esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work  
### Image Generation Algorithm  
Since [PERSON] et al. [15] proposed the Denoising Diffusion Probabilistic Model (DDPM) in 2020, researchers have been continuously improving the model and extending its applications to multiple fields. This paper's image generation field of interest has surpassed the previous SOTA model GAN [16]; [PERSON] et al. have proposed that the diffusion model can generate significantly better image quality than the GAN model [17]. The primary reason for this phenomenon lies in the high difficulty of training GAN models. For instance, one major challenge is how to select optimal weights for the saddle points of numerous loss functions. In practical applications of GANs, researchers have found that although certain techniques can be used to enhance the stability of saddle points, loss functions often struggle to converge to these saddle points, which directly results in poor model stability. This issue remains unresolved to date. However, diffusion models can circumvent this problem: they only require training a \"generator\" with a simpler training objective function. Moreover, diffusion models can be directly applied to downstream tasks without fine-tuning. Consequently, most subsequent research related to image generation has been based on diffusion models.  
The diffusion model is a generative model that generates data matching the target distribution. The model generally works by adding noise to the image so that the input image becomes a noise distribution and then constructs the input image from this noise. The model is divided into two phases: the diffusion phase and the construction phase.  
whose primary function is to predict the noise added to the image. The formula is expressed as follows [15]:  
\[\widetilde{Z}=UNet(x_{t},t) \tag{3}\]  
where \(\widetilde{Z}\) is the noise added to the image as predicted by the model, and \(x_{t}\) is the output of the tth time step of the diffusion phase. The training of the UNet network is accomplished by reducing the L1 loss or L2 loss between the added noise \(Z\) and the predicted noise \(\widetilde{Z}\). By using Formula (2), we can derive the formula for the construction of \(x_{0}\) through \(x_{T}\)[15]:  
\[x_{0}=\frac{1}{\sqrt{\alpha_{t}\alpha_{t-1}\cdots\alpha_{1}}}(x_{t}-\sqrt{1- \alpha_{t}\alpha_{t-1}\cdots\alpha_{1}}\widetilde{Z}) \tag{4}\]  
However, suppose we construct \(x_{0}\) by the above formula. In that case, the specific effect is poorer because it is a one-step construction, so the authors construct the image by multiple time steps, and the formula is expressed as follows [15]:  
\[x_{t-1}=\frac{1}{\alpha_{t}}(x_{t}-\frac{\beta_{t}}{\sqrt{1-\alpha_{t}}}) \widetilde{Z}+\sqrt{\frac{1-\overline{\alpha_{t-1}}}{1-\overline{\alpha_{t}} }}\beta_{t}Z \tag{5}\]  
where \(\widetilde{Z}\) is obtained through Formula (3), and Z is the data that fits the standard normal distribution. The \(x_{0}\) can be constructed by Formula (5). The authors add \(Z\) to the formula here to increase the uncertainty throughout the construction phase, giving a random perturbation to the model [15].
# Mechanical Structure Design and Experimental Study of Gamma-ray Monitor for Small Satellite Payload
## 2 Materials and Methods
### Mechanical Structure Design of Novel GRM  
The crystal support structure was designed based on the smooth shell structure of the central bearing tube, and the flange end was added outside the crystal support structure to connect with the bracket for radiant cooling plate by bolts. The design of the electronic box was based on the truss-stiffened shell structure of the central bearing cylinder, and the axial truss supporting the circuit board. A PCB board with a thickness of 2 mm was adopted, in a disc shape to facilitate the uniform layout of SiPM. Six convex ears were installed to connect with the electronic box for bolt connection. After the above fundamental design was completed, the main components were designed iteratively and corresponding models were corrected in real-time to obtain the preliminary design. A rabbet was designed at the junction of the crystal support structure and the electronic box to ensure the electromagnetic shielding of the structure. The photoconductive glass was bonded to the sodium iodide crystal and crystal support structure through GN522 adhesive. In addition to the electronic box using aluminum alloy 2A12-H112, the other mechanical structure of the novel GRM adopted magnesium alloy ZK61M. The weight of the novel GRM is about 750 g, and the external size is \(\Phi\)146.6 mm \(\times\) 32.8 mm, as shown in Figure 1a.
## 2 Mathematical Formulation of the 2L-VRPSC
### Stochastic Programming Model of the 2L-VRPSC
#### 2.2.2 Formulation of the 2L-VRPSC
The formulation of the loading constraints that adopted from [3], are defined as follows:  
\[0\leq x_{ib}\leq(W-w_{ib})(1-\Omega_{ib})+(W-l_{ib})\Omega_{ib}, \forall i\in V_{0},b\in\{1,\cdots,m_{i}\}, \tag{8}\] \[0\leq y_{ib}\leq(L-l_{ib})(1-\Omega_{ib})+(L-w_{ib})\Omega_{ib}, \forall i\in V_{0},b\in\{1,\cdots,m_{i}\},\] (9) \[\left\{ \begin{aligned} & x_{ib}+w_{ib}(1-\Omega_{ib})+l_{ib} \Omega_{ib}\leq x_{jb^{\prime}},\\ & x_{jb^{\prime}}+w_{jb^{\prime}}(1-\Omega_{jb^{\prime}})+l_{jb^{ \prime}}\Omega_{ib^{\prime}}\leq x_{ib},\forall i,j\in V_{0},b\in\{1,\cdots,m_ {i}\},b^{\prime}\in\{1,\cdots,m_{j}\},\end{aligned} \right.\] (10) \[\left\{ \begin{aligned} & y_{ib}+l_{ib}(1-\Omega_{ib})+w_{ib} \Omega_{ib}\leq y_{jb^{\prime}},\\ & y_{jb^{\prime}}+l_{jb^{\prime}}(1-\Omega_{jb^{\prime}})+w_{jb^{ \prime}}\Omega_{ib^{\prime}}\leq y_{ib},\forall i,j\in V_{0},b\in\{1,\cdots,m_ {i}\},b^{\prime}\in\{1,\cdots,m_{j}\},\end{aligned} \right.\] (11) \[\left\{ \begin{aligned} & x_{ib}+w_{ib}(1-\Omega_{ib})+l_{ib} \Omega_{ib}\leq x_{jb^{\prime}},\\ & y_{jb^{\prime}}+l_{jb^{\prime}}(1-\Omega_{jb^{\prime}})+w_{jb^{ \prime}}\Omega_{jb^{\prime}}\leq y_{ib},\\ & x_{jb^{\prime}}+w_{jb^{\prime}}(1-\Omega_{jb^{\prime}})+l_{jb^ {\prime}}\Omega_{jb^{\prime}}\leq x_{ib},\\ &\forall i,j\in R^{k}:\sigma(i)<\sigma(j),b\in\{1,\cdots,m_{i}\}, b^{\prime}\in\{1,\cdots,m_{j}\}.\end{aligned} \right. \tag{12}\]  
Constraints (8) and (9) enforce that each item must be placed in the loading space. Constraints (10) and (11) enforce that any two items to be transported by the same vehicle do not overlap. The first-in-last-out (FILO) requirements are modeled by constraint set (12).
# Biogenic Nanoparticles: Synthesis, Characterisation and Applications
## 2 Biogenic Synthesis of Nanoparticles
### Types of Nanoparticles  
#### 2.1.1 Silver  
Silver nanoparticles (AgNPs) have been researched extensively for applications in different fields and have raised considerable interest in the biomedical field because of its antimicrobial properties and antioxidant potency [30]. Their antibacterial properties depend on size, with smaller particles being more effective against many pathogens. They have been shown to successfully counter Gram-negative and positive bacteria, and also have a variety of uses in wound dressings [31].
## 3 Modeling Framework
### System Description and Operational Assumptions  
The proposed delivery architecture is based on a multimodal system that couples UAVs with a public transportation network, specifically the bus system of the city of Belfort, France. This system, illustrated in Figure 1, enables drones to perform last-mile deliveries by leveraging the existing circulation of public buses, thereby avoiding the need for dedicated delivery vehicles.  
Each drone is initially loaded onto the roof of a bus at a central logistics hub located near the train station, which acts as a depot and sorting point. Parcels are pre-loaded on the drone prior to departure. As the bus moves along its line, the drone monitors its GPS position. When approaching a delivery point (i.e., a bus stop with an associated delivery), the drone autonomously takes off from the bus roof, ascends vertically, performs a short horizontal flight to the customer's location (within a 15 m radius from the stop), and completes the delivery by lowering the parcel and notifying the recipient.  
After delivery, the drone climbs to a cruising altitude, navigates horizontally to intercept the same or a following bus on the same line, and lands back on its roof. If necessary, the drone may change carriers (i.e., switch to another bus on the same line) to maintain operational continuity. Wireless recharging through inductive systems located on the bus roof is also envisioned to extend mission range and reduce dependency on full battery swaps.  
The bus lines operate on a fixed schedule, with a bus dispatched every 10 to 15 min depending on the line. This dense cadence enables sustained parcel throughput without introducing new traffic on the urban road network.  
The key assumptions of the system are summarized below:  
* Deliveries are made sequentially at bus stops along a single line.  
Figure 1: System architecture of the Deliv’air project: UAVs perform deliveries from the roof of public buses and rejoin the line for sequential missions.  
* The drone performs short point-to-point deliveries within a fixed radius from the bus stop.
* UAV takeoff and landing occur vertically from/to the moving bus roof.
* The drone does not return to a depot; it remains in operation along the bus line.
* Each drone mission includes takeoff, ascent, cruise, delivery, return cruise, and landing.
* Drone speed is assumed constant during horizontal segments; vertical ascent/descent durations are computed.
* No wind or weather effects are modeled at this stage.
* Payload mass varies from 1 to 10 kg depending on the scenario.  
This architecture allows for continuous and energy-efficient delivery in urban areas while reducing the number of ground vehicles required and minimizing road congestion.
## 4 Prototype Implementation
### Healthcare Services Prototype
#### 4.2.1 Remote Health Monitoring Service  
The remote health monitoring service is implemented as a web application that is accessible by the users (e.g., doctors, nurses) via web browsers. It utilizes Java servlet technology for handling incoming HTTP POST notifications from the IIP and simple hypertext markup language (HTML) with asynchronous JavaScript and XML (AJAX) for presenting the output to the users. This service shows the latest measurements information from the remote patient, which currently includes pulse rate, blood oxygen saturation (SpO2), spirometry and location. The service subscribes to these four information channels and receives almost real-time notifications from the IIP. At the patient's side, an android application has been implemented for gathering measurements from a wireless pulse oximeter and spirometer devices through Bluetooth connections. This application sends all measurements to their corresponding information channels in the IIP. Location information is gathered directly by the Android application from the smartphone's built-in global positioning system (GPS) sensor. The web application's user interface is shown in Figure 11. By utilizing this service, healthcare personnel can monitor the patient's health condition remotely. Since mobile devices are used at the patient's side, the patient is not restricted to measurements from a specific location.
## 3 Results and Discussion
### 2D-HSQC-NMR Spectra  
For 2D-HSQC-NMR, the lignin samples were prepared using 20 mg of lignin dissolved in 0.75 mL DMSO-D6, and then the analysis data were acquired with a Bruker AVANCE 600 spectrometer (Bruker, Germany). Meanwhile, the relative quantification and the corresponding assignments of A\({}_{\alpha}\), B\({}_{\alpha}\) and C\({}_{\alpha}\) are tabulated in Table 5, and their corresponding NMR spectra are delineated and shown in Figure 4. It is quite obvious that the GVL extracted lignin, in comparison with MWL, was slightly lower in \(\beta\)-O-4 contents, ranging from 23.8% at more severe conditions to 31.1% at rather mild conditions. However, the other linkages of \(\beta\)- \(\beta\) and \(\beta\)-5 showed somewhat comparable values with MWL. In one previous study, 58.2% of \(\beta\)-O-4 linkages were reported when they extracted lignin from cotton stalks with 80% aqueous GVL but at 170 \({}^{\circ}\)C and employing H\({}_{2}\)SO\({}_{4}\) as a catalyst [29]. Our lower values might be due to the crude form of the extracted lignin fractions.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sample** & **G Units** & **S Units** & **Total** \\ \hline Pine MWL & \(810.6\pm 34.9\) & - & \(810.6\pm 34.9\) \\ \hline GVL Lignin 140:2 & \(300\pm 35.8\) & - & \(300\pm 35.8\) \\ \hline GVL Lignin 140:4 & \(250\pm 36.8\) & - & \(250\pm 36.8\) \\ \hline GVL Lignin 160:2 & \(242\pm 46.0\) & - & \(242\pm 46.0\) \\ \hline GVL Lignin 160:4 & \(210\pm 10.4\) & - & \(242\pm 10.4\) \\ \hline GVL Lignin 180:2 & \(196\pm 24.7\) & - & \(196\pm 24.7\) \\ \hline GVL Lignin 180:4 & \(150\pm 48.1\) & - & \(150\pm 48.1\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Derivatization followed by reductive cleavage (DFRC)—GC/MS results.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline Pine MWL & 43.5 & 2.0 & 16.1 \\ GVL Lignin 140:2 & 28.8 & 3.5 & 11.6 \\ GVL Lignin 140:4 & 31.1 & 3.6 & 13.2 \\ GVL Lignin 160:2 & 26.9 & 3.0 & 13.0 \\ GVL Lignin 160:4 & 23.8 & 3.8 & 12.0 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Quantification (%) of various structures in four selected lignin samples.
## 2 Background and Preliminary
### Anomaly Types in MTS
#### 2.2.3 Inter-Metric Anomalies  
Global intermetric anomaly: Certain metrics in MTS experience significant changes, with a length matching the entire sequence. This type of anomaly typically occurs when initial parameters or system states deviate from the normal range. When the change in a key metric triggers a series of abnormal fluctuations in related metrics, the entire sequence reflects this anomaly. An example of an entire sequence anomaly is shown in Figure 3a, as its behavior significantly deviates from that of other metrics.  
\[diss_{corr}(corr(\mathbf{x}^{i},\mathbf{x}^{j}))>\delta \tag{9}\]  
where \(\mathbf{x}^{i}\), and \(\mathbf{x}^{j}\) are different metrics, and \(corr\) represents the degree of correlation between them.  
Local intermetric anomaly: There is correlation between metrics within the entity, which can be linear or nonlinear, such as the relationship between temperature and electricity consumption. If these correlations are disrupted, MTS will exhibit anomalous behavior. During a certain time period, the relationships between metrics significantly deviate from historical patterns, suggesting a change in the interdependencies among the metrics. For example, in Figure 3b, there is initially a positive correlation between two metrics, but the highlighted red area on the left clearly breaks this relationship, leading to anomalies.  
\[diss_{corr}(corr(\mathbf{x}^{i},\mathbf{x}^{j}),corr(\mathbf{x}^{i}_{t:t+k}, \mathbf{x}^{j}_{t:t+k})>\delta \tag{10}\]  
where \(k\) is the time window. If the correlation is disrupted from \(t\) to \(t+k\), it indicates that the correlation coefficient has exceeded the threshold compared to the normal value.  
Temporal-local intermetric anomaly: The anomalies violate the dependencies between metrics and temporal relationships, focusing on how a specific metric breaks its correlation and deviates from its historical trend within a certain time period, as indicated by the highlighted red area on the right side of Figure 3b.
## 4 Result and Analysis
### Distribution of the Water Yield on an Annual Scale  
The water production model for the study area from 2010 to 2018 was developed utilizing the InVEST water production module, as illustrated in Figure 5. The average annual water yield in the study area is 6.725 billion m\({}^{3}\), as calculated from the results of the InVEST model, as shown in Table 8. In terms of the temporal analysis, water production in the study area was comparatively plentiful in 2010, 2013, 2016, and 2018. The water production depths, as determined by the pixel grid, were 224 mm, 208 mm, 235 mm, and 242 mm. The mean annual water production depth was 148 mm. Table 7 indicates that the annual precipitation in 2010, 2013, 2016, and 2018 exceeded 700 mm, and the water production depth during these four years was the highest recorded in the study period. It is evident that precipitation directly influenced the annual water production capacity in the research area. In years with low precipitation, the annual water depth is correspondingly reduced. Certainly, this does not imply that precipitation is the sole factor influencing the annual water volume. The capacity for water production is influenced by potential evapotranspiration, topography, and other variables; however, precipitation more directly  
\begin{table}
\begin{tabular}{c} **Recoverable** \\ **Value** \\ **(104 m\({}^{3}\).a\({}^{-1}\))** \\ \end{tabular} \\ \hline
1 & 912.52 & 410.63 & 1 & 870.99 & 391.95 & 1 & 810.92 & 364.91 \\
2 & 2224.06 & 1000.83 & 2 & 2122.86 & 955.29 & 2 & 1  
indicates the robustness of regional water production capacity and is the primary factor impacting water production depth. In 2018, the precipitation was plentiful, resulting in peak water output. In 2014, owing to the reduced precipitation, the water production depth was merely 16 mm.  
The water output in the research area steadily rose from south to north and sharply declined from west to east annually, according to the watershed scale. The western portion of the study region had the highest water production value, whereas the southern portion of the study area had the lowest water production value. The rainfall for the year is particularly closely related to the water production level for the year, and the water production level for forest land is significantly higher than that of urban water production, according to the data comparison and a combination of the effects of climate, rainfall, and terrain on regional water production. This suggests that forest land has a strong capacity to conserve water resources and contributes to improving the local ecological environment's surrounding water resources.
## 2 Proposed Methodology
### Feature Selection Module Based on Maximal Information Coefficient  
The Maximal Information Coefficient (MIC) [31] was proposed by [PERSON] et al. and is calculated based on mutual information. Mutual information quantifies the amount of information one random variable contains about another. The formulas for calculating mutual information are presented in Equations (1) and (2), while the formulas for computing MIC are provided in Equations (3) and (4).  
\[I(x;y)=\int p(x,y)\log_{2}\frac{p(x,y)}{p(x)p(y)}dxdy \tag{1}\]  
\[I[x;y]\approx I[X;Y]=\sum_{X,Y}p(X,Y)\log_{2}\frac{p(X,Y)}{p(X)p(Y)} \tag{2}\]  
where \(p(x,y)\) represents the frequency of falling into the grid at (\(x,y\)) and \(p(x)\) and \(p(y)\) represent the probability distribution over \(x\) and \(y\), respectively.  
\[mic(x;y)=\max_{a\times b<B}\frac{I(x;y)}{\log_{2}\min(a,b)} \tag{3}\]  
\[MIC[x;y]=\max_{|X||Y|<B}\frac{I[X;Y]}{\log_{2}(\min(|X|,|Y|))} \tag{4}\]  
In Equations (3) and (4), \(a\) and \(b\) represent the number of rows and columns in the partition and \(B\) has the value \(n^{0.6}\), where \(n\) is the total number of samples. If \(MIC\) is 1, it indicates perfect correlation, while 0 represents no linear relationship. Compared to traditional correlation methods such as Pearson's and Spearman's, the \(MIC\) more effectively captures nonlinear relationships between variables, while offering high computational efficiency and robustness. Therefore, \(MIC\) was employed in this study to select the spectral characteristic parameters and spectral similarity metric parameters most strongly correlated with dye concentration and ultrasonic pretreatment power. Specifically, the spectral similarity metric parameters and the spectral characteristic parameters with the highest MIC values were chosen, as they most effectively reflect the underlying relationships between the variables. These parameters, which showed the strongest associations with dye concentration and ultrasonic power, were preferred over other metrics, ensuring both precision and robustness in the modeling process.
# Measurement and Modeling of Narrowband Channels for Ultrasonic Underwater Communications
###### Abstract  
Underwater acoustic sensor networks are a promising technology that allow real-time data collection in seas and oceans for a wide variety of applications. Smaller size and weight sensors can be achieved with working frequencies shifted from audio to the ultrasonic band. At these frequencies, the fading phenomena has a significant presence in the channel behavior, and the design of a reliable communication link between the network sensors will require a precise characterization of it. Fading in underwater channels has been previously measured and modeled in the audio band. However, there have been few attempts to study it at ultrasonic frequencies. In this paper, a campaign of measurements of ultrasonic underwater acoustic channels in Mediterranean shallow waters conducted by the authors is presented. These measurements are used to determine the parameters of the so-called \(\kappa\)-\(\mu\) shadowed distribution, a fading model with a direct connection to the underlying physical mechanisms. The model is then used to evaluate the capacity of the measured channels with a closed-form expression.  
sensor networks; underwater acoustic communications (UAC); fading channels; statistical channel modeling; narrowband channel measurements; Doppler spread; parameter estimation; channel ergodic capacity; \(\kappa\)-\(\mu\) shadowed; Rice shadowed +
Footnote †: journal: Sensors