esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results and Discussion
### Hop Cone Analysis
#### 2.3.2 Cyranose 320 Analysis on Hop Cones  
Electronic nose output demonstrates the ability of the instrument to differentiate among the samples belonging to different fertilisation treatments. The data collected by the 32 e-nose sensors were analysed using principal component analysis (Figure 1; Tables S2 and S3). PCA explains 82.39% of the variability, illustrating the discriminating capacity of the e-nose. Although the data analysis shows interesting results, the instrument did not allow the molecules detected by each sensor to be identified.
# A High Full Quality CMOS Image Sensor for Space Applications
## 2 Sensor Architecture
### Pixel Operation for Multiple Charge Transfer  
Figure 4a and b show the timing diagrams of single and triple charge transfer operations for the pixel control signals, respectively. The pixel readout process during T\({}_{\text{READ}}\) will be explained in Section 2.4. First of all, one of the 3000 rows in the pixel array is selected by using the row select signal (SEL), and in case of single charge transfer, the photo-generated charge in the PPD is transferred to the FD capacitor when the transfer gate control signal (Tx) is high. The pixel signal value is then sampled by a sampling capacitor in the readout circuit when the signal sampling signal (P\({}_{\text{SIG}}\)) is high. After sampling the pixel signal value, the FD node is reset to an initial voltage when the RST is high. The reset value of the FD node is finally sampled using a sampling capacitor in the readout circuit for an analog delta reset sample (DRS) operation [28]. Starting from the first row, this procedure is repeated until the last row is processed.  
The control signals for the multiple charge transfer operation are the same as the ones for the single charge transfer operation except that the Tx becomes high multiple times. As exemplified for triple charge transfer in Figure 4b, the photo-charge is transferred twice by making the Tx high two times before the SEL becomes high, and the last charge transfer is performed when the pixel row is selected, as with the single charge transfer operation.  
Figure 4: Timing diagram: (**a**) Single charge transfer; (**b**) Multiple charge transfer.  
Figure 3: Potential diagram of the multiple charge transfer: (**a**) Initial potential of pinned photodiode (PPD) and floating diffusion (FD) node; (**b**) Integration of photo-generated charges in PPD; (**c**) Charge transfer to the FD node; (**d**) Accumulated charges in FD node after multiple charge transfer.  
Notice that the main difference from the conventional 4T pixel operation is that the operation order for the pixel reset and the photo-charge transfer is switched. While the photo-charge is transferred to the FD node after the reset in the conventional 4T pixel, the charge is transferred before the pixel reset, which allows for multiple charge transfer.
## 4 Simulation Experiments and Analysis Based on Error Sensitivity
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Stability Accuracy**} & \multicolumn{5}{c}{**Error Levels [\(m^{\circ}\)]**} \\ \cline{2-6}  & **1** & **5** & **10** & **20** & **50** \\ \hline \multirow{5}{*}{**Error Types** (For each line, the error level is increasing \(\triangle\theta\) [\(\mu rad\)] (Data in the Central Area with Three Decimal Places)} & 39.553 & 39.492 & 39.499 & 39.534 & 39.524 \\  & \(\delta_{apx}\) & 39.614 & 39.929 & 40.316 & 41.128 & 43.521 \\  & \(\delta_{apz}\) & 39.546 & 39.535 & 39.539 & 39.531 & 39.520 \\  & \(\triangle\alpha_{am}\) & 39.540 & 39.516 & 39.515 & 39.518 & 39.505 \\  & \(\triangle\beta_{am}\) & 39.496 & 39.528 & 39.525 & 39.571 & 39.564 \\  & \(\triangle\alpha_{pmn}\) & 39.511 & 39.548 & 39.526 & 39.530 & 39.508 \\  & \(\triangle\beta_{pmn}\) & 39.536 & 39.538 & 39.548 & 39.523 & 39.555 \\  & \(\varepsilon_{ax}\) & 39.519 & 39.531 & 39.530 & 39.532 & 39.542 \\  & \(\varepsilon_{ay}\) & 39.434 & 39.093 & 38.755 & 37.982 & 35.669 \\  & \(\varepsilon_{px}\) & 39.521 & 39.511 & 39.518 & 39.542 & 39.505 \\  & \(\varepsilon_{pz}\) & 39.567 & 39.727 & 39.922 & 40.366 & 41.700 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Simulation results under BD Dis of \(1.5^{\circ}/1 Hz\). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Stability Accuracy**} & \multicolumn{5}{c}{**Error Levels [\(m^{\circ}\)]**} \\ \cline{2-6}  & **1** & **5** & **10** & **20** & **50** \\ \cline{2-6}  & **1** & **5** & **10** & **20** & **50** \\ \hline \multirow{5}{*}{**Error Types** (For each line, the error level is increasing \(\triangle\theta\) [\(\mu rad\)] (Data in the Central Area with Three Decimal Places)} & 39.553 & 39.492 & 39.499 & 39.534 & 39.524 \\  & \(\delta_{apx}\) & 39.614 & 39.929 & 40.316 & 41.128 & 43.521 \\  & \(\delta_{apz}\) & 39.546 & 39.535 & 39.539 & 39.531 & 39.520 \\  & \(\triangle\alpha_{am}\) & 39.540 & 39.516 & 39.515 & 39.518 & 39.505 \\  & \(\triangle\beta_{am}\) & 39.496 & 39.528 & 39.525 & 39.571 & 39.564 \\  & \(\triangle\alpha_{pmn}\) & 39.511 & 39.548 & 39.526 & 39.530 & 39.508 \\  & \(\triangle\beta_{pmn}\) & 39.536 & 39.538 & 39.548 & 39.523 & 39.555 \\  & \(\varepsilon_{ax}\) & 39.519 & 39.531 & 39.530 & 39.532 & 39.542 \\  & \(\varepsilon_{ay}\) & 39.434 & 39.093 & 38.755 & 37.982 & 35.669 \\  & \(\varepsilon_{px}\) & 39.521 & 39.511 & 39.518 & 39.542 & 39.505 \\  & \(\varepsilon_{pz}\) & 39.567 & 39.727 & 39.922 & 40.366 & 41.700 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Simulation results under BD Dis of \(1.5^{\circ}/0.5 Hz\). In those tables, for each column, the first piece of data is the error level of this column, and for the rest of the grid, each relevant error type on the left is equal to the error level, with all of the other errors equal to 0 simultaneously. For each line, the first grid is the error type of this line, and for the rest of the grid, the error increases corresponding to the error level on the top of the column, and other error types are still all equal to 0 simultaneously.
## 3 Results  
Coffee Consumers Group Identical Samples and Separate Coffees Based on Roast Level and Storage Condition  
In Figure 2, the consensus product map for the 12 evaluated coffee samples shows that coffee consumers were able to discriminate between light and dark roast coffees, as all light roast \"City\" samples are positioned on the left-hand side of the MFA map while all dark roast \"Vienna\" coffee samples are positioned on the right-hand side. Further, the 95% confidence ellipses obtained via a bootstrapping algorithm do not overlap between the two roast levels, indicating that the coffee samples smelled perceivably different. Overall, coffee consumers were also able to place the blind duplicate samples close to each other, with all blind duplicated samples showing overlapping confidence ellipses. This indicates that participants perceived the duplicated samples as similar and that participants were able to execute the PM task.  
Roast level separates the samples along the first dimension, explaining 32% of the overall variance, with the lighter roast \"City\" samples on the left-hand side and the darker roast \"Vienna\" coffees on the right-hand side of the map. Along the second dimension, explaining another 14% of the total variance, samples are separated due to storage conditions, with freshly roasted and freezer-stored samples positioned at the bottom of the map and room temperature-stored coffee samples positioned towards the center or top of the map.  
Looking within the two different roast levels, it becomes apparent that differences due to storage conditions are more dramatic for the dark roast \"Vienna\" samples than the light roast \"City\" samples. All \"City\" roast samples are placed close to each other with overlapping confidence ellipses, indicating that the changes due to storage conditions (fresh roast CN vs. freezer storage CF vs. room temperature  
Figure 2: Projective mapping (PM) consensus product map of the coffee samples with their 95% confidence ellipses. Descriptors are projected onto the product space. Samples are coded by roast level (dark “Vienna” roast “V”; light “City” roast “C”) and storage condition (freezer storage “F”; room temperature storage “R”; and newly roasted “N”). Numbers indicate blind duplicates presented to the panelists. Descriptors in bold differ significantly among the samples by Cochrane’s Q-test (\(p<0.1\)).  
storage CR) are less apparent for the lighter roast coffee. In contrast, for the dark roast \"Vienna\" samples, storage conditions led to perceivable and clear differences with consumers positioning the room temperature-stored samples (VR) far from the freshly roasted VN and freezer-stored VF \"Vienna\" samples. These differences are also statistically significant as the confidence ellipses of the VR samples do not overlap with any of the other dark roast (VN, VF) or light roast (CN, CF, CR) samples.
# River Discharge and Saltwater Intrusion Level Study of Yangtze River Estuary, China
###### Abstract  
The Yangtze River Estuary (YRE) is the largest estuary in China, with three-order bifurcations and four outlets into the sea. In recent years, issues of saltwater intrusions have received increased attention due to the increased levels and frequencies of the intrusions. The saltwater intrusions into the YRE resulting from river discharges were investigated in this study based on river discharge levels at the Datong station. A hydrodynamic and salinity transport model (MIKE21) was used to quantify the influences of the river discharges on the saltwater intrusions in the YRE. The model was effectively validated through observational data of the tidal and salinity levels. The 25%, 50% and 70% frequencies of the river discharges during the dry seasons were determined to be 18,112, 16,331 and 14,832 m\({}^{3}\)/s, respectively. A multi-year averaged river discharge of 27,856 m\({}^{3}\)/s was used to simulate the salinity level changes resulting from the different river discharges. The results revealed the following: (1) the salinity of the South Branch (SB) was distributed as \"high-low-high\"; and the changes in the salinity levels were greatly affected by the river discharges. A strong correlation was found between the salinity and flow in the North Branch (NB) and SB of between 0.917 and 1; (2) the changes in the river discharges had major impacts on the changes in the salinity levels in the SB. When the runoff was 27,856 m\({}^{3}\)/s, the salinity excessive area rate (the ratio between salinity excessive area (>0.45%) and the SB area) less than 10%. However, when the river discharges were reduced to 16,331 m\({}^{3}\)/s, the salinity excessive area rate is more than 50%; (3) As the river discharges decreased, the amplification line (0.2%) also rapidly decreased, and the amplification lines (0.45%, 2%) increased. At points far from the river's entrance, the effects of the runoff were observed to be weakened, such as the amplification lines gradually becoming reduced; (4) the changes in the river discharges were observed to have significant impacts on the freshwater reservoir water withdrawal. When the river discharges were maintained at 27,856 m\({}^{3}\)/s, the salinity of the Baogang, Chenhang, and Qingcaosha Reservoirs remained below 0.45%. The salinity levels of the four reservoir locations examined in this study were found to exceed the Chinese drinking water standard (0.45%) for more than 23 days in the 14,832 m\({}^{3}\)/s river discharge scenario, which negatively affected the drinking water of the population living near the YRE.  
Keywords: river discharge; saltwater intrusion; MIKE21; Yangtze River Estuary; freshwater reservoir +
Footnote †: journal:
## 5 Use Case of 6G-OPENSEC-Security
### General Architecture  
The framework for the 6G-OPENSEC-SECURITY project is rooted in the ETSI GS ZSM 002 specification. ZSM aims to create a fundamental architecture (Figure 6) that allows for completely autonomous solutions for 5G and beyond network operations, i.e., with zero human intervention.  
Figure 6: ZSM reference architecture.  
This way, it is possible to draw the alignment of the 6G-OPENSEC-SECURITY with the ZSM architecture. Figure 7 illustrates this alignment with the general ZSM structure.  
The framework for the 6G-OPENSEC-SECURITY project is rooted in the ETSI GS ZSM 002 specification. Figure 7 illustrates this framework, drawing its alignment with the general ZSM structure.  
In Figure 7, two pivotal elements emerge within this framework: the Security Closed-Loop Governance (SCLG) and the Security Closed-Loop Automation (SCLA). The SCLG consists of two integral modules: the Closed-Loop Manager and the Closed-Loop Coordinator. On the other hand, the SCLA comprises four distinct modules: Security Decision, Security Data Analytics, Security Data Collection, and Security and Privacy data service. The responsibilities of each one of these elements are:  
* **SCLA**--This component is responsible for processing the results obtained from the monitoring process. It identifies the data samples requiring evaluation and triggers processes to mitigate the detected security threat.
* **SCLG**--This component oversees the coordination and management of all created closed loops and their governance.  
Collectively, these components form the Security Closed-Loop (SCL), working in tandem to ensure robust governance and automation within the network-slicing environment. Overall, the SCL is a mechanism that serves as the backbone for continuous monitoring and analysis of network service performance. It helps operators take proactive measures, anticipating potential issues before they arise while enabling prompt, reactive responses to detected anomalies. This comprehensive approach ensures the sustained delivery of high-quality services while maximising security within the network slice. However, in this section, the focus will be on the SCLA component and its modules.
## 2 Methodology  
Research papers presenting deployments are selected based on multiple criteria:  
* The years 2002 up to 2011 have been reviewed uniformly, without emphasis on any particular year. Deployments before the year 2002 are not considered, as early sensor network research projects used custom hardware, differing from modern embedded systems significantly.
* Articles have been searched using the Association for Computing Machinery (ACM) Digital Library ([[http://dl.acm.org/](http://dl.acm.org/)]([http://dl.acm.org/](http://dl.acm.org/))), the Institute of Electrical and Electronics Engineers (IEEE) Xplore Digital Library([[http://ieeexplore.ieee.org/](http://ieeexplore.ieee.org/)]([http://ieeexplore.ieee.org/](http://ieeexplore.ieee.org/))), Elsevier ScienceDirect and SpringerLink databases. Several articles have been found as external references from the aforementioned databases.
* Deployments are selected to cover a wide WSN application range, including environmental monitoring, animal monitoring, human-centric applications, infrastructure monitoring, smart buildings and military applications.  
WSN deployment surveys can be found in the literature [13, 14, 15, 16, 17, 18]. This survey focuses on more thorough and detailed review regarding the aspects important for WSN OS design. This survey also contains deployments in the prototyping phase, because of two reasons. First, rapid prototyping and experimentation is a significant part of sensor network application development. Second, many of the research projects develop a prototype, and stable deployments are created later as commercial products, without publishing technical details in academic conferences and journals. Therefore software tools must support experimentation and prototyping of sensor networks, and the requirements of these development phases must be taken into account.  
Multiple parameters are analyzed for each of the considered WSN deployments. For presentation simplification, these parameters are grouped, and each group is presented as a separate subsection.  
For each deployment, the best possible parameter extraction was performed. Part of information was explicitly stated in the analyzed papers and web pages, and part of it was acquired by making a rational guess or approximation. Such approximated values are marked with a question mark right after the approximated value.
# ACE: A Routing Algorithm Based on Autonomous Channel Scheduling for Bluetooth Mesh Network
## 3 Bluetooth Mesh Communication and Problems
* **Uncertain end-to-end latency:** Communication occurs successfully in Bluetooth Mesh only if two nodes advertise and scan on the same channel at the same time, thus uncertain latency can be introduced by the time to match the ADV channel and the scanning channel. Besides, the path of packet transmission from the same source node to the same destination node may be different even in the same topology. Furthermore, packets collisions issue in Bluetooth Mesh could also cause an uncontrollable delay due to packets retransmission. The above problems are unacceptable for some applications with stable latency requirements. * **Prone to be interference by BLE devices:** BLE devices are ubiquitous in our daily life such as smartphones, Bluetooth headsets, and fitness bands, etc. It is possible that various BLE devices from outside the Bluetooth Mesh network have a negative impact on the communication flows inside the network, especially from iBeacon devices using three ADV channels.
# High-Accuracy Calibration Based on Linearity Adjustment for Eddy Current Displacement Sensor
## 3 Calibration Method Based on Linearity Adjustment Model
### Sensitivity Adjustment Function  
The mapping relationship \(f_{s}:u\to U\) between the actual sensor output value \(u\) and the required output value \(U\) is established so as to change and adjust the slope of the output characteristic curve (the sensitivity). The actual output value \(u\) and the required output value \(U\) satisfy:  
\[\left\{\begin{array}{l}u\in[u_{min},u_{max}],\\ U\in[U_{min},U_{max}].\end{array}\right. \tag{8}\]  
Then, the spatial linear mapping from \(u\) to \(U\) can be described by:  
\[f_{s}:u_{s}=U_{min}+\frac{u-u_{min}}{u_{max}-u_{min}}(U_{max}-U_{min}), \tag{9}\]  
where \(u_{s}\) is the output voltage after sensitivity adjustment.  
According to the \(x-u\) calibration data of the sensors in Figure 4, it can be concluded that \(u\) satisfies \(u\in\)[\(-1\) V, \(+1\) V]. Then, the sensitivity can be maximized based on the input voltage range requirements of the acquisition module (NI, PXI-6289), which is limited to \(U\in\)[0 V, \(+10\) V], and the sensitivity adjustment function can be described by:  
\[f_{s}:u_{s}=5\cdot(u+1). \tag{10}\]  
The sensitivity adjustment results are shown in Figure 6 and Table 2.  
Figure 5: Calibration flow chart.  
As shown in Figure 6 and Table 2, the adjusted output range is nearly at 0\(\rightarrow\)10 V with the sensitivity increased from around 0.33 V/mm to above 1.63 V/mm.
# Privacy-Preserving Data Mining on Blockchain-Based WSNs
## 4 Methodology
### Distributed Data Mining
#### 4.3.2 Design of the Experiment  
Each sensor node collects its own data (in our simulation, there is actually a round-robin data assignment scheme that simulates sensor node data collection, as is depicted in Figure 3). The sensor node data is then used to \"augment\" the Hoeffding tree received from the previous sensor node, producing an updated Hoeffding tree that is then passed to the next sensor node. The whole procedure is depicted in Figure 4 and has already been proved effective in [45].  
As can be observed from Figure 4, the initial Hoeffding tree (\(HT_{1}\)) is first learned in the batch from the data provided by the first sensor node--\(\mathit{Sensor}_{1}\) (\(\mathit{Data}_{1}\)). This tree is then propagated through the WSN, and in each subsequent WSN node (\(\mathit{Sensor}_{k}\)), an updated Hoeffding tree is generated (\(HT_{k}\)) from the previous Hoeffding tree (\(HT_{k-1}\)) and the data from the current sensor node--\(\mathit{Sensor}_{k}\) (\(\mathit{Data}_{k}\)). The final Hoeffding tree is then represented by the last tree in this chain--\(HT_{n}\).  
Such a partial Hoeffding tree's (\(HT_{k}\)'s) propagation through the WSN (instead of the actual sensor data being propagated) is most suitable for preserving privacy, and also diminishes the load on the WSN.
# A Reverse Shortest Path Tree-Based Multicast Joining Node Selection Method
## 3 Reverse Shortest Path Tree Based MJN Selection Method
### Construction of RSPT
#### 3.1.2 Using Probe Message to Collect Information  
Upon receiving a probe request, each node responds with a probe message that is sent to the DR via unicast. The nodes forwarding the probe message add their own load status to it. In large network topologies, the probe messages may contain excessive node information that exceeds the maximum transmission unit (MTU) limit. Therefore, the probe message supports fragmentation. The design of the probe message is illustrated in Figure 1.  
The header of the probe message has the following fields.  
1. Type: This field indicates the type of the message, whether it is a probe message or a request message;
2. Fragment Tag: This flag indicates whether the message is the last fragment or not. If the message does not have any fragments or is the last one, the flag is set to 1; otherwise, it is set to 0;
3. Fragment Index: This parameter indicates the index of the current fragment;
4. Node Cnt: This parameter indicates the maximum number of node information that can be stored in the message;
5. Node index: This parameter indicates the number of nodes with information that has been recorded in the message;
6. (IM ID-load status): Each intermediate node's information is stored as a key-value pair, with the ID of the node as the key and its load status as the value. \[\text{Cost}(i,j)=\begin{cases}\alpha\frac{\text{dis}(i,j)}{\text{ Diameter}}+\beta\frac{\text{FT}}{\tau_{\text{max}}}+\gamma\frac{\text{ PL}}{\tau_{\text{max}}}+\delta\frac{\text{avIT}}{\tau_{\text{max}}}+\epsilon \frac{\text{avIL}}{\tau_{\text{max}}}&\text{dis}(i,j)\leq K\\ \zeta\frac{\text{dis}(i,j)}{\text{ Diameter}}&\text{else}\end{cases}\] (1) \[\alpha+\beta+\gamma+\delta+\epsilon =1\] \[0\leq\alpha,\beta,\gamma,\delta,\epsilon \leq 1\] \[\zeta \geq 1\]  
Figure 1: Probe message design.