esa-sceva/satcom-chunk-collection huggingface dataset:
# Slotted ALOHA on LoRaWAN-Design, Analysis, and Deployment
## Appendix A  
The duration of uplink and downlink transmissions depends on parameters of LoRa modulation such as SF (Spreading Factor), BW (Bandwidth), CR (Coding Rate, values from 1 to 4) and can be expressed as the sum of the time needed to transmit the preamble and the physical message (Equation (A1)):  
\[T_{\text{tx}}=T_{\text{preamble}}+T_{\text{PHY Message}}\] (A1)  
Equations (A2) and (A3) represent how these two terms have been calculated, where \(N_{\text{preamble}}\) is the number of symbols used by the radio transceiver as the physical preamble of the message and \(N_{\text{PHY}}\) indicates the number of symbols transmitted in the physical message and can be determined as shown in Equation (5). \(T_{\text{sym}}\) (Equation (A4)) is the duration (in seconds) of a symbol and depends on SF and BW:  
\[T_{\text{preamble}}=T_{\text{sym}}\cdot\left(N_{\text{preamble}}+4.25\right)\] (A2)  
\[T_{\text{PHY Message}}=T_{\text{sym}}\cdot N_{\text{PHY}}\] (A3)  
\[T_{\text{sym}}=\frac{2^{\text{SF}}}{\text{BW}}\] (A4)  
\[N_{\text{PHY}}=8+\max\left[ceil\left[\frac{28+8\cdot\text{PL}+16\cdot\text{CRC }-4\cdot\text{SF}}{4\cdot(\text{SF}-2\cdot\text{DE})}\right]\cdot(\text{CR}+4 ),\ 0\right]\] (A5)  
In Equation (A5) Payload Length (PL) denotes the number of bytes in the physical payload, CRC indicates the presence (value 1) or not (value 0) of the CRC field in the physical message and DE indicates if the mechanism to prevent issues about the clock drift of the crystal reference oscillator is used (value 1 for SF12 and SF11, 0 for others). The transmission data rate can be obtained as shown in Equation (A6):  
\[\text{DR}=\text{SF}\cdot\frac{\text{BW}}{2^{\text{SF}}}\cdot\frac{4}{\text{ CR}+4}\] (A6)
# Edible Packaging: A Technological Update for the Sustainable Future of the Food Industry
## 3 Results
### Chitosan Films  
Chitosan is a linear polysaccharide composed of \(\beta\)-linked N-acetyl-glucosamine and N-glucosamine units. It is derived from alkaline N-deacetylation of chitin, which is the second most prominent natural polysaccharide after cellulose [42]. The main sources of chitin are the exoskeletons of crustaceans and several insects and fungi. Chitosan is biodegradable, non-toxic, and biocompatible [37]. chitin is insoluble in water or other common solvents, but by applying high alkaline and warm conditions, this chitin can convert into chitosan. After this treatment, produced chitosan becomes soluble in acidic mediums such as diluted hydrochloric, formic, and acetic acids [26; 32].  
Several studies have reported chitosan use in food packaging due of its physicochemical properties. Chitosan has good mechanical and film-forming properties and selective permeability to O\({}_{2}\) and CO\({}_{2}\)[42]; it also possesses antimicrobial and antioxidant properties [43]. So, the chitosan-based film and coatings can be used in products like apples, pears, strawberries, cucumbers, bell peppers, peaches, and plums. However, chitosan films have some limitations for use in food products. Chitosan is hydrophilic; thus, it shows poor moisture barrier properties. Several strategies can be applied to enhance the functional qualities of chitosan, like modifying solvent type, pH, adding plasticizers [44], and incorporating lipid components [9]. Another main challenge to chitosan-based films and coatings is that they are not thermoplastic, which results in degradation before reaching the melting point. Therefore, chitosan is difficult to extrude, and the films cannot be heat-sealed, which limits their production at the commercial level [45].
## 3 Proposed Framework
### Sensing Layer  
This layer collects data for the higher layers through various IoT infrastructures, such as wireless network sensors, radio identifiers, and so on. These infrastructures can be set up on streets and intersections, in addition to using devices embedded in vehicles and in the smartphones of drivers and passengers. As an example, social networking applications that are nowadays embedded in the dash panels of smart cars and are being used in many developed countries can be used as a source of data.
## 2 Experimental Methods  
There is a varied range of proposed experimental methods to estimate shear-wave velocity. This study uses some of methods to calculate shear wave velocity, \(\mathrm{V_{S}}\), by employing \(\mathrm{V_{P}}\) and lithology. For instance, [PERSON] et al. [8] introduced an equation to estimate \(\mathrm{V_{S}}\) as follows:  
\[\mathrm{V_{S}}=0.86\mathrm{V_{P}}-1170 \tag{1}\]  
In addition, [PERSON] and [PERSON] introduced one of the most popular methods to estimate \(\mathrm{V_{S}}\), by considering \(\mathrm{V_{P}}\) and lithology [6] (Equation (2)):  
\[\mathrm{V_{S}}=\frac{1}{2}\left\{\left[\sum_{i=1}^{\ell}\mathrm{X_{i}}\left( \sum_{j=0}^{N_{i}}\mathrm{a_{\bar{i}}}\mathrm{V_{P}^{j}}\right)\right]+\left[ \sum_{i=1}^{\ell}\mathrm{X_{i}}\left(\sum_{j=0}^{N_{i}}\mathrm{a_{\bar{i}}} \mathrm{V_{P}^{j}}\right)^{-1}\right]^{-1}\right\} \tag{2}\]  
where \(\ell\) refers to the number of pure components in terms of lithology; \(\mathrm{X_{i}}\) presents volume proportion of lithological constituents; \(\mathrm{a_{\bar{i}}}\) is empirical regression coefficients relying upon lithology (Table 1); \(\mathrm{N_{i}}\) refers to the order of polynomial for constituent \(\mathrm{I}\); \(\mathrm{V_{P}^{j}}\) is the water-saturated P-wave velocity in the j rock facies. Finally, \(\mathrm{V_{S}}\) is S-wave velocities (km/s) in composite multi-mineral brine-saturated rock.  
[PERSON] et al. [8] provided three other equations based on \(\mathrm{V_{P}}\) and lithology (Equations (3)-(5)):  
\[\mathrm{V_{S}}=0.5508\mathrm{V_{P}^{2}}+1.0116\mathrm{V_{P}}-1.0305 \tag{3}\]  
\[\mathrm{V_{S}}=0.5832\mathrm{V_{P}}-0.0777 \tag{4}\]  
\[\mathrm{V_{S}}=0.77\mathrm{V_{P}}-0.8674 \tag{5}\]  
These equations are used for a specific purpose. For instance, Equation (3) is used for limestone, and Equations (4) and (5) are used for dolomite and shale, respectively. Furthermore, [PERSON] et al. [7] introduced a model based on \(\mathrm{V_{P}}\) to calculate the shear wave velocity for each formation (Equation (6)):  
\[\mathrm{V_{S}}=0.7858-1.2344\mathrm{V_{P}^{2}}-0.1238\mathrm{V_{P}^{3}}+0.000 6\mathrm{V_{P}^{4}} \tag{6}\]
# Recent Progress in Fast Distributed Brillouin Optical Fiber Sensing
## 4 Developments of the Fast BOTDA Schemes
### Slope-Assisted Fast BOTDA
#### 4.3.1 Increasing the Slope Numbers  
To make full use of both slopes of the BGS, a coefficient \(R_{B}\)[41] is defined as the ratio of the two Brillouin gains corresponding to the two tones (i.e., \(N_{f}=2\)), which is shown in Figure 9a. It should be noted that the profile of \(R_{B}\) has a wide monotonous slope and its insensitivity of the power fluctuation for the pump pulse. The BFS of the FUT can be demodulated based on the value of \(R_{B}\). Furthermore, a multi-slope-assisted BOTDA is implemented using the OFA-probe wave, which is shown in Figure 9b. A special OFA-probe wave, which has a large frequency interval, is generated and amplified by obeying the BGS profile from which two neighbor and the maximum Brillouin signals are chosen to calculate the value of \(R_{B}\) and the BFS. In this scheme, a wide dynamic range is obtained by adding the tones at the expense of the sampling rate.  
In the experiment, a dynamic strain with a vibration frequency of \(\sim\)12.8 Hz is acquired and demodulated at the sampling rate of 1 kHz. The maximum strain variation of 5000 \(\mu\varepsilon\) corresponding to the BFS change of 241 MHz is realized with the frequency intervals ranging from 20 MHz to 80 MHz (see Figure 10a-d). It should be noted that the maximum sampling rate in theory of this scheme can reach 10 times higher than that of the typical OFA-based BOTDA thanks to its large frequency interval.  
Figure 8: The sketch diagram of the SA-BOTDA [39]. The intensity of the Brillouin signal changes as the BFS changes.  
Figure 9: The frequency relationship [42] for (**a**) a double-slope-assisted scheme and (**b**) a multi-slope-assisted scheme.
# Hybrid Methodology Using Sentinel-1/Sentinel-2 for Soil Moisture Estimation
## 2 Materials and Methods
### Database
#### 2.1.2 Sentinel-1  
The first S-1A satellite was launched on 3 April 2014 and was followed by the S-1B Sentinel satellite on 25 April 2016. This dual-satellite constellation offers a 6-day repeat frequency for all regions of the globe [74]. The SAR payloads use a C-band frequency of 5.4 GHz and have the following standard operating modes: stripe map (SM), interferometric wide swath (IW), extra wide swath (EW), and WaVe (WV). In the present study, IW S-1 images are analyzed. They are characterized by a 10 m \(\times\) 10 m spatial resolution and dual VV and VH polarization measurements. All of the images were generated from the high-resolution, Level-1 Ground Range Detected (GRD) product. The calibration is designed to convert the digital values of the raw images into backscattering coefficients (\(\sigma\)0).
## 3 Results
### Real-World Applications and the Impact of JAVIS in Hospital Workflows
#### 3.8.1 Improving Clinical Documentation and Decision Support  
Efficient documentation and structuring of patient medical records are essential in clinical workflows, particularly in emergency room (ER) settings. JAVIS was deployed in ER discharge processes, where it facilitated the summarization of patient medical records, reducing the time required for manual documentation. Physicians reported that these AI-generated discharge summaries improved workflow efficiency, allowing them to focus more on patient care rather than administrative tasks.  
In addition, due to hospital networks operating in isolated environments without Internet access, physicians reported struggling with medical knowledge retrieval, such as web searching for specific disease symptoms. JAVIS has been used to retrieve medical knowledge related to specific diseases and symptoms. While this was not used for diagnostic purposes, physicians reported that it was valuable for refreshing previously learned information, such as disease symptoms or general medical knowledge, i.e., using it as an internal network browser for non-critical situations.  
Furthermore, the hospital's electronic medical records (EMR) development team leveraged JAVIS-hosted LLaMA 3.1 to reformat and structure unstructured EMR data, significantly improving the readability and accessibility of patient records. This enabled more efficient clinical decision-making, as physicians could more easily extract and interpret relevant patient information.  
These applications demonstrate JAVIS's capacity to enhance documentation workflows, provide structured medical knowledge retrieval, and improve EMR usability, addressing key operational inefficiencies in clinical settings.
## 3 Test Results and Analyses
### Calculation Method of Strain in DIC  
DIC is an effective method for measurements of surface displacement and strain, widely used in rock mechanics [22; 23; 24; 25]. The DIC-2D speckle system can monitor the deformations in two directions, \(u\) and \(v\), for each point on the free surface. In the coordinate system shown in Figure 2, the \(xy\) plane is the free surface. The relationship between the strain components and in-plane deformation for a point can be expressed by [26]  
\[\begin{split}\varepsilon_{x}&=\frac{\partial u}{ \partial x}+\frac{1}{2}\bigg{[}\bigg{(}\frac{\partial u}{\partial x}\bigg{)}^ {2}+\bigg{(}\frac{\partial v}{\partial x}\bigg{)}^{2}\bigg{]}\\ \varepsilon_{y}&=\frac{\partial u}{\partial y}+ \frac{1}{2}\bigg{[}\bigg{(}\frac{\partial u}{\partial y}\bigg{)}^{2}+\bigg{(} \frac{\partial v}{\partial y}\bigg{)}^{2}\bigg{]}\\ \gamma_{xy}&=\frac{\partial u}{\partial y}+\frac{ \partial v}{\partial x}+\frac{\partial u}{\partial x}\frac{\partial u}{ \partial y}+\frac{\partial v}{\partial x}\frac{\partial v}{\partial y}\end{split} \tag{3}\]  
where \(u\) and \(v\) are the deformations for a point in the \(x\) and \(y\) directions, respectively, \(\varepsilon_{x}\), \(\varepsilon_{y}\), and \(\gamma_{xy}\) are the linear and angular strains of the point in the \(x\) and \(y\) directions, respectively.  
The in-plane principal strains \(\varepsilon_{1}\) and \(\varepsilon_{2}\) can be expressed as [27]  
\[\begin{array}{l}\varepsilon_{1}=\frac{\varepsilon_{x}+\varepsilon_{y}}{2}+ \sqrt{\left(\frac{\varepsilon_{x}-\varepsilon_{y}}{2}\right)^{2}+\frac{1}{4} \gamma_{xy}^{2}}\\ \varepsilon_{2}=\frac{\varepsilon_{x}+\varepsilon_{y}}{2}-\sqrt{\left(\frac{ \varepsilon_{x}-\varepsilon_{y}}{2}\right)^{2}+\frac{1}{4}\gamma_{xy}^{2}} \end{array} \tag{4}\]  
The tensile strain failure criterion [28; 29] for surface crack formation is used  
\[\varepsilon_{t}\geq\varepsilon_{td} \tag{5}\]  
where \(\varepsilon_{t}\) is the maximum dynamic tensile strain of a point during the blasting and \(\varepsilon_{td}\) is the dynamic tensile fracture strain of the rock.
# Preliminary Impact Assessment of the Ad Hoc Separation Minima: A New Separation Mode
## 4 Fast Time Simulation Results
### LECMZMU Sector  
#### 4.1.1 Capacity  
* Baseline scenario The capacity value obtained from the FTS computed during the busy week (8-14 July) is 46 aircraft/hour (46.95 aircraft/hour), as shown in Figure 5 (red arrow). The declared capacity of the LECMZMU sector is slightly lower, 45 aircraft/hour. Therefore, the value obtained from the CAPAN methodology is considered acceptable. The capacity value determined for the Baseline Scenario serves as a benchmark with which the capacity values associated with Scenario 1 and Scenario 2 can be compared.
* Scenario 1 The new TC WL in the LECMZMU sector with the Ad Hoc separation concept implemented results in a new capacity value. This was computed by simulating the LECMZMU scenario again but applying the ADSM between the different aircraft pairs. As shown in Figure 6, the obtained result is 47 aircraft/hour (47.79 aircraft/hour).
* Scenario 2 Considering that ASMT includes CD&R functionalities, from a conservative perspec  
Figure 4: LECMZMU, LECMTLU, and LECMDGU sectors of MADRID ACC.  
tive, it can be assumed that there would be a 50% improvement in the detection task and a 20% improvement in the decision making. This means a time reduction of 6 s in detection and 5 s in decision making, as mentioned before. Therefore, by calculating the new WL value, and subsequently the corresponding capacity, a value of 48 aircraft/hour (48.06 aircraft/hour) is obtained in Scenario 2, as shown in Figure 7.  
Table 2 provides a summary of the numerical capacity results obtained from the CAPAN methodology for the three scenarios presented in LECMZMU. The application of this concept would increase capacity in relative terms by 2 aircraft/hour (from 46 to 48 aircraft), while in absolute terms this would be an increment of 1.1 aircraft/ hour, which represents an increase of 2.3% in the current declared capacity of the sector.  
Figure 5: LECMZMU capacity 5 NM separation—Baseline Scenario.  
Figure 6: LECMZMU capacity Ad Hoc separation—Scenario 1.
# Improvement of Electro-Optical Properties of PSLC Devices by Silver Nanowire Doping
## 2 Materials and Methods  
The working principle of PSLC device fabricated in this study is shown in Figure 1. When no electric field is applied, the negative LC and polymer molecules are orientated perpendicularly to the substrate. In this state, the incident light will not be scattered and the device is transparent. When an electric field is applied, the negative LC molecules want to orientate parallel to the substrate, but the polymer network will disturb the rotation and a multi-domain structure will form. The incident light will be scattered and the device becomes opaque.
# Empirical Trials on Unmanned Agriculture in Open-Field Farming: Ridge Forming
## 3 Results and Discussion
### Linearity Performance  
The linearity performance of the developed robot during ridge forming is illustrated in Figure 5. To assess this objective, the acquired GPS-RTK data were compared with the baseline, which is an ideal straight line between the start and end points. From the data collected during repeated operations, the location gap between the baseline and the robot's autonomous driving was 4.84 \(\times\) 10\({}^{-4}\)\(\pm\) 1.83 \(\times\) 10\({}^{-4}\) m. This represents the vertical distance between the actual and ideal locations. When converted to standard SI units, the deviation is \(\sim\)0.267 m. However, these results do not account for external factors, such as field slope, sensor performance errors, friction coefficients, and tire deflection. By excluding these factors, the linearity could potentially be improved further. Despite these considerations, the developed robot demonstrates the potential to replace conventional methods for ridge forming.
## 4 Field Verification  
The Hulusu coal mine, located in Ordos city, is currently facing significant pressure to reduce emissions from coal gangue. In an effort to increase the consumption of coal gangue without disrupting mining operations at the working face, the mine has adopted overburden isolated grout filling technology. This method involves the simultaneous filling of overburden fracture through surface boreholes during mining. The technique was initially tested at the 21404 working face, which extends over a strike length of 3055 m, a mining width of 310 m, and an average depth of approximately 650 m. The coal seam has an inclination angle in the range of 0-3\({}^{\circ}\), a thickness in the range of 3-6 m, and a bulk density of 1.4 t/m\({}^{3}\).  
The construction of the coal gangue overburden isolated grout filling system in the Hulusu coal mine begins with the transportation of raw gangue to the primary crushing system for initial fragmentation. Subsequently, a secondary crushing system is employed  
Figure 6: Results of the gangue compaction experiments: (**a**) coal gangue compaction body; (**b**) law of compaction coefficient change in the gangue slurry with a threshold pressure.  
to break down the gangue blocks further. Finally, wet ball mills are utilized to transform the crushed gangue into a slurry suitable for grouting filling (Figure 7).  
The average depth of the filling zone in the 21404 working face of the Hulusu coal mine is 450 m. The design filling pressure ranges from 10 to 12 MPa, with a corresponding compaction coefficient for the gangue slurry of approximately 0.47. Given that the ratio of the filling volume to the mining volume is 40%, the implementation of gangue overburden  
Figure 7: Gangue filling method in the Hulusu coal mine: (**a**) coal gangue ball-milling and pulping system; (**b**) project photos.  
isolated grout filling in the 21404 working face can dispose of coal gangue that accounts for 57% of the mined coal volume. In reality, the output of coal gangue in the working face is less than 15% of the mined volume. The implementation of overburden isolated grout filling can fully realize the zero discharge of coal gangue. From November 2022 to April 2024, the total amount of filling gangue was 1,068,000 t, with a single-day filling capacity reaching 4000 t. Engineering applications have confirmed the viability and efficiency of pulverizing raw coal gangue for the creation of slurry used in overburden isolated grout filling.
## 2 Mathematical Preliminaries  
Consider a two-dimensional scenario in which a spacecraft \(S\) initially (time instant \(t=t_{0}\triangleq 0\)) covers a circular, Keplerian orbit of assigned radius \(r_{0}\) around a primary body of gravitational parameter \(\mu\). The spacecraft is equipped with a continuous-thrust propulsion system which gives a freely steerable thrust vector and a propulsive acceleration \(\mathbf{a}_{p}\) whose magnitude \(a_{p}\) ranges in the interval \([0,\,a_{m}]\), where \(a_{m}>0\) is the maximum (finite) value of \(a_{p}\). The continuous propulsive acceleration induced by the primary propulsion system is used to transfer the spacecraft to a circular, coplanar, target orbit of assigned radius \(r_{f}\
eq r_{0}\) by minimizing the required flight time \(\Delta t=t_{f}-t_{0}\equiv t_{f}\), where \(t_{f}>t_{0}\) is the final time instant. In other terms, the spacecraft transfer mission coincides with a minimum-time, circle-to-circle orbit raising if \(t_{f}>r_{0}\), or orbit lowering when \(r_{f}<r_{0}\). Bearing in mind the symmetry of the transfer problem, introduce a classical polar reference frame \(\mathcal{T}(O;r,\theta)\) with the origin in the primary body center-of-mass \(O\), in which \(r\) is the \(O\)-\(S\) distance and \(\theta\) is the polar angle measured from the \(O\)-\(S\) line at the initial time instant. The polar reference frame \(\mathcal{T}\) is sketched in Figure 1, where \(\tilde{t}_{r}\) (or \(\tilde{t}_{\theta}\)) is the radial (or transverse) unit vector. Figure 1: Polar reference frame \(\mathcal{T}(O;r,\theta)\) with origin in the primary body center-of-mass and spacecraft propulsive acceleration vector \(\mathbf{a}_{p}\). Blue line \(\rightarrow\) circular parking orbit; red line \(\rightarrow\) circular target orbit; black line \(\rightarrow\) spacecraft optimal transfer trajectory. In Figure 1, the direction of the spacecraft propulsive acceleration vector is identified by the thrust angle \(\alpha\in[-\pi,\ \pi]\) rad, which is defined as the angle (measured counterclockwise into the plane of the circular parking orbit) between the \(O\)-\(S\) line and the direction of \(\mathbf{a}_{p}\). Accordingly, the propulsive acceleration vector can be written as a function of the (control) angle \(\alpha\) as  
\[\mathbf{a}_{p}=a_{p}\ \cos\alpha\,\hat{\mathbf{r}}_{r}+a_{p}\ \sin\alpha\,\hat{\mathbf{r}}_{\theta} \tag{1}\]  
so that the polar form of the spacecraft equations of motion are the well-known  
\[\dot{r}=u\quad,\quad\dot{\theta}=\frac{\
u}{r}\quad,\quad\dot{u}=-\frac{\mu}{r ^{2}}+\frac{\
u^{2}}{r}+a_{p}\ \cos\alpha\quad,\quad\dot{v}=-\frac{u\,v}{r}+a_{p}\ \sin\alpha, \tag{2}\]  
where \(u\) (or \(v\)) is the radial (or transverse) component of the spacecraft velocity. The system of differential Equation (2) is completed by the 4 initial conditions that model the spacecraft traveling in the circular parking orbit, viz.
# Emotion Analysis AI Model for Sensing Architecture Using EEG
## 4 Application and Usage of Brainwave-Based Fine-Tuned Model in Architectural Spaces
### Fine-Tuned Model Validation and Performance Evaluation
#### 4.1.5 Discussion and Implications  
The OpenAI API primarily serves as a model hosting and inference platform and does not provide built-in performance evaluation tools. As a result, users must implement custom scripts to collect model predictions and compute metrics such as accuracy, precision, recall, F1 score, and Cohen's Kappa. It is critical to maintain consistency in label formatting, as variations in label representation (e.g., \"1 positive\" vs. \"positive (1)\") can lead to misalignment in accuracy calculations. This study conducted eight fine-tuning iterations, culminating in Version 8, which achieved 60.3% accuracy, a significant improvement over earlier versions. However, challenges remain, particularly in the classification of neutral (0) emotions, as well as the model's tendency to over-predict positive (1) emotions. These findings suggest that further refinement is necessary, considering the intrinsic characteristics of EEG signals and the GPT model's inherent optimisation for text-based processing. Given the increasing adoption of CNN-LSTM hybrid architectures for EEG-based emotion classification due to their ability to capture both spatial and temporal features, this study explores a two-step approach in which CNN-LSTM-extracted features are processed by a GPT-based model. This strategy is expected to mitigate classification inconsistencies between the neutral and positive classes, thereby enhancing overall classification performance.