esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Field Spectroscopy, Retrieval of Sun-Induced Fluorescence and Biophysical Properties  
TOC spectral radiances were collected under clear-sky conditions immediately before flux measurements at each collar [8; 37]. The sampling strategy was designed to minimize the differences in solar zenith angle (SZA) between measurements, confirmed by the ANOVA test, which reports non-significant differences in SZA between treatments in each campaign (\(p=0.43\), \(p=0.41\), \(p=0.33\), \(p=0.65\), \(p=0.99\), \(p=0.99\), and \(p=0.57\) for Campaigns 1-7, respectively). The ranges of SZA for the spectral measurements are reported in Table 1. Two portable spectrometers (HR4000, OceanOptics, USA) were used to estimate chlorophyll fluorescence at the O\({}_{2}\)A band (i.e., F\({}_{760}\),) and reflectance in the spectral range 400-1000 nm. The measurements protocol was the following: We first measured the incident solar irradiance by nadir observations of a leveled calibrated standard reflectance panel (Spectralon, LabSphere, USA). We then acquired five measurements of TOC spectral radiances from nadir at 110 cm above the targeted area using bare fiber optics of 25\({}^{\circ}\) of field of view (about 43 cm diameter at the ground, Figure S4). F\({}_{760}\) was estimated by exploiting the spectral fitting method [6]. The spectral interval used for F\({}_{760}\) was set to 759.00-767.76 nm. Albedo\({}_{400-900}\) was calculated from TOC spectral radiances as shown in Equation (7), assuming a Lambertian behavior of the reflected radiance.  
\[Albed_{0400-900} = \frac{\int_{400}^{000}L_{r}\times\pi}{\int_{400}^{400}E} \tag{7}\]  
where L\({}_{r}\) is the reflected radiance and E\({}_{400-900}\) is the Irradiance. fAPAR was estimated in three different ways: (i) fAPAR\({}_{\text{SCOPE}}\) was simulated by the process based SCOPE model [44]. (ii) fAPAR\({}_{\text{RENDVI}}\) was based on the established relationship between measured fAPAR and the red edge NDVI (RENDVI) found in maize, soybean and grasslands [45] (Equation (8)).  
\[fAPAR_{\text{RENDVI}} = 1.61\times\text{RENDVI}-0.03 \tag{8}\]  
where RENDVI is calculated as shown in Equation (9):  
\[\text{RENDVI} = \frac{(\text{R}_{\text{NIR}}-\text{ R}_{\text{RE}})}{(\text{R}_{ \text{NIR}}+\text{ R}_{\text{RE}})} \tag{9}\]  
where R\({}_{\text{NIR}}\) and R\({}_{\text{RE}}\) are reflectance factors in spectral bands 770-800 nm and 700-710 nm, respectively. (iii) APAR\({}_{\text{LifeMoreau1996}}\) was based on subtracting the integral (between 400 and 700 nm) of the incoming PAR (PAR\({}_{\text{inc}}\)) from the integral (between 400 and 700 nm) of the reflected PAR (PAR\({}_{\text{refl}}\)) measured by the spectrometers [7; 46] and then multiplying by the proportion of canopy absorption (RAPAR) [47] (Equation (10)).  
\[APAR_{\text{LifeMoreau1996}} = (\text{PAR}_{\text{inc}}-\text{PAR}_{\text{refl}})\times \text{RAPAR} \tag{10}\]  
where RAPAR is calculated as:  
\[RAPAR = 0.105-0.323\times\text{NDVI}+1.468\times\text{NDVI} \tag{11}\]  
The fAPAR formulations are quite consistent with each other (Figure S5), and therefore hereafter we use fAPAR\({}_{\text{RENDVI}}\).
## 3 Results and Discussion
### Volatile Aroma Compounds
#### 3.2.4 Higher Alcohols  
Wine major higher alcohols are formed in fermentation by yeast either from sugars or from amino acids by the Ehrlich mechanism [17]. They form a basis of wine aroma and, at a low concentration, contribute to its complexity and character, while at total levels above 350 mg/L can have a direct negative impact and also mask other, positive aromas. The importance of higher alcohols is not solely in their contribution to wine aroma as they are also precursors to particular odoriferous volatile esters.  
The highest concentration of the major wine higher alcohol, i.e., isoamyl alcohol was detected in the control _S. cerevisiae_ must, although not significantly different compared to the levels found in _P. kluyveri_ and _L. thermotolerans_ inoculated musts (Table 1, Figure S2). It was shown in previous studies that particular non-_Saccharomyces_ yeast, such as _P. kluyveri_[10] and _M. pulcherrina_[10; 36], produce lower concentrations of higher alcohols in sequential fermentation compared to pure _S. cerevisiae_ inoculation, although opposite results were also published for co-inoculation with _M. pulcherrina_ in red wine [37]. In this work, a lower concentration of isobutanol was recorded in _M. pulcherrina_ and the lowest in _T. delbrueckii_ must in relation to the other musts (Table 1). It was shown previously that the application of different commercial preparations, and therefore different strains of the same non-_Saccharomyces_ yeast, e.g., _T. delbrueckii_, in sequential fermentation can lead to contrasting outcomes regarding the content and composition of higher alcohols in relation to pure _S. cerevisiae_ inoculation [11; 38]. Besides the strain, the availability of yeast nutrients and suppressors in a given grape must matrix, as well as the genetically predetermined regulation system for the selection of nitrogen from various sources, certainly have a large effect [18].  
In contrast to other major higher alcohols in wine, 2-phenylethanol is a carrier of a pleasant odor reminiscent of roses. It is mainly a product of alcoholic fermentation, although a smaller part derives from grapes in both free and glycosidically bound form. The concentration found in the control must inoculated with _S. cerevisiae_ almost doubled those found in the musts of non-_Saccharomyces_ starters (Table 1, Figure S2). As in the case of the abovementioned other major fermentation alcohols, previous results regarding the effect of non-_Saccharomyces_ yeasts in sequential or co-inoculation with _S. cerevisiae_ on 2-phenylethanol concentration were also contrasting. While certain authors observed a decrease after the use of _P. kluyveri_ and _M. pulcherrima_[10], others noted an increase after inoculation with _T. delbrueckii_[11; 12; 13] and _L. thermotolerans_[13].
## 2 Background
### Overview of DoS and DDoS Attacks on CC
#### 2.2.1 DoS, DDoS Attacks on CC
CC significantly reduces the cost of monitoring and maintaining IT infrastructure. The control and management of CC resources are usually reliant on standard networking protocols, allowing administrators to manage and control a distributed IT infrastructure centrally. Unfortunately, using standard networking protocol also will enable adversaries to get unauthorized access if the security is lacking or weak. Attacks like DDoS are among the most common threat in private CCs, resulting in degradation or denial of services [22]. DDoS attacks against application-layer services are not new. The first well-known DDoS attack appears to have occurred in August 1999 against a higher educational institution in the United States. It has since been directed at other sectors, including CNN, eBay, Yahoo, and Amazon [9]. In 2009, a DDoS attack disrupted many popular online services, including Facebook, Live Journal, Twitter, and Amazon [8]. Circa 2014, there were more than 7000 DDoS attacks launched daily [23]. The first quarter of 2013 saw the average attack volume at about 50 Gbps, up 718 percent from the last quarter of 2012 [23; 24]. The largest DDoS attack recorded in history occurred at the end of 2016, involving a botnet called Mirai [25] that infected the servers of Dyn, a corporation dealing with a domain name system (DNS). In addition, the attack duration keeps increasing. For example, until the last quarter of 2020, no attack lasted more than 302 h. However, the longest attack in the first quarter of 2021 was 746 h (more than 31 days), then surpassed by a 776-h onslaught (more than 32 days) in the second quarter. One projection for the global market of DDoS protection systems [26] for 2022 predicts the value to expand from $3.3 billion in 2021 to $6.7 billion by 2026.
# A Low-Complexity Lossless Compression Method Based on a Code Table for Infrared Images
## 5 Results
### Proposed Method Compared with JPEG Series Algorithms  
On an experimental platform of 12 th Gen Intel(R) Core(TM) i7-12700H, a 20-core CPU at 2.30 GHz, and 16 GB of RAM, we test JPEG 2000, JPEG XL, JPEG XT, and the method proposed in this paper, and the results are shown in Figure 8 and Table 8. Figure 8a shows the compression ratio test results of the proposed method and JPEG series methods on 37 images, along with the theoretical compression ratio calculated based on the entropy of the difference image. Figure 8b presents the speed test results. Table 8 records the average values of the compression ratio and speed from Figure 8. It also includes the percentage change in the compression ratio of the proposed algorithm compared to both the JPEG series methods and the theoretical value.  
The method proposed in this paper achieves an average compression ratio of 3.3 for infrared line-scan images, which is 5% lower than the theoretical value. Compared to JPEG 2000, the proposed method incurs a 10% loss in compression efficiency but provides a 20-fold speed improvement, reaching an average of 210 MB/s. Its performance and speed both outperform JPEG XT, and it is approximately 8 times faster than JPEG XL.  
Figure 8: Speed and compression ratio of the proposed method and JPEG series. (**a**) Compression ratio. (**b**) Compression speed.  
Figure 7: Various experimental scenes (partial of 37).
# Compp-DDPG: Computation Offloading with Privacy Preservation in a Vehicular Edge Network
## 3 Model of VEN with Privacy Preservation
### Communication Model  
Two wireless communication modes, vehicle-to-vehicle and vehicle-to-infrastructure (V2V and V2I), are involved in the proposed VEN. The V2V communication mode is adopted when the ICV-\(i\) decides to offload the vehicular task \(T_{i}\) to the surrounding ICV within one hop. The available ICV, which has spare computational resource VEC-\(k\), processes the computational task and returns the calculation results to ICV-\(i\). The transmission rate \(r_{ik/ki}\) between ICV-\(i\) and VEC-\(k\) is  
\[r_{ik/ki}=B_{ik/ki}\log_{2}\bigg{[}1+\frac{\rho_{0}P_{i}/P_{k}}{\sigma^{2}[d_{ ik/ki}]^{c}}\bigg{]}. \tag{3}\]  
The two-way communication delay between ICV-\(i\) and VEC-\(k\) is obtained as  
\[t_{comm}^{VEC}=\frac{\lambda_{i}D_{i}^{in}}{r_{ik}}+\frac{\lambda_{i}D_{i}^{out }}{r_{ki}}. \tag{4}\]  
After offloading the vehicular task to the MEC server, the communication process includes the communication between the RSUs and the MEC server, and between the ICVs and the RSUs. In general, the optical fiber transmission time between the RSUs and the MEC server is negligible [17]. In this work, the ICVs and the RSUs communicate in the mode which is assumed to use the protocol of IEEE 802.11p. According to [34], the uplink and downlink transmission rate between ICV-\(i\) and RSU-\(j\) is  
\[r_{ij}^{ULL/DL}=\frac{\lambda_{i}D_{i}^{in}/D_{i}^{out}N_{j}\tau_{ij}\varrho}{ \big{(}1-\tau_{ij}\big{)}^{N_{j}}\sigma+T_{ij}^{success}N_{j}\tau_{ij}\varrho+1 -\big{(}1-\tau_{ij}\big{)}^{N_{j}}-N_{j}\tau_{ij}\varrho(\text{RTS}+\text{ AIFS}+\delta)}, \tag{5}\]  
where \(N_{j}\) is the number of ICVs offloading the vehicular task to the MEC server through the RSU-\(j\). \(\tau_{ij}\) is the connection probability between ICV-\(i\) and RSU-\(j\) in the random time slot. \(\sigma\) is a time slot duration, \(\varrho=\big{(}1-\tau_{ij}\big{)}^{N_{j}-1}\), and \(\delta\) is the propagation delay. \(T_{ij}^{success}\) is the successful transmission period between ICV-\(i\) and RSU-\(j\), which is expressed as  
\[T_{ij}^{success}=\Phi+\frac{\lambda_{i}D_{i}^{in}/D_{i}^{out}}{\omega_{i}\log \big{(}1+P_{i}h_{ij}\big{)}}, \tag{6}\]  
where \(\Phi\) is related to the media access control protocol. \(\omega_{j}\) is the RSU-\(j\)'s bandwidth, \(P_{i}\) stands for the ICV-\(i\)'s transmission power, and \(h_{ij}\) denotes the channel gain between ICV-\(i\) and RSU-\(j\).  
The uplink and downlink transmission time between ICV-\(i\) and RSU-\(j\) is calculated as  
\[t_{UL/DL}^{MEC}=\frac{\lambda_{i}D_{i}^{in}/D_{i}^{out}}{r_{ij}^{ULL/DL}}. \tag{7}\]  
The two-way communication delay between ICV-\(i\) and RSU-\(j\) is given by  
\[t_{comm}^{MEC}=t_{UL}^{MEC}+t_{DL}^{MEC}. \tag{8}\]
# Data-Driven Fault Detection and Diagnosis: Challenges and Opportunities in Real-World Scenarios
## 5 Results
### Dataset 2: Case Packer  
In this case, the main goal is to detect a known fault as soon as possible, without any training phase. Faults have been separately considered since they cannot occur in sequence. Hence, four scenarios have been simulated, in which the dataset corresponding to one fault is appended after the nominal behavior. In this case, two distinct feature sets are considered: the first one, which includes the peak, the mean, the root mean square, and the crest factor, has been obtained through the Pearson correlation analysis. The second feature set includes the principal components (PCs) obtained through the PCA.  
Results of the AAD + ADP algorithm applied to the two sets of features, i.e., features selected through the Pearson correlation analysis and the features extracted through the PCA, are shown in Figure 9a and Figure 9b, respectively. This figure depicts the points assigned to different clusters in different colors and anomalies with red crosses. As can be seen in Figure 9b, the extracted PCs are not able to reveal the health condition of the component. In contrast, the performances of the algorithm applied to the features selected through the Pearson correlation analysis are summarized in Table 5. In all cases, a false positive (red cross) is detected during the nominal behavior. Fault 1, Fault 2, and Fault 3 are detected after five cycles (0.75 s) of their occurrence, and a novel cluster is created when the anomaly corresponding to the fault is detected. Instead, when Fault 4 occurs, an anomaly is detected. However, the following points continue to be assigned to the cluster corresponding to the nominal behavior.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Condition**} & **Detected Setting** & \multirow{2}{*}{**Latency (s)**} & **N Points Assigned to the** & **False** \\  & **Change (Sample)** & & **Corresponding Cluster** & **Alarms** \\ \hline Setting 1 & - & & 4982 & \\ Setting 2 & 4.984 & 15 & 11,048 & \\ Setting 3 & 16.032 & 10 & 6187 & \\ Setting 4 & - & - & - & \\  & & & 20,681 & \\ Motor Fault & 20,682 & \(-\)1536 & 157 & 488 \\  & 20,839 & \(-\)1379 & 1380 & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performances of clustering-based with pretraining approach (electric motor).  
Figure 8: Results of the AAD + ADP algorithm (electric motor) for (**a**) operating condition recognition, and (**b**) fault detection.
## 7 Approaches to Prevent and Manage Infections Caused by Biofilms
### Therapeutic Strategies
#### 7.1.3 DNABII Proteins  
DNABII proteins, such as integration host factor (IHF) and histone-like protein HU, are essential for creating and preserving biofilms. These proteins attach to eDNA in the biofilm matrix, enhancing its stability and fortifying its structure [112]. Given their critical role in maintaining biofilm integrity, DNABII proteins provide a promising target for therapeutic approaches designed to disrupt biofilms and improve therapies for infections linked with biofilms.  
DNABII proteins attach to eDNA inside the EPS matrix, inducing structural bending and organization that reinforce the biofilm's three-dimensional architecture [112]. This stability improves the biofilm's resistance to physical disturbances, immunological responses, and antimicrobial substances. The ubiquitous existence of DNABII proteins across bacterial species renders them a universal and broad-spectrum target for biofilm removal [257].  
Anti-DNABII antibodies provide a viable therapeutic strategy for breaking the connection between DNABII proteins and eDNA, weakening the biofilm matrix. These antibodies specifically target the DNA-binding domain of DNABII proteins, compromising the structural integrity of the biofilm and rendering bacterial cells susceptible to immune responses and antimicrobial treatments [258; 259]. For instance, [PERSON] et al. revealed that antibody ies targeting IHF impeded biofilm development on sinus implants, underscoring their prospective use in addressing biofilm-associated infections in clinical environments [260]. [PERSON] et al. similarly observed that anti-DNABII antibodies interfered with biofilms produced by many human pathogens, demonstrating their extensive potential in addressing biofilm-related diseases [261].
# SCADA System Testbed for Cybersecurity Research Using Machine Learning Approach
## 2 Background
### The SCADA Communication Protocol  
There are several communication protocols developed for use in SCADA systems. These protocols define the standard message format for all inter-device communications in the network. One popular protocol, which is widely used in SCADA system environments, is Modbus protocol [5]. Modbus is an application-layer messaging protocol that provides the client/server communications between devices connected to an Ethernet network and offers services specified by function codes. The function codes tell the server what action to take. For example, a client can read the status of the discrete outputs or the values of digital inputs from the PLC; or it can read/write the data contents of a group of registers inside the PLC. Figure 2 illustrates an example of Modbus client/server communication.  
The Modbus register address type consists of four data reference types [5, 6] which are summarized in Table 1. The \"xxxx\" following a leading digit represents a four-digit address location in the user data memory.  
Figure 1: Industrial control systems (ICS) reference model [4].
## 5 Conclusions  
The study demonstrates the importance of combining natural gamma-ray spectrometry and ASTER data in the early stages of mineral exploration. This combination was applied to target areas with high mining potential in the Kelaat M'Gouna inlier. The results obtained allowed detailed mapping of hydrothermal alteration zones related to mineralization. Maps of Potassium (K in %), Uranium (eU in ppm), Thorium (eTh in ppm) and ratios of K/eTh and K/eU were generated to delineate the high concentrations of radioactive elements related to the altered zones, particularly in Potassium.  
Band ratios extracted from the ASTER image were calculated to visualize the spatial distribution of specific minerals in the alteration zones. Clay, phyllite, propylitic minerals and iron oxides were mapped in some lithologies that host several mineral occurrences.  
The mineral prospectivity maps generated by the fuzzy logic modelling allowed us to locate the alteration zones. Seven anomalous zones were then distinguished. The geological data showed that these zones are located in the contact zones between the granitic massits, especially the Wawitcht, Isk n'Alla and Azlag granites and their host rocks formed by the Ediacaran volcano-sedimentary rocks of the Ouarzazate Group. In addition, most of these zones have been mapped in rocks that host the prospects and, mining showings already indicated, notably those of gold. Other anomalous zones have been mapped in fault zones, mainly in NE-SW and N-S trends, such as the Sidi Flah fault zone and the Tagmout graben zone. To this end, it is recommended to carry out a detailed structural study in conjunction  
Figure 7: Combination of mineral prospectivity maps.  
with geophysics to locate, delineate, and follow the deep rooting of the metallic bodies and tectonic structures that may plug the mineralization in the study area.  
Conceptualization, Y.M., A.A. ([PERSON]), A.M. ([PERSON]) and M.O.; Data curation, Y.M. and M.O.; Formal analysis, Y.M., A.A.([PERSON]) and M.O.; Funding acquisition, Y.M. and A.M. ([PERSON]); Investigation, Y.M.; Methodology, Y.M., M.O., B.D. and Y.E.-t.; Project administration, Y.M. and A.A.([PERSON]); Resources, Y.M., G.R., S.L. and A.M. ([PERSON]); Software, Y.M., M.O. and L.A.; Supervision, A.A. ([PERSON]) and A.M. ([PERSON]); Validation, Y.M., A.A. ([PERSON]), A.M. ([PERSON]), A.A. ([PERSON]), A.A. ([PERSON]), A.A. ([PERSON]), M.B., G.R., S.L., A.M. ([PERSON]), M.O. and B.D.; Visualization, Y.M., A.A. ([PERSON]), A.M. ([PERSON]), A.M. ([PERSON]) and M.O.; Writing--original draft, Y.M. and M.O.; Writing--review and editing, Y.M., A.A. ([PERSON]), A.M. ([PERSON]), M.O., B.D., L.A., Y.E.-t., A.A. ([PERSON]), M.B., G.R., S.L. and A.M. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
## 5 Results Analysis
### Simulation Results for Scenarios 1-5
#### 5.2.4 Resilience Index  
Resilience index (\(\mathrm{R(t_{h})}=\frac{\mathrm{f_{0}^{h}\,F(t)dt}}{\mathrm{t_{h}-t_{0}}}\)) tries to represent the resilience through a parameter, the value of which varies between 0 and 1. It is widely used in current literature for resilience analyses of transport infrastructures. A high resilience index indicates a low resilience loss (i.e., a resilience index equal to one corresponds to a zero-resilience loss).  
Figure 10a,b shows the resilience index computed for the scenarios investigated without and with the use of the alternative itinerary. In particular, it can be noted how the resilience index decreases with the duration of the partial or complete closure of the north tube (i.e., from 1 h to 2 h, and 3 h, respectively). Higher resilience indexes were found with the partial closure of the tube (i.e., Scenario 1) in contrast with the complete one. In the event of the complete closure of the north tube, the resilience index is higher when the traffic by-pass is opened within a time of 10 min from the start of the incident (i.e., Scenario 2a against Scenarios 2b, and 2c). By comparing Figure 10a,b, it can also be noted how when the alternative itinerary is activated for HGVs only, both in the case of the partial (i.e., Scenario 3) and complete closures (i.e., Scenarios 4a, 4b, and 4c) of the north tube, higher resilience indexes are found in contrast with those of the corresponding scenarios (i.e., respectively 1, 2a, 2b, and 2c) for passenger cars only. Lower resilience indexes were found when the north tube is completely closed and all the traffic flows (i.e., both passenger cars and HGVs) are redirected to the aforementioned alternative itinerary. In other words, the results of the resilience index confirm our previous comments on the resilience loss in all the cases investigated.