esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Uncertainties in the Evaluation  
There are several factors that have influenced our evaluation and contributed to the associated uncertainties. First, our evaluation is limited to Harvard Forest due to data availability; therefore, the results may not be representative for other locations with different species composition. Second, the uncertainties in ground observations, including phenology (e.g., large variation in observed leaf offset, Figure 3), LAI and carbon flux could propagate into our evaluation. Third, as simulated LAI was compared with site-level observation, the limitation of the Agro-IBIS model (e.g., linear decrease of LAI in a fixed period in autumn) and different spatial scales (grid cell _vs._ site) might introduce uncertainties in the selection of ground phenology reference. Finally, different data processing strategies (e.g., smoothing and interpolation of vegetation indices) might cause differences in the phenology derived using the same methods. In order to reduce such uncertainties, comprehensive evaluation at different locations should be conducted with ground phenological observations that can be standardized and that have a spatial scale more consistent with the satellite (e.g., images from \"PhenoCam\" [54]).
# Research on Safety Prediction of Sector Traffic Operation Based on a Long Short Term Memory Model
## 3 Time Series Forecasting Model
### LSTM Model  
LSTM is a special kind of the recurrent neural network (RNN), which is a class of artificial neural network where connections between units form a directed cycle [32]. In most RNNs, the hidden layer function is an elementwise application of sigmoid function. However, LSTM uses purpose-built memory cells to store information, which is better at finding and exploiting long range dependencies in the data [33].  
The basic network structure is shown in Figure 4, where \(C_{t}\) represents the cell state, \(tanh\) is the hyperbolic tangent activation function, and \(\sigma\) is the sigmoid activation function. The LSTM cell takes input \(x_{i}\) and the hidden vector from the last time step and presents \(\hat{y}_{i}\) as an estimated output with a new hidden vector and memory vector.  
For the current time step, \(\hat{y}_{i}=\text{LSTM}(x_{i},y_{i})\) is established. Firstly, the forget gate determines the cell state by checking the input value, which is the number of conflict events in the last time instant \(i-1\):  
Figure 4: Network structure of LSTM.  
\[f_{i}=\sigma(W_{f}\cdot[y_{i-1},x_{i}]+b_{f}) \tag{8}\]  
Secondly, the input gate decides how much input information enters the current cell, which determines cell state within the cell:  
\[\tilde{C}_{i}=\tanh(W_{c}\cdot[y_{i-1},x_{i}]+b_{C}) \tag{9}\]  
\[C_{i}=f_{i}*C_{i-1}+i_{i}*\tilde{C}_{i} \tag{10}\]  
Finally, the output gate determines the current output which is the number of conflicts at the current time instant \(i\).  
\[o_{i}=\sigma(W_{o}\cdot[y_{i-1},x_{i}]+b_{o}) \tag{11}\]  
\[y_{i}=o_{i}*\tanh(C_{i}) \tag{12}\]  
In order to test and verify the forecasting performance of model, prediction models based on SVR, LSTM, and RR are established, respectively. SVR is the most common application form of SVMs, which could achieve good results in many domains [34], while RR is one of a family of penalized methods for the regression task [35]. The training and verification processes are shown in Figure 5. The results of another two prediction models, i.e., SVR and RR, are given for the purpose of comparison. By doing this, we forecast the flight conflicts for the current time instant \(t\). The prediction can enable us to estimate the number of conflicts in a defined sector, which could provide insights in traffic safety.
## 8 Methods for Designing, Testing, and Deploying the GBG-RPL Framework in Smart Healthcare CPS
Another crucial component of the deployment strategy is user awareness and training, which involves teaching administrators and users about GBG-RPL's features and security precautions. These methods enable modifications in response to real-time input and evolving needs, guaranteeing the framework's flexibility and long-term efficacy in smart healthcare scenarios. 2. Additional Considerations  
The implementation of the GBG-RPL framework employs a thorough and repetitive methodology to guarantee ongoing improvement and peak efficiency. Using an iterative development cycle facilitates continuous improvement by incorporating knowledge gained from the deployment and testing phases into later iterations [101]. In order to guarantee alignment with user needs, interactive development methods involve stakeholders such as administrators, cybersecurity specialists, and healthcare professionals. Security audit and compliance measures are implemented to conduct thorough security audits, ensuring adherence to industry standards and regulations [102]. User feedback is actively sought to inform design improvements, adopting a user-centric design approach that ensures the uninterrupted availability of healthcare services. Additionally, the deployment emphasizes the adoption of best practices in cybersecurity and healthcare data management, drawing insights from successful implementations in related domains [103].
# MZAP--Mobile Application for Basketball Match Tracking and Digitalization of Endgame Reports
## 5 MZAP Android Application--Match Report Preview and Digital Signing
### Security Mechanisms for PDF Match Endgame Reports
#### 5.2.2 Applying Digital Signatures to PDF Files  
Digital signing of the document is performed using a PKCS#12 KeyStore, which is generated locally and embedded within the MZAP Android application. While the generated digital signatures use SHA-256 with RSA and are cryptographically valid and verifiable, the use of a self-signed X.509 certificate means they are not inherently trusted by standard verification mechanisms, as they lack endorsement from a recognized Certificate Authority (CA). However, as the certificate is self-signed, standard revocation mechanisms such as CRL or OCSP are not applicable. Certificate trust distribution and rotation are managed via server-side validation mechanisms and enforced through application updates.  
In the event of certificate compromise, implemented revocation strategy relies on server-side blacklisting of the compromised certificate's unique identifier and mandatory application updates to replace the affected KeyStore. While this approach lacks real-time revocation capabilities provided by CA-backed PKI systems, it is sufficient within our narrowly scoped trust model. Despite its limitations (particularly the distribution and bundling of a PKCS#12 KeyStore) this approach was adopted since it suits the needs of the Basketball League of Serbia of internal use.  
Future development plans aim to enhance security by integrating digital signatures with a Time Stamping Authority (TSA). Additionally, the process of digitally signing and password-protecting the PDF documents will be migrated entirely to the MZAP server. This will enable full utilization of certificate-based security mechanisms, leveraging CA-issued certificates typically delivered via USB tokens.
## 3 Synthesis of Amine-Functionalized Silica/Organosilica Membranes
### Sol-Gel Route
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Precursors** & **Calcination** & **Operation** & **CO\({}_{2}\) Permeance [10\({}^{-10}\)** & **CO\({}_{2}\)/N\({}_{2}\)** & **Ref.** \\
**Used** & **Temperature [\({}^{\circ}\)C] \({}^{\circ}\)**b** & **Temperature [\({}^{\circ}\)C] \({}^{\circ}\)**b** & **mol/(m\({}^{2}\) s Pa)]** & **Selectivity [-]** & **Ref.** \\ \hline TEOS, APTES, & 300-350 (vacuum) & 22 & 100–800 & 40–80 \({}^{\circ}\) & [6] \\ \hline TEOS, GlyNa & 300–350 (vacuum) & 22 & 100–400 & 50–100 \({}^{\circ}\) & [6] \\ \hline TEOS, APTES & 300–500 & 22 & 70 & 55 \({}^{\circ}\) & [30] \\ \hline BTESE, APTES & 250 & 50 & \textasci{-}350 & 1–10 \({}^{\text{d}}\) & [7] \\ \hline APTES & 300 & 35 & 30 & 8 \({}^{\text{d}}\) & [21] \\ \hline BTPP & 300 & 35 & 337 & 25 \({}^{\text{d}}\) & [21] \\ \hline PA-Si & 250 & 35 & 260 & 22 \({}^{\text{d}}\) & [22] \\ \hline SA-Si & 250 & 35 & 170 & 11 \({}^{\text{d}}\) & [22] \\ \hline TA-Si & 250 & 35 & 1720 & 21 \({}^{\text{d}}\) & [22] \\ \hline QA-180 \({}^{\text{e}}\) & 180 & 35 & 40 & 4 \({}^{\text{d}}\) & [23] \\ \hline QA-180-250 & 250 & 35 & 520 & 24 \({}^{\text{d}}\) & [23] \\ \hline QA-250 & 250 & 35 & 490 & 18 \({}^{\text{d}}\) & [23] \\ \hline \hline \end{tabular}  
* The temperature used for the membrane formation; \({}^{\text{b}}\) The temperature operated for gas permeation test; \({}^{\text{c}}\) Separation factor of a mixed system; \({}^{\text{d}}\) Ideal selectivity; \({}^{\text{e}}\) QA-180 represents a quaternary ammonium-silica membrane fired at 180 \({}^{\circ}\)C. Abbreviations: **TEOS**, tetraethoxysilane; **APTES** and **PA-Si**, 3-aminopropyltriethoxysilane; **GlyNa**, sodium glyciuate; **BTESE**, 1,2-Bis(triethoxysil)jethen; **BTPP**, 4,6-bis(3-triethoxysil-1-propoxy)-1-3-pyrimidine; **SA-Si**, 3-(triethoxysil)-\(N\)-methylpropan-1-amine, **TA-Si**, 3-(triethoxysil)-\(N\)-\(N\)-dimethylpropprop-1-amine, **QA-180**, Trimethyl 3-(trimethoxysil)propylammonium chloride derived membrane fired at 180 \({}^{\circ}\)C, **QA-180**-250**, Trimethyl 3-(trimethoxysil)propylammonium chloride derived membrane fired at 180 \({}^{\circ}\)C before a post-heat-treatment at 250 \({}^{\circ}\)C; **QA-250**, Trimethyl[3-(trimethoxysil)propylammonium chloride derived membrane fired at 250 \({}^{\circ}\)C.  
\end{table} 
Table 1: Summary of synthesis conditions and membrane performance for reported amine-functionalized silica-based membranes fabricated via a sol-gel route.
# Remote Sensing-Based Multilayer Perceptron Model for Grassland Above-Ground Biomass Estimation
## 3 Data Acquisition and Preprocessing
### Remote Sensing Data Acquisition and Preprocessing  
Remote sensing data is the fundamental data source for constructing the grassland above-ground biomass inversion model in this study. To obtain the spectral reflectance characteristics of the grassland vegetation in the study area, Landsat 9 remote sensing imagery was selected as the primary data source. Landsat 9 imagery has a spatial resolution of 30 m, which can clearly capture vegetation cover and canopy reflectance characteristics, meeting the needs for precise biomass estimation. In this study, Landsat 9 Level 2 data were downloaded from the US Geological Survey (USGS) Earth Explorer platform ([[https://earthexplorer.usgs.gov](https://earthexplorer.usgs.gov)]([https://earthexplorer.usgs.gov](https://earthexplorer.usgs.gov)), accessed on 15 August 2024), including two scenes: path 123/row 29, acquired 6 August 2024 and path 124/row 29, acquired 13 August 2024. These scenes were selected following two criteria: (1) temporal proximity to the ground sampling date (15 August 2024), with a maximum nine-day interval to minimize phenological differences; and (2) minimal cloud cover (<5%) to ensure high-quality spectral data. The two images were mosaicked using ENVI software v.5.3 to cover the entire Xiwuzhumuqin Banner, followed by cropping with the administrative boundary shapefile. This approach ensures that the remote sensing data accurately reflect the vegetation status at the time of sampling while avoiding cloud-induced artifacts. The data have undergone atmospheric and geometric corrections to ensure quality and applicability.  
Figure 1: Distribution map of the study area and sampling points.  
To match the timing with the ground-truth biomass measurements, imagery from around 15 August 2024 was selected. After mosaicking two images using ENVI software v.5.3, the data were clipped using the Xiwuzhumuqin Banner administrative boundary shapefile, resulting in remote sensing data covering only the study area. Subsequently, reflectance data from the red, green, blue, near-infrared, and shortwave infrared bands were extracted, and vegetation indices (such as NDVI, EVI, etc.) were calculated for model analysis. These spectral data directly reflect the vegetation cover and health status of the grassland, providing key feature variables for biomass inversion.
## 2 Materials and Methods
### Case Study: AS-IS Downtime Management Process  
Through a period of observation at a production company using plastic injection molding, it was possible to define the downtime management process. A very simple and intuitive notation that leads back to the classic notation of a flow chart was used to represent the AS-IS process. The downtime, as shown in Figure 2, can start from different source events: a block of the machine due to an alarm or a block of the machine performed manually. Only some operators are authorized to command the machines; they are called conductors. Conductors could manually shut down a production unit due to malfunction, waste production, or management needs. All information communications from the production department take place via walkie-talkie. Not all operators have these devices, only the so-called indirect production workers (e.g., maintenance workers, technologists, quality control workers, and conductors); line operators do not have walkie-talkies. The latter, in many cases, are the first to notice the problem; they need an intermediary equipped with a walkie-talkie to report the problem to the conductor. In some cases, following the conductor's intervention, they evaluate the need to request the intervention of a specialized operator (maintenance technician or technologist); also, in this case, communication takes place via walkie-talkie.  
The exchange of information via walkie-talkie allows for fast and effective communication, but at the same time, it does not allow for the tracking of the information exchanged. The collection of information relating to the stoppage time, as shown in Figure 3, is entrusted to the work shift manager, who draws up a special Excel form at the end of the work shift in which they report the duration and cause of the stops that affected the different production units.  
Figure 2: AS-IS downtime management process.
# Distributed Unmanned Aerial Vehicle Cluster Testing Method Based on Deep Reinforcement Learning
## 4 The Proposed Algorithm
### Main Idea  
Aiming at the computing and communication resource problems encountered in the actual operation of UAVs, this work simplifies the engineering complexity of the communication and testing task execution of UAV cluster ground stations and each machine in the cluster, and provides a resource optimization scheme for large-scale adaptive UAV cluster tests. In this study, the architecture of a UAV cluster communication simulation system is designed and constructed to simulate communication and match the testing tasks performed by the whole cluster, so as to reduce the workload of single and overall debugging during the flight of Unmanned Aerial Vehicles. Specifically, we divide the overall system into a ground controller and a cluster of UAVs containing multiple individual machines. In the system, we study and design a distributed testing task allocation method for UAV clusters based on reinforcement learning, aiming to achieve an optimal task allocation scheme, system load balancing, and bandwidth minimization during the UAV cluster test. The central server at the ground controller is responsible for training the distributed algorithm and model, calculating the current optimal mission deployment scheme according to different computing resources and communication quality, and monitoring the current UAV survival condition. After receiving the signal instruction, the UAV terminal plans the current task to be executed through its own calculation and planning component to ensure the safe flight and predetermined trajectory of the UAV while completing the task. Finally, the UAV transmits the communication and task status information during the mission execution back to the ground controller for fault handling and model updating.
# Algorithm for Base Action Set Generation Focusing on Undiscovered Sensor Values
## 2 Base Action Set Generation Algorithm Focusing on Undiscovered Sensor Values  
### Definition of an Action Fragment  
Our intent was to construct an algorithm to acquire a robot's base action set that does not depend on a specific task. Also, we wanted to make this base action set usable in many situations (i.e., universal). We chose a base action set that effectively causes changes in the external environment as a candidate. We hypothesized that an action to effectively change the external environment can be observed indirectly through events to find undiscovered sensor values. Discovering sensor values means situations that have never happened before occurred in the external environment. In other words, a robot can be considered to have caused some changes to it.  
On the basis of the above, our intent is to generate effective actions that cause changes in the external environment dynamically by generating actuator output signals without using pre-defined actions. Therefore, we define a unit that combines sensor input signals and actuator output signals for analyzing relationships between them. The \"action fragment\" is defined as a set of sensor input and actuator output signals for a specific period of time.  
The number of sensors and actuators are denoted as \(m\) and \(n\), respectively. The action fragment of \(m\) sensors and \(n\) actuators is denoted as \(\mathbf{F}\) and the data length of the action fragment \(\mathbf{F}\) is denoted as \(l\). \(\mathbf{F}\) contains each of the sensor input values and actuator output signals. The \(i\)th sensor input signal is denoted as \(s_{i}\), and the \(j\)th actuator output signal is denoted as \(a_{j}\). These are expressed as Equations (1)-(3).  
\[\mathbf{F}=[\mathbf{s}_{1},\cdots,\mathbf{s}_{m}|\mathbf{a}_{1},\cdots,\mathbf{a}_{n}]^{\mathrm{ T}} \tag{1}\]  
\[\mathbf{s}_{i}=[s_{i}(0),s_{i}(1),\cdots,s_{i}(t),\cdots,s_{i}(l-1)] \tag{2}\]  
\[\mathbf{a}_{j}=\big{[}a_{j}(0),a_{j}(1),\cdots,a_{i}(t),\cdots,a_{j}(l-1)\big{]} \tag{3}\]  
The action fragment is designed as a part of the robot's behavior. Thus, the actuator output signals are generated first, and the robot moves according to the signals when we use the action fragment. Then, the robot's sensor input signals are recorded, and they are combined with the actuator output signals as an action fragment. For example, if we use a differential wheeled robot equippedwith two speed-controllable wheels and a forward distance sensor, two actuator output signals are generated and input to the wheels. Then, the sensor input signals are recorded and combined to form the action fragment.
# Two-Level Congestion Control Mechanism (2 LCCM) for Information-Centric Networking
## 3 Design
### Overview of 2 LCCM
In addition, if the current state of the chunk transmission is intolerable, another replica node can continue this transmission service and some of the previously received data segments remain valid. The typical situation may be that the transmission link is under a heavy congestion state which means that the router cache queue is full and causes packet drops, high end-to-end delay, or the data service cannot continue due to link overload or disconnection. Once the replica switching occurs, the congestion control module at the receiver can collect the state of the links and adaptively adjust  
Figure 1: Protocol stack and architecture of 2 LCCM. the request sending rate to match the bandwidth of the new link state without adding additional mechanisms. Thus, the whole transmission process can be considered as a chunk transmission between the receiver and the sender under the rate adjustment of the congestion control algorithm, or a replica node selection or switching with the support of a copy selection method.
# Enhancing Wind Energy Harvesting Using Passive Turbulence Control Devices
## 4 Effect of the Coupling Strength
### Effect of Coupling Strength  
Figure 6 shows the output power of the GEH-PTC along with the load resistance with three different electromechanical coupling strengths. Figure 6a,b respectively represent the weak coupling (\(k^{2}/(4\zeta+4\zeta^{2})=0.029\)) and the critical coupling (\(k^{2}/(4\zeta+4\zeta^{2})=1\)), and the wind speed range is 2.098-2.646 m/s. When the electromechanical coupling strength presents weak coupling and critical coupling, the output power firstly increases and then decreases with the increase of external load, and it reaches the maximum value at the optimal load resistance. The optimal load resistance of the weak coupling and the critical coupling is respectively 1.1 M\(\Omega\) and 3.0 M\(\Omega\), which is very close to the theoretical one (\(R_{opt}=1/\omega_{n}C_{P}\)) [64]. When the wind speed is 2.646 m/s, the peak output power in the above two cases reaches 0.15 mW and 0.5 mW, respectively.  
For the strong coupling (\(k^{2}/(4\zeta+4\zeta^{2})=2.9\)), results are shown in Figure 6c. At each wind speed, there will be a trough between the two power peaks, and the corresponding resistance value of the trough is the optimal load resistance. Therefore, based on Figure 6, we can guess that the coupling strength is an important factor affecting the output power. The output power increases with an  
Figure 5: The output power comparison of the GEH-PTC and the VIVEH in experiment.  
increase in electromechanical coupling strength. However, when the coupling strength is higher than the critical coupling strength, the output power does not increase further, and the power reaches saturation. The change of output power along with the wind speed is shown in Figure 7, and the resistance is in range of 0.5-8.0 M\(\Omega\). As a whole, it can be observed that under the three coupling strengths power increases with increasing wind speed. However, at a higher wind speed, the power increase rate gradually decreases, which is consistent with the experimental results. In addition, as shown in Figure 7c, when the electromechanical coupling strength presents as strong coupling, the power increase rate under small resistance (\(R=0.5\) M\(\Omega\)) is obviously greater than that under large resistance (\(R=8.0\) M\(\Omega\)), which can be seen from the intersection of bold solid lines. Different from strong coupling, weak coupling (Figure 7a) and critical coupling (Figure 7b) do not show such a phenomenon of solid line crossing, and the power reaches the maximum at the optimal load (Figure 7a: \(R_{opt}=1.1\) M\(\Omega\); Figure 7b: \(R_{opt}=3.0\) M\(\Omega\)), which can also be reflected in Figure 6.