esa-sceva/satcom-chunk-collection huggingface dataset:
# Re-Visiting Acoustic Sounding to Advance the Measurement of Optical Turbulence
## 2 Materials and Methods
### Sonic anemometry and Optical Turbulence Point Measurement
As introduced earlier, a modified form of the ideal gas law can be written as \(\rho=P/R_{d}T_{v}\) to account for moisture, which results in the following:  
\[\
abla n_{z_{1}\to z_{2}}\approx a\frac{\partial\rho}{\partial z}\approx \frac{a}{R_{d}\overline{T_{v}}}\bigg{[}\frac{-\overline{P}}{\overline{T_{v}}} \bigg{(}\frac{\partial T_{v}}{\partial z}\bigg{)}+\bigg{(}\frac{\partial P}{ \partial z}\bigg{)}\bigg{]}, \tag{15}\]  
Recalling that \(\partial P/\partial z=-\rho g\), where \(g\) is the acceleration due to gravity, one can re-write Equation (15) as follows:  
\[\
abla n_{z_{1}\to z_{2}}\approx\frac{-a\overline{P}}{R_{d}\overline{T_{v}}} \bigg{[}\frac{\partial T_{v}}{\partial z}+\frac{g}{R_{d}}\bigg{]}\approx \frac{-a\overline{\rho}}{\overline{T_{v}}}\bigg{[}\frac{\partial T_{v}}{ \partial z}+\frac{g}{R_{d}}\bigg{]}\approx\frac{-(n-1)}{\overline{T_{v}}} \bigg{[}\frac{\partial T_{v}}{\partial z}+\frac{g}{R_{d}}\bigg{]}, \tag{16}\]  
As developed earlier in this paper, \(T_{v}\) is effectively the sonic temperature recorded using the sonic anemometer. It is not a conserved quantity as is the potential virtual temperature. In order to discern the dynamic range of the fluctuations in the index of refraction gradient, as is implied in the derivation associated with Equation (5), converting to a potential virtual temperature is warranted, but not necessary if one removes the large-scale virtual temperature lapse rate with height. However, to ensure consistency and preserve the underlying nature of the causal source of the gradients (i.e., turbulent mixing), the gradient of the index of refraction can be separated into a large-scale index component--an ensemble average--and an eddy component that represents the small-scale turbulent fluctuations as follows:  
\[\
abla n=\
abla n+\
abla n^{\prime} \tag{17}\]  
By doing so, the dominant large-scale index of refraction gradients, otherwise concealing high frequency (small-scale) fluctuations in the index of refraction gradient that determine \(C_{n}^{2}\), can be removed to arrive at the following:  
\[\
abla n^{\prime}_{z_{1}\to z_{2}}\approx\bigg{\{}\frac{-(n-1)}{\overline{T_{v }}}\bigg{[}\frac{\partial T_{v}}{\partial z}+\frac{g}{R_{d}}\bigg{]}\bigg{\}}- \bigg{\{}\frac{-(n-1)}{\overline{T_{v}}}\bigg{[}\frac{\partial T_{v}}{\partial z }+\frac{g}{R_{d}}\bigg{]}\bigg{\}} \tag{18}\]  
By assuming the mean layer virtual temperature at any one time, \(\overline{T_{v}}\) is equivalent to the ensemble average mean layer virtual temperature, \(\langle\overline{T_{v}}\rangle\), Equation (18) can be simplified to the following:  
\[\
abla n^{\prime}{}_{z_{1}\to z_{2}}\approx\frac{-(n-1)}{\langle\overline{T_{ v}}\rangle}\bigg{[}\frac{\partial T_{v}-\partial\langle T_{v}\rangle}{ \partial z}\bigg{]} \tag{19}\]  
The ensemble average of the right side of Equation (19) provides \(\
abla\langle n^{\prime}\rangle\), which is then combined with the other terms in Equation (5) to arrive at a \(C_{n}^{2}\) profile.
# Optimizing CNN-LSTM for the Localization of False Data Injection Attacks in Power Systems
## 4 Simulation Experiments
### Comparative Experiments and Analysis  
#### 4.4.1 Robustness Comparison  
To assess the robustness of the proposed method, the test systems were compared under three conditions: no noise and 5% and 10% standard deviation Gaussian noise. The test results are presented in Table 3. It is evident that the model demonstrated satisfactory detection performance across all three scenarios. Specifically, for the IEEE14-bus test system, the F1 scores were 99.73%, 99.71%, and 99.70% under the respective conditions. Similarly, for the IEEE118-bus test system, the F1 scores were 99.15%, 99.10%, and 99.08% under the three conditions.  
Additionally, we tested the model under different attack intensities to evaluate its performance under various levels of attack strength. As per Section 2.2, it was assumed that an attacker constructed an attack vector \(\text{a}=\left(\text{a}_{1},\text{a}_{2},\dots,\text{a}_{\text{m}}\right)^{ \text{T}}\). We introduced the L2-norm as a measure of the intensity of the attack. The L2-norm of the attack vector can be expressed as:  
\[\|\text{a}\|_{2}=\sqrt{\text{a}_{1}^{2}+\text{a}_{2}^{2}+\dots+\text{a}_{m}^{2}} \tag{13}\]  
The larger the L2-norm of the attack vector, the greater the intensity of the attack. The specific detection effects are shown in Figure 10. It can be seen that the proposed detection method could maintain an effective localization ability under different attack intensities. Moreover, as the attack intensity increased, the localization tended to improve. This was because when the attack strength increased significantly, normal data and attacked data became easier to distinguish.
## 2 Background and Related Works
### Background
#### 2.2.6 Baseline LM: Pretrained and Instruction-Tuned Generative Language Model  
Pretrained large language models (LLMs) are artificial intelligence models that have undergone an initial training phase, called pretraining, on massive and diverse text datasets before being adapted for specific tasks. During pretraining, the model learns general language patterns, grammar, semantics, and even some world knowledge by predicting missing or next words in sentences, often using self-supervised learning techniques. The broad language understanding acquired during pretraining enables effective fine-tuning for specialized domains or tasks.  
FLAN [24] demonstrates that instruction-tuning a pretrained language model on a diverse set of NLP tasks using natural language instructions significantly enhances zero-shot learning abilities. Instead of only learning from raw text, the model is explicitly taught to follow instructions phrased in natural language, which improves its ability to generalize to unseen tasks. Not to mention large-scale LLMs like GPT [25], Gemini [26], Claude [27], etc., modern small-scale instruction-tuned generative LMs like Llama-3.2-3B-Instruct, Qwen2.5-3B-Instruct, Gemma-3-4b-it, and others affected by FLAN's instruction-tuning approach are fine-tuned by instruction-tuning and aligned with human preferences and world knowledge on vast amount of diverse datasets of instruction-response pairs, enabling them to capture patterns and relationships between words, and to understand and execute a wide range of user instructions, even for tasks not explicitly seen during training. Thus, even small-scale instruction-tuned LMs can be easily adapted to new domains or any text-to-text generative tasks like 'Text2 Cypher', without extensive retraining or architectural changes. They have demonstrated strong capabilities in language understanding and generation, semantic parsing, and few-shot or zero-shot generalization [11; 12].  
However, SQL and Cypher have their own syntax patterns and semantics that are different from natural languages. Thus, since even large-scale LLMs have been trained mostly with natural language text data, they have limitations in bridging semantic gaps between natural sentences and query sentences. Thus, further fine-tuning the instruction-tuned LLMs with the appropriate datasets has been suggested and reported to be effective in improving performance [6]. Nonetheless, such a simple further fine-tuning with datasets cannot fully handle such various variants of the semantic gap.  
This paper addresses this challenge. We let baseline SLM be refined by reinforcement learning with GRPO, utilizing the semantic information of key-value and triple relationships extracted during the training process. These techniques enhance the models' ability to generate accurate and executable Cypher queries, particularly in scenarios with limited training data for specific schemas.
# Dynamic Response Analysis of Structures Using Legendre-Galerkin Matrix Method
## 2 Basics for SDOF and MDOF Systems
### Linear Newmark-\(\beta\) Method  
One of the most efficient methods of handling equations of motion of SDOF and MDOF systems has been proposed by [PERSON] [2]. In 1959, [PERSON] also presented a set of step-by-step numerical equations that are entirely known as the Newmark-\(\beta\) method [5]. In the present study, the linear Newmark-\(\beta\) method is taken into account from [5] to verify the results of the newly developed LGM method.  
Figure 1: SDOF structure subjected to dynamic load P(t).  
In order to solve the equation of motion of a MDOF system using the linear Newmark-\(\beta\) method, it is important to remind that the following steps should be generally followed:  
1. Determination of the initial conditions, \(\{u_{0}\}\), \(\{\dot{u}_{0}\}\) and \(\{\ddot{u}_{0}\}\), where the value of \(\{\ddot{u}_{0}\}\) is given by: \[\ddot{u}_{0}=\frac{p_{0}-Ci_{0}-Ku_{0}}{M}\] (6)
2. Determination of \(\Delta t\) (assumed time step, e.g., 0.005)
3. Determination of the effective stiffness matrix: \[\dot{k}=k+\frac{\gamma}{\beta\Delta t}C+\frac{1}{\beta\left(\Delta t\right)^{2 }}M\] (7) where \(\beta=1/6\) and \(\gamma=1/2\) for linear acceleration method.
4. Calculation of a and b factors: \[\left\{\begin{array}{l}a=\frac{1}{\beta\Delta t}M+\frac{\gamma}{\beta}C\\ b=\frac{1}{2\beta}M+\Delta t\Big{(}\frac{\gamma}{2\beta}-1\Big{)}C\end{array}\right.\] (8)
5. Calculation of the effective load: \[\Delta\dot{p}_{i}=\Delta p_{i}+a\dot{u}_{i}+b\ddot{u}_{i}\] (9)
6. Calculation of displacement, velocity, and acceleration: \[\left\{\begin{array}{l}u_{i+1}=u_{i}+\Delta u_{i},\\ \dot{u}_{i+1}=\dot{u}_{i}+\Delta u_{i},\\ \ddot{u}_{i+1}=\ddot{u}_{i}+\Delta\ddot{u}_{i},\end{array}\right.\] (10) where, \[\Delta u_{i}=\frac{\Delta\dot{p}_{i}}{\dot{K}}\] (11) \[\Delta\ddot{u}_{i}=\frac{\gamma}{\beta\Delta t}\Delta u_{i}-\frac{\gamma}{ \beta}\dot{u}_{i}+\Delta t\bigg{(}1-\frac{\gamma}{2\beta}\bigg{)}\ddot{u}_{i}\] (12) \[\Delta\ddot{u}_{i}=\frac{\gamma}{\beta\left(\Delta t\right)^{2}}\Delta u_{i }-\frac{1}{\beta\Delta t}\dot{u}_{i}-\frac{1}{2\beta}\ddot{u}_{i}.\] (13)
# Unveiling LoRa's Oceanic Reach: Assessing the Coverage of the Azores LoRaWAN Network from an Island
## 3 Materials and Methods
### Experiment  
For this experiment, the end device was installed on a fishing vessel, and the location was tracked based on GPS (Global Positioning System) location transmitted at five-minute intervals (when in motion). Considering regular vessel speeds around 5 knots, it takes a vessel 5 min to move by a distance of 750 m. Since vessels tend to follow relatively straight lines, occasional missing packets do not imply a significant difference between a logic spatial inference and the actual route, rendering the output adequate for mapping aggregated activity zones across long periods. The gateway receives the location data from the end device as a LoRa message and forwards it to the network server. As it arrives at the network server, it is pushed to a REST Webhook developed to handle the LoRa messages from the end device along with the battery voltage, RSSI and SNR network-level metrics. The tracking device (hardware) did not support logging the packets sent; therefore, tracking packet loss was impossible at this stage.  
Figure 3: Location of the Serra de Santa Barbara gateway.  
The process of handling payloads from the GPS tracking device involves several steps. The device sends a message containing encoded location information to a gateway (via LoRaWAN), which then forwards it to a network server (via the internet). Upon receipt, the server pushes the news and received signal strength indicator (RSSI) and signal-to-noise ratio (SNR) metrics to a webhook. The Webhook was responsible for the decoding of the LoRa payload containing the location and other data and for creating new entries in a table of a database with the following columns: timestamp; device latitude; device longitude; gateway id; RSSI; SNR and battery voltage. Gateway locations are stored in another table in the database and contain each gateway's id, latitude, longitude, and elevation. The webhook was built using Node and Express.js, and the payload decoding was processed using a JS function provided by the device manufacturer. It accepts POST requests to the uplink endpoint, '/up'. To ensure secure data transmission, the connection to the webhook is established via SSL (Secure Sockets Layer). Additionally, only incoming requests with a valid API key are accepted, ensuring that only authorized parties can access the data and preventing unauthorized access or tampering. The decoded location information and signal metrics are then stored in an online MySQL database for easy access. The payload handling process described above ensures secure and efficient data transmission from the LoRaWAN device to the network server and database.
## 4 Conclusions  
In summary, we successfully synthesized Ni\({}_{3}\)Se\({}_{4}\)-MoSe\({}_{2}\) composites by directly growing MoSe\({}_{2}\) on Ni\({}_{3}\)Se\({}_{4}\) nanosheets for the first time. The Ni\({}_{3}\)Se\({}_{4}\) nanosheets served as templates to support MoSe\({}_{2}\) and were expected to prevent MoSe\({}_{2}\) from aggregation and improve its conductivity. XRD, XPS, FE-SEM, and TEM were used to characterize the morphology and structure of the samples. The results revealed that MoSe\({}_{2}\) was chemically bonded on the surface of Ni\({}_{3}\)Se\({}_{4}\). Further, electrochemical measurements of the composites verified that the HER performance was improved compared to pristine MoSe\({}_{2}\) nanosheets, whereas the Ni3 Se4 nanosheets were not catalytically active to the HER but could reduce the charge transfer resistance and facilitate electron transfer between MoSe\({}_{2}\) and the electrolyte. The Ni\({}_{3}\)Se\({}_{4}\)-MoSe\({}_{2}\) composites with a ratio of 1:2 performed the best, with a small overpotential of 145 mV and a low Tafel slope of 65 mV/dec. Continued increase in the amount of Ni\({}_{3}\)Se\({}_{4}\) led to inferior HER performance. These results suggest that the HER activity of MoSe\({}_{2}\) nanosheets can be enhanced by constructing composites with Ni\({}_{3}\)Se\({}_{4}\) in appropriate ratios.  
**Supplementary Materials:** The following are available online at [[http://www.mdpi.com/2076-3417/9/23/5035/s1](http://www.mdpi.com/2076-3417/9/23/5035/s1)]([http://www.mdpi.com/2076-3417/9/23/5035/s1](http://www.mdpi.com/2076-3417/9/23/5035/s1)): Figure S1: EDX spectra of Ni\({}_{3}\)Se\({}_{4}\)@MoSe\({}_{2}\); Figure S2: (a), (b), (c) SEM images of Ni\({}_{3}\)Se\({}_{4}\)@MoSe\({}_{2}\) with different Ni\({}_{3}\)Se\({}_{4}\):MoSe\({}_{2}\) ratios of 1:2, 1:1, and 2:1, respectively.  
**Author Contributions:** [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments; [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] helped for data measuring and analysis; [PERSON] and [PERSON] wrote the paper; Both the authors discussed the results and commented on the manuscript. [PERSON], [PERSON] and [PERSON] are the corresponding author for the whole work.  
**Funding:** This work was supported in part by the Basic Research Laboratory of the NRF funded by the Korean Government [grant number 2018R1 AA1022647] and in part by the Chung-Ang University Research Scholarship Grants in 2018.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Preliminary Propulsion and Power System Design of a Tandem-Wing Long-Range eVTOL Aircraft
## 1 Introduction
### Background  
This manuscript elaborates on the propulsion and power system design methodology devised in the Wigeon project as a means of the preliminary design of open rotor eVTOL configurations, which are characterised by the use of innovative distributed propulsion architectures for their propulsion and power systems. Procedures to design efficient propellers are already available in the literature [5; 6] and can be applied to eVTOL aircraft; however, the use of these unconventional distributed propulsion configurations, required in order to ensure safety, controllability, and performance during vertical and horizontal flight and the transition between them also results in more difficult performance analysis due to the close coupling between the propulsive and aerodynamic systems, even though analyses of these open rotor distributed propulsion configurations applied to VTOL [7; 8] and UAV [9] vehicles are already available in the literature.  
The wide range of operating conditions of eVTOL aircraft also presents a challenge for the choice of an optimal design which encompasses all of the mission conditions. Moreover, the power system of eVTOL aircraft usually features fully electric energy sources, which are not commonly used in commercial aircraft and provide a technological challenge due to their lower current performance when compared to conventional combustion aircraft. This study aimed to tackle these challenges with a simple design methodology for the early performance estimation of such systems, by applying the available methods and studies into a combined design framework for the propulsion and power systems of open rotor battery-powered eVTOL aircraft.  
Figure 1: Rendering of the final design of the analysed configuration of the Wigeon.
## 5 Conclusions  
This study proposes a hand-eye separation-based robot initial frame localization and follower tracking method to address the issues of image quality degradation, limited computational resources, and complex environmental interference in tunnel drilling operations. First, by improving the RFBNet model, ShuffleNet V2 is used as a lightweight alternative to VGG16 for the backbone network. This, combined with channel splitting and depthwise separable convolutions, reduces the model's parameters by 15 MB and boosts inference speed to 31 FPS, significantly adapting to the resource constraints of edge devices. Additionally, the GIoU loss function is introduced to optimize bounding box regression. By calculating the minimum enclosing box, it resolves the gradient vanishing issue in traditional IoU, improving average precision (AP) by 3.3%. For continuous tracking, the SiamRPN algorithm is combined with Kalman filtering for prediction, along with a target detection-based re-localization strategy. This effectively addresses occlusions and target loss, improving the success rate and accuracy by 1.6% and 1.7%, respectively. Furthermore, by integrating a PID controller with dynamic compensation terms, tracking jitter caused by gravity and elastic deformation in the flexible robotic arm is suppressed, further enhancing system stability. Experimental results demonstrate that the proposed method shows excellent localization accuracy and tracking robustness in simulated tunnel environments. The improved initial frame localization model achieves an average precision of 0.94 in complex scenarios such as occlusions, rapid motion, and background interference. The tracking algorithm achieves a success rate and accuracy of 0.639 and 0.861, respectively, on the OTB-2015 dataset, significantly outperforming traditional SiamRPN and other classic algorithms. This research provides an efficient, low-energy, and deployable solution for intelligent drilling operations in tunnel construction, addressing the challenges posed by more extreme industrial environments.  
Conceptualization, H.Z., J.G. and B.Z.; methodology, J.G., H.Z. and C.X.; validation, C.X.; investigation, J.G. and H.Z.; data curation, H.Z. and C.X.; writing--original draft preparation, H.Z.; writing--review and editing, J.G.; supervision, B.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The OTB-2015 datasets can be obtained from [[http://www.cs.toronto.edu/~dross/ivt/](http://www.cs.toronto.edu/~dross/ivt/)]([http://www.cs.toronto.edu/~dross/ivt/](http://www.cs.toronto.edu/~dross/ivt/)).  
Figure 12: Comparison of tracking results of the proposed method with the baseline algorithm and the classical algorithm.  
Conflicts of Interest: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
## 3 Theory
### Partial Least Squares  
The partial least squares (PLS) method is considered as a good alternative to principal component regression (PCR). The PLS method is a more robust approach when the data are analyzed by a regression for the independent variables \(X\) and the dependent variables \(Y\). The PLS is built with a set of outer relations that describe \(X\) or \(Y\) as linear combinations of components, as presented in Equation (2) [22]. The quality of a PLS model can be evaluated by the coefficient of determination, \(R^{2}\), which provides information about the variation in the used data sets. One variable is assumed to be the explanatory variable, and the other variable is assumed to be a response variable. The coefficient of determination represents the quality of the data adjustment within a straight line, and the value is 1 when there is total agreement between the real and estimated values [23; 24].  
\[X=TP^{\prime}+E=\sum_{h=1}^{H}t_{h}p_{h}^{\prime}+E, \tag{3}\]  
\[Y=UQ^{\prime}+F=\sum_{h=1}^{H}u_{h}q_{h}^{\prime}+F \tag{4}\]  
where \(T\) and \(U\) are the score matrices, \(P^{\prime}\) and \(Q^{\prime}\) are the loading matrices of \(X\) and \(Y\), respectively, and \(E\) and \(F\) are the residuals of \(X\) and \(Y\).
###### Abstract  
We present ground-based in situ snow measurements in Kiruna, Sweden, using the ground-based in situ instrument Dual Ice Crystal Imager (D-ICI). D-ICI records dual high-resolution images from above and from the side of falling natural snow crystals and other hydrometeors with particle sizes ranging from 50 \(\upmu\)m to 4 mm. The images are from multiple snowfall seasons during the winters of 2014/2015 to 2018/2019, which span from the beginning of November to the middle of May. From our images, the microphysical properties of individual particles, such as particle size, cross-sectional area, area ratio, aspect ratio, and shape, can be determined. We present an updated classification scheme, which comprises a total of 135 unique shapes, including 34 new snow crystal shapes. This is useful for other studies that are using previous shape classification schemes, in particular the widely used Magono-Lee classification. To facilitate the study of the shape dependence of the microphysical properties, we further sort these individual particle shapes into 15 different shape groups. Relationships between the microphysical properties are determined for each of these shape groups.  
natural snow crystals; hydrometeors; classification; shape; microphysical properties +
Footnote †: journal: Applied Physics Letters