion
### Comparison of Lignin Content Using the Klason Method or ABSL Method
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Sample** & **Total (Klason)** & **Total (ABSL)** & **Cellulose** & **Hemicellulose** & **Lignin (Klason)** & **Lignin (ABSL)** \\ \hline BE01 & 98.55 & 97.77 & 41.27 \(\pm\) 1.39 & 28.70 \(\pm\) 2.90 & 28.58 \(\pm\) 0.73 & 27.78 \(\pm\) 0.90 \\ BE04 & 104.82 & 99.08 & 48.09 \(\pm\) 0.26 & 28.53 \(\pm\) 0.25 & 28.20 \(\pm\) 1.94 & 22.46 \(\pm\) 0.97 \\ BE07 & N/A & N/A & 45.64 \(\pm\) 0.03 & 27.76 \(\pm\) 0.55 & N/A & N/A \\ BE10 & 102.08 & 100.29 & 45.24 \(\pm\) 0.25 & 30.58 \(\pm\) 0.40 & 26.26 \(\pm\) 0.28 & 24.47 \(\pm\) 1.07 \\ BE13 & 99.08 & 97.24 & 43.00 \(\pm\) 0.37 & 30.32 \(\pm\) 0.39 & 25.76 \(\pm\) 0.98 & 23.92 \(\pm\) 0.33 \\ BE16 & 96.52 & 95.14 & 46.02 \(\pm\) 0.95 & 24.27 \(\pm\) 1.01 & 26.23 \(\pm\) 0.16 & 24.85 \(\pm\) 1.89 \\ BE19 & 100.97 & 95.19 & 41.15 \(\pm\) 0.27 & 32.62 \(\pm\) 0.33 & 27.20 \(\pm\) 1.98 & 21.42 \(\pm\) 1.23 \\ BE22 & 102.4 & 98.65 & 41.46 \(\pm\) 0.83 & 32.28 \(\pm\) 0.81 & 28.66 \(\pm\) 0.42 & 24.91 \(\pm\) 0.87 \\ BE25 & 97.44 & 94.04 & 41.65 \(\pm\) 1.04 & 30.25 \(\pm\) 0.62 & 25.54 \(\pm\) 0.74 & 22.14 \(\pm\) 0.75 \\ \hline \hline \end{tabular} N/A: no valid result was obtained.  
\end{table} 
Table 2: Cellulose, hemicellulose and lignin content (%, \(w\)/\(w\)) of _Miscantthus_ cultivars.
## 3 Results
### Disaster Management  
Drones are being used in disaster management prior to or post events. The geos-surveying images collected by drones, combined with other primary and secondary data can be input to GIS software (i.e., ArcGIS, QGIS, AutoCAD Map 3D) to digitally map the landslide prone areas [88]. Post landslide events, drones can be used to investigate the damage and impact areas. For example, [PERSON] et al. [81] conducted morphometric measurements in a landslide event using orthoimages captured by drones. The collected data was then analysed in ArcGIS to digitise and estimate the morphometric parameters, including the area and volume of the landslides. In many cases, drones were proven to be effective in disaster victim identification process [83; 117]. For example, drone-based geospatial surveillance presented advantages in guiding rescue operations in darkness caused by power cuts during natural disaster events [84]. Also, through the integration of drone laser scanning and mobile laser scanning, drones are instrumental in quantifying post-fire tree structures with high precision [87]. This advanced technique facilitates the development of site-specific forest management plans, especially in controlling burn severities by managing crown fuel abundance and configuration.
## 2 Materials and Methods
### Comparison of Positioning Deviations according to the Scenario Phase  
The whole dataset was also compared in terms of its current scenario phase. Four phases of the scenario were identified: static (\"STAT\")--when the trolley did not move, acceleration (\"ACC\")--when the trolley arm was gradually accelerated from a zero angular speed to the required angular speed, uniform movement (\"UNIF\")--when the trolley arm performed a uniform movement at the required angular speed and deceleration (\"DEC\")--when the trolley arm was gradually braked to a zero angular speed. Individual samples of the dynamical measurements were divided per value of instantaneous angular acceleration between three ranges: for \"ACC\" in the interval (\(-\infty\); \(-0.2>\) m s\({}^{-2}\), for \"UNIF\" in the interval (\(-0.2\); \(0.4>\) m s\({}^{-2}\) and for \"DEC\" in the interval (\(0.4\); so) m s\({}^{-2}\). For the \"STAT\" scenario phase, a separate set identical to that in section Evaluation (4) was used. As in the preceding case of Evaluation (7), this evaluation was performed based on the median values from the datasets and presented in Boxplot. These evaluations were also performed separately for the range of deviations for the whole measurement period (\"FIX\" + \"FLOAT\" + \"GNSS\") and separately for the subset of the samples from the period \"FIX\".