esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Data Processing
#### 2.4.2 Data Processing and Analysis  
The raw data obtained by the data collector needed to be filtered before use. In the process of CO\({}_{2}\) flux observation, weather factors such as rainfall or instrument failure would cause missing or abnormal observation data. The data needed to be screened and abnormal values removed. Night-time flux data with frictional wind speed less than 0.15 m\(\cdot\)s\({}^{-1}\) and data with a variance of more than 3\(\sigma\) from the monthly average were excluded. Linear interpolation was used for missing data within 2 h of a known point [51; 52].  
Since the installation height of AutoSIF-1 is close to the tree canopy, the impact of cloud and other weather conditions on the data is negligible. It is considered that the quality of the data obtained under weather conditions other than rainy days is reliable and can be used. Since PAR is the biggest driver of plant photosynthesis and fluorescence emission and is only a factor between sunrise and sunset, data outside this range was deleted. Data influenced by human factors or equipment failure that led to days with missing data were deleted. Due to the rapid changes in weather conditions, the data may have outliers of less than 0 or extra-large (within the \(\mu\pm 3\sigma\), where \(\mu\) and \(\sigma\) are the average and standard deviation, respectively), and these data were discarded. Overall, during the 122 day study period, a total of 85 days (including 45 sunny days and 40 cloudy days) of data can be used.
## 4 An Engineering Application
### Analysis of the Pipeline Settlement Displacement
#### 4.2.3 Result Analysis  
The settlement curve compared with the observed data is shown in Figure 17. It is shown that although the calculated settlement displacement of the pipeline is deeper than the observed results and the maximum displacement is larger, the predictions from this study method are, in general, consistent with the observed data. By analyzing and comparing, it can also be seen that the proposed calculation method tends to be somewhat conservative. Actually, the comparison results of GX01-GX05 are better than those of  
Figure 16: Inclinometer curve of CX4.  
GX06\(\sim\)GX09. The main reason is that the GX01\(\sim\)GX05 results are basically parallel to the edge of the foundation pit, and this distribution of measuring points agrees with the calculated model much better. For the measuring point GX05, due to the fact that the power pipeline is not parallel to the edge of the foundation pit, there is a large error between the calculated result and the measured data, but the trend of settlement and displacement changes at the GX06\(\sim\)GX09 is basically similar to the calculated results.  
Figure 18 is the error bar chart of the measuring points, and the negative value indicates that the calculated result is less than the observed value. There is a maximum error in the vicinity of the GX07, and the value is about 30%. This error can hardly be lowered because of the complexity of the foundation pit edge in relation to pipeline location, and this working condition is still discussed in detail later in parameter analysis. The negative error appears primarily on both ends of the pipeline, and this is due to the boundary constraint conditions. Data analyses show that the other measuring point's error is no more than 15%, and can even be less than 5%. The error results also prove the usefulness of the proposed method for a practical noncircular pipeline settlement analysis.  
Figure 17: Comparison of calculated result and observed result in different construction periods.  
Figure 19 illustrates the settlement displacement of GX07\(\sim\)GX09 during different construction periods. As can be seen from the result, the settlement of GX07 is lower than the other measuring points, which is not consistent with the theoretical calculations. The calculation model focuses on the pipeline parallel to the edge of the foundation pit, and this use of assumption can attempt to explain the difference in results. It is precisely because the pipeline is not parallel to the edge of the foundation pit that the number of support piles around GX07 is greater than that around GX08, which effectively regulates the pipeline settlement during the construction period. For GX09, although its distance to the edge of the foundation pit is approximately equal to the distance from GX07, the settlement value is larger than the value of GX07. It can be attributed to the fact that GX09 is adjacent to GX08, and the continuity of pipeline deformation leads to a relatively high settlement value.
## 2 Materials and Methods
### Hyperspectral-Derived Indices  
The indicators described in Table 1 were calculated from the hyperspectral imagery. These indices have been shown to be closely related to certain specific features of plant physiology [17, 23]. We selected four types of indices, related to: (i) fluorescence emission by PS-I photosystem, (ii) structural effects, (iii) chlorophyll content, and (iv) carotenoid content.  
The fluorescence emission was assessed from the radiance spectra by the Fraunhofer Line Depth (FLD) principle calculated from a total of three bands for the in (L763 nm) and out bands (L750 nm; L780 nm) (FLD3), using the equation described in [PERSON] _et al._[17]. Some other indices related to the emission of chlorophyll fluorescence as described in [PERSON] _et al._[26] were also included in the analysis.  
The structural indices were calculated to assess whether final yield in wheat could be captured by the Normalized Difference Vegetation Index, NDVI [27], with a modification of NDVI to increase its sensitivity, in the form of the Renormalized Difference Vegetation Index, RDVI [28]. Soil-adjusted indices (OSAVI and MSAVI) that minimize the background influence were included in the analysis as well as MCARI1, which has demonstrated a great potential for LAI predictions [29].  
The selected chlorophyll a + b indices were the Transformed Chlorophylll Absorption in Reflectance Index (TCARI) [30], the aforementioned TCARI, normalized by OSAVI to obtain TCARI/OSAVI, as proposed by [PERSON] _et al._[30] and used by [PERSON] _et al._[31]. Other indices related with the pigment content were also included in the analysis (Table 1).  
The Photochemical Reflectance Index (PRI) was calculated using the 570 nm band as a reference [32] in the form PRI = (R570 - R531)/(R570 + R531), also using the 515 nm band as reference as proposed by [PERSON] _et al._[33] and normalized by structural and chlorophyll effects (PRIn, [34]). Some other indices related with the carotenes content were included (CAR, [35]). RARS is related to the carotenoids content, as well as chlorophyll a + b [36].  
\begin{table}
\begin{tabular}{l c c} \hline
**Index** & **Equation** & **Reference** \\ \hline \multicolumn{3}{c}{_Fluorescence indices_} \\ \hline \multirow{2}{*}{FLD} & E\({}_{\text{out}}\cdot\text{L}_{\text{in}}-\text{E}_{\text{in}}\cdot\text{L}_{\text{out}}\) & [17, 37, 38] \\  & \(R683^{2}\) & \\  & (\(R675\cdot R691\)) & [26] \\ \multirow{2}{*}{R690/R630} & \(R690\) & [26] \\  & \(R688\cdot R710\) & \\  & \(R697^{2}\) & [26] \\ \multirow{2}{*}{R685/R655} & \(R685\) & [26] \\  & \(R655\) & \\ \hline \multicolumn{3}{c}{_Structural indices_} \\ \hline \multirow{2}{*}{NDVI} & \(R800-R670\) & [27] \\  & \(R800+R670\) & \\ \multirow{2}{*}{RDVI} & \(R800-R670\) & [28] \\ \hline \end{tabular}
\end{table}
Table 1: Hyperspectral indices used in this study.
## 2 Materials and Methods
### Antimicrobial Activity of Gaseous Citral on Inoculated Table Grape Berries  
Healthy table grape cv Italia, purchased from a local retail grocer (Sassari, Italy), were transported to the laboratory and immediately processed as described in this paper. Grape bunches were firstly washed with sterile distilled water, disinfected for 3 min in a hypochlorite solution (2.5 %, _v/v_), and then washed twice with distilled sterile water. After drying for 30 min in a sterile trap, single berries were detached; 5 \(\upmu\)L of 10\({}^{6}\) spores/mL were inoculated in the wound formed by pedicel removal and the berries were placed in a sterile reservoir (VWR, Milan, Italy). To assess the effect of gaseous citral on the growth of _R. oryzae_ ITEM 18876, six plastic trays containing each 6 inoculated berries were prepared; then, 3 reservoirs were placed inside 20 L sealed polyethylene box, equipped with a circulation fan and a heating system, which allow the citral to spread and saturate the box volume [25]. The other 3 reservoirs (positive control) were not exposed to the citral vapor and they were placed inside of plastic trays covered with a lid (34 \(\times\) 23 \(\times\) 16 cm, Gensini, Florence, Italy). Citral treatments were performed using two concentrations, the first equal to the MIC and the second one equal to 4-fold the MIC value (4 MIC); to favor the volatilization of citral, the circulation fan and heating system were left on for 2 h.  
Additional six trays containing 6 un-inoculated berries each as negative control were also prepared each as negative control. Three were placed inside the polyethylene box and the other three in a plastic tray with lid (34 \(\times\) 23 \(\times\) 16 cm, Gensini, Florence, Italy). The box and plastic tray were incubated at 25 \({}^{\circ}\)C for 7 days and the mycelia growth was observed daily.
## 5 AZTRM-D Simulated Real-World Scenario
### AZTRM-D Enabled System Versus the Hacker  
To truly appreciate the effectiveness of the AZTRM-D methodology, let us examine how a sophisticated adversary might attempt to breach Company A's infrastructure. This exercise demonstrates how the framework's integrated defenses neutralize each phase of a modern cyberattack.  
The attacker's first move is reconnaissance. They scan Company A's public-facing infrastructure for misconfigured cloud storage or exposed APIs. However, AZTRM-D ensures continuous posture management, with AI-driven compliance tools automatically correcting misconfigurations. Public APIs are secured behind ZT access policies, requiring continuous validation of user identity and device posture. Any anomalous request patterns trigger an automatic block and alert.  
Frustrated, the attacker pivots to a Man-in-the-Middle (MITM) network attack. However, all internal traffic is encrypted with strong, modern protocols like TLS 1.3. Furthermore, ZTNA policies require every connection to be continuously authenticated and assessed for risk, making it nearly impossible for an attacker to intercept traffic or move laterally [26]. The AI-powered network monitoring tools would detect the anomalous traffic from a MITM attempt and automatically isolate the affected network segment.  
The adversary then shifts to social engineering, using a sophisticated phishing email to obtain employee credentials. Even if the email bypasses AI-driven filters, the stolen credentials alone are insufficient. ZT identity verification, enforced by MFA and continuous behavioral analysis, would immediately block a login attempt from an unrecognized device or location, rendering the credentials useless [56].  
As a next step, the hacker might target the software supply chain, attempting to inject a malicious dependency into the build pipeline. This is a common vector for many organizations [28]. However, AZTRM-D's enforcement of AI-driven SCA and SAST at every stage of development means the malicious library is flagged and quarantined instantly. Every build artifact also undergoes cryptographic integrity validation, ensuring no unauthorized code reaches production [30].  
Desperate, the attacker might target IoT devices or attempt hardware-based attacks with a malicious USB drive. In both cases, the ZT principles of device integrity and least privilege provide a robust defense. The IoT devices operate on a micro-segmented network with firmware integrity checks, while endpoint protection policies prevent unauthorized peripherals like the rogue USB from executing code, immediately isolating the device from the network [8].  
At every stage, the adversary encounters insurmountable barriers reinforced by real-time analytics, dynamic access controls, and automated risk assessment. The AZTRM-D methodology fundamentally disrupts the cyberattack life cycle, ensuring that even a sophisticated attacker is rendered ineffective.
# Trypsin Inhibitor from Soybean Whey Wastewater: Isolation, Purification and Stability
## 3 Results and Discussion
### Isolation and Purification of STI
#### 3.2.2 The Effect of Ammonium Sulfate Saturation on STI Isolation and Purification  
Saling out is commonly used for protein separation and enrichment. In highly concentrated salt solutions, salt ions can bind to water molecules on the protein surface, destroying the hydration membrane and reducing the solubility of the protein, consequently separating the protein from other impurities, such as sugars [24].  
This study evaluated the effect of different ammonium sulfate saturation concentrations (25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, and 65% \(w/v\)) on the STI in soybean whey. As shown in Figure 3a, a higher level of ammonium sulfate saturation gradually increased the recovery rates of the soybean whey proteins and trypsin inhibitory activity, which displayed respective values of 64.48% and 89.47% as the ammonium sulfate saturation reached 40%. As ammonium sulfate saturation increased, the recovery rate of the trypsin inhibitory activity stabilized and did not increase, whereas that of the soybean whey protein continued to rise. The results indicated that the saturation of ammonium sulfate reached 40% and STI was precipitated basically, and the increasing saturation of ammonium sulfate would reduce the purity of STI. The electrophoretic analysis results of the precipitated protein also confirmed these findings (Figure 3b). When the ammonium sulfate saturation exceeded 40%, the trypsin inhibitor bands disappeared in gel, and the protein precipitated was a non-trypsin inhibitor with a molecular weight greater than 20 kDa. The results showed that the STI was the least soluble and most readily precipitated when the ammonium sulfate saturation level reached 40%, which was the optimal saturation for salting out STI from soybean whey. Because the STI recovered via ammonium sulfate salting out still contained some non-trypsin inhibitor, the precipitated protein required purification during the next step.  
Figure 2: The effect of pH on STI isolation and purification (**a**). The electrophoretic pattern of the protein isolated from the soybean whey by pH precipitation (**b**). Different letters on the same series indicate significant differences (\(p<0.05\)) in varying conditions.
## 3 PAZ Description  
The PAZ satellite platform is similar to the TerraSAR-X one and has been developed by Airbus DS. The instrument front-end has been designed by Airbus DS Spain/CASAespacio ( Construcciones Aerospaciales S.A.). The Spanish National Institute for Aerospace Technology (INTA, Instituto Nacional de Tecnologia Aerospacial) has been responsible for the development of the Ground Segment of the PAZ Mission, and it is responsible for PAZ scientific exploitation.  
Scientific exploitation conveys all knowledge areas related to SAR technology: radio-frequency, digital signal processing, image processing, remote sensing, _etc_. The main objectives are the following:  
* To identify areas of interest considering the Spanish environment and compromises.
* To provide data to the scientific community for educational, scientific and technological purposes.
* To research SAR systems' characterization and calibration, operating modes' definition, multi-static configurations and multi-sensor developments.
* To collaborate with high level SAR institutions.
* To develop a set of research-based demonstrators to prove SAR technology capabilities in different applications.  
Searchers or research groups can communicate their interest to the e-mail address \(PAZ\[EMAIL_ADDRESS]\).  
\begin{table}
\begin{tabular}{c c c c c} \hline
**RCS** & **Monostatic 1** & **Bistatic 1** & **Monostatic 2** & **Bistatic 2** \\ \hline Maximum & 37.385 dBm & 11.074 dBm & 23.611 dBm & \(-\)6.293 dBm \\ Minimum & \(-\)50.229 dBm & \(-\)28.678 dBm & \(-\)51.619 dBm & \(-\)22.193 dBm \\ Average & \(-\)3.331 dBm & \(-\)15.522 dBm & \(-\)20.532 dBm & \(-\)14.658 dBm \\ \hline \end{tabular}
\end{table}
Table 1: Bistatic RCS simulations results.  
Figure 12: Bistatic RCS averaged along the incidence direction azimuth, \(\phi_{i}\): (**a**) Bistatic 1 case study; (**b**) Bistatic 2 case study.  
For the proposed study, the antenna gain and the imaging modes are of great importance. They are analyzed in the following subsections.
## 4 Proposed Procedure for Tuning the Proposed Controller
### Proposed Approach for Controller Parameters Tuning  
Using the proposed method, the PM and tolerable time delay in current control loop (\(T_{td}\)) are determined for the studied system of Table 2. For the sake of simplicity, the values of all control parameters are considered the same (\(\rho=k_{i}=m_{i}\)) and PM, \(T_{td}\), and bandwidth of the \(i_{gd}\) and \(i_{gq}\) control loops are determined for the different values of the control parameter \(\rho\). It can be observed that an increase in control parameter \(\rho\) decreases \(T_{td}\) and increases the bandwidth (\(W_{b-curr}\)). The total time delay in the control loop due to computations and PWM sampling is \(T_{d}=1/f_{sw}\). Indeed, the control parameter \(\rho\) should be selected such that \(T_{td}\) is greater than \(T_{d}\).  
Considering \(T_{c}<50\)\(\upmu\)s and \(f_{\rm s}=10\) kHz (according to system parameters in Table 3), the total time delay in the current control loop is \(T_{d}=100\)\(\upmu\)s. Therefore, the controller parameter is selected as \(\rho=1500\), which results in \(PM=57^{\rm o}\), considering \(T_{d}\) in the control loop, and current loop bandwidth \(W_{b-curr}=980\) rad\(/\)s. Accordingly, the PI controller parameters for the DC-link voltage and the reactive power controller are selected as \(k_{p}=k_{q}=300\), and \(k_{I}=1500\). Using the selected \(k_{p}\), \(k_{I}\) and \(k_{q}\), PM of the DC-link voltage and the reactive power control loops are about \(70^{\rm o}\) and \(90^{\rm o}\) respectively. Also, the DC-link voltage and the reactive power control loop bandwidths are selected at \(W_{b-\tau_{dc}}=W_{b-Q}=300\) rad\(/\)s, which is about five times lower than that of the current control loop.  
Figure 6: Converting a state feedback loop (**a**) to a single output feedback loop (**b**).
# A Heart Rate Variability-Based Paroxysmal Atrial Fibrillation Prediction System
## 4 Discussion
### Analysis of Misclassifications  
We attempted to explore the causes of misclassifications of our PAF prediction system. Firstly, hierarchical clustering was adopted to find subgroups in PAF as well as in non-PAF HRV sequences. All the HRV sequences (4414 non-PAF and 1671 PAF) in the three datasets adopted in this study were used to obtain representative subgroups. The four 32 \(\times\) 32 feature matrices for each sequence were flattened into a 4096 vector, and then the similarity among the sequences was measured by their Euclidean distances. The [PERSON]'s method was applied to perform linkages among the converted vectors. The clustering result of the PAF HRV sequences and that of non-PAF sequences are provided in Appendix A Figure A2. Using a distance of 3500 as a cutting threshold, the PAF and non-PAF HRV sequences were divided into nine and six subgroups, respectively. Secondly, we further separated the sequences that were correctly identified from those that were falsely identified in each subgroup, resulting in a total of 30 subgroups. Thirdly, the converted vectors in each subgroup were averaged so that all of the HRV sequences were represented by 30 4096 vectors. Finally, the 30 vectors were again clustered using the Euclidean distances among them and the [PERSON]'s linkage method. Figure 8 illustrates the clustering result. The symbols of TP, TN, FP, and FN in the figure denote true positive (PAF), true negative (non-PAF), false positive, and false negative, respectively. The number behind each group represents the group index, whereas the number in parentheses indicates the number of sequences in the corresponding subgroup. As shown in the figure, the false-negative HRV sequences were indeed apart from the true positive ones. Similar situations can be also observed among the true negative and false positive subgroups. Such phenomena suggest that the misclassified HRV sequences, either PAF or non-PAF, exhibit larger differences than the correctly identified ones and, hence, they are more likely to be misclassified. Such misclassifications may be inevitable, either due to large differences from their fellow sequences or due to measuring noise. By using shorter sequences and simpler features, we anticipate that our PAF prediction system can reduce misclassifications by integrating the classification results of neighboring HRV sequences.  
Figure 7: Prediction accuracy (Acc.), true positive percentage (TPP), and true negative percentage (TNP) in the (**a**) training, (**b**) validation, and (**c**) testing datasets after different percentages of noise were added to the HRV sequences therein.
## 4 Performance Analysis  
### Average Latency  
The total delay of task offloading includes the queuing delay, the service time at the MEC server, and transmission delay. The queuing delay is from task arrival to the start of processing. The transmission delay includes uplink and downlink. Therefore, we can obtain the total delay using Equation (13).  
\[T_{total}=T_{w,k}+T_{sv,k}+2T_{s} \tag{13}\]  
where \(T_{w,k}\) is the waiting time at the MEC server, \(T_{sv,k}\) is the service time of the MEC server, and \(T_{s}\) is the link transmission time.  
Assuming that the transmission process of the user sending requests and receiving results is always successful, the total average delay of users can be represented as  
\[E(T_{total})=\sum_{k\in K}p_{o,k}[E(T_{w,k}+T_{sv,k})+2T_{s}] \tag{14}\]  
where \(E(T_{w,k}+T_{sv,k})\) is an average of the sum of waiting time and service time. The expectation of waiting time can be obtained from the probability distribution function of the waiting delay, which is given in Equation (15).  
\[E(T_{w,k})=\int_{0}^{\infty}tf_{T_{w,k}}(t)dt=\int_{0}^{\infty}t\left(\sum_{n=c_{k} }^{c_{k}+r_{k}-1}q_{k,n}\frac{c_{k}u_{k}(c_{k}u_{k}t)^{n-c_{k}}}{(n-c_{k})!}e^{-c _{k}u_{k}t}\right)dt \tag{15}\]  
Service time follows an exponential distribution with a mean value \(1/u_{k}\); therefore, the expectation of service time can be given in Equation (16).  
\[E(T_{sw,k})=\frac{1}{u_{k}} \tag{16}\]  
Finally, we can obtain the total delay of task offloading \(E(T_{total})\) in Equation (17)  
\[E(T_{total})=\sum_{k\in K}p_{o,k}\left[\int_{0}^{\infty}t\left(\sum_{n=c_{k}}^ {c_{k}+r_{k}-1}q_{k,n}\frac{c_{k}u_{k}(c_{k}u_{k}t)^{n-c_{k}}}{(n-c_{k})!}e^{-c _{k}u_{k}t}\right)dt+\frac{1}{u_{k}}+2T_{s}\right] \tag{17}\]