esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Sesquiterpenals and Their Bioactivities
### Polycyclic Sesquiterpenols  
Polycyclic sesquiterpenols encompass compounds with three or more carbocyclic structures, with representative examples including patchoulol, \(\alpha\)-santalol, cedrol, and curcuminol (Table 1, Figure 1). Patchoulol, a tricyclic sesquiterpenol, constitutes the principal component of patchouli essential oil. It exists as a white to off-white solid under ambient conditions. This compound is primarily derived from _Pogostemon callin_, where its essential oil is obtained through steam distillation of the leaves, stems, and roots [44]. Beyond its characteristic persistent woody aroma, which facilitates its extensive use in cosmetics and household products, patchoulol demonstrates therapeutic potential in Alzheimer's disease prevention, anti-anginal activity [27], arrhythmia suppression [27], and antihypertensive effects [27]. Alpha-Santalol, a tricyclic sesquiterpenol primarily derived from the essential oils of _Santalum album_[45], has been shown to possess broad-spectrum antimicrobial properties. It effectively inhibits the growth of several notable pathogens [29], including _Helicobacter pylori_, _Candida albicans_, and _Escherichia coli_. Given its multifunctional bioactivities, including antibacterial prophylaxis [29], virucidal activity [31], antioxidant properties [30], and antitumor potential [28], \(\alpha\)-santalol is positioned as a promising pharmaceutical candidate. Cedrol, a tricyclic sesquiterpenol presenting as white crystals with a mild cedarwood aroma, finds extensive application in woody and oriental fragrance formulations, particularly in soap and detergent perfumery [46]. Pharmacological studies reveal its anti-inflammatory [33], analgesic [33], cardioprotective [32], antitumor [34], hair growth-promoting, and antimalarial properties [46]. Curcumin, an orange-yellow crystalline tricyclic sesquiterpenol isolated from _Curcuna longa_[35], is a bitter-tasting compound that demonstrates a wide range of bioactivities, including anticancer, antimicrobial, antifungal, antiviral, and anti-inflammatory effects [47].
## 2 Materials and Methods  
### Data Collection: Household Survey  
The household survey applied in this study was developed by the international SysOrg project consortium [29] to collect comprehensive data on three key sustainability perspectives addressed in the project, which are diet, organic food consumption, and food waste in different SysOrg case territories. The present study focuses on the territory of Warsaw and takes into consideration a range of HHS aspects, including socio-demographic information, the frequency of food consumption (including organic food), the proportion of organic foods in participants' diets, shopping behaviors, perceptions of sustainability and organic products, and food choice determinants. Certain questions were derived from previously validated questionnaires [30; 31; 32]. The selected questions from the survey are reported in the Supplementary Materials (Table S1). The HHS, administered using Lime Survey([[https://www.limesurvey.org/](https://www.limesurvey.org/)]([https://www.limesurvey.org/](https://www.limesurvey.org/))) was distributed through the river sampling methodology (a non-probabilistic approach to recruiting respondents online) [33] by diverse social media channels (e.g., Facebook, Instagram) and online sites (e.g., Municipality of Warsaw). To reach respondents from 18 districts of Warsaw, the survey was circulated through the online communication channels of different neighborhoods of the city. Altogether, 484 adults (>18 years old) completed the survey, and after data cleaning (a process aiming to develop one standardized dataset for the results of the SysOrg household survey for all 5 case study territories excluding respondents speeding and with incomplete answers), the responses of 449 Warsaw residents were included in further analyses. The characteristics of the included respondents are presented in Appendix A. Previous research has displayed additional characteristics of the study group [29].  
Data collection was conducted in compliance with the European Commission's General Data Protection Regulation (679/2016), and the study followed the ethical guidelines set forth by the Declaration of Helsinki [34]. All participants provided informed consent for the use of their responses in the anonymously completed HHS. The research protocol received approval from the Scientific Research Ethics Committee at the Institute of HumanNutrition Sciences of the Warsaw University of Life Sciences (approval number: 50/2021; date: 15 November 2021).  
At the outset of the survey, participants were instructed to ensure that it was completed by the adult individual primarily responsible for food preparation and purchasing within the household. Analytical methods were employed to explore associations between the self-reported share of organic food in respondents' diets (0%, 1-10%, 11-25%, 26-50%, 51-75%, 76-99%, and 100%) and the responses to questions pertinent to healthy and sustainable behaviors. Considering the distribution of the surveyed population in terms of the declared share of organic food in the diet, some results were presented in four groups, namely the 0%, 1-10%, 11-25%, and 26-100% groups.
# Power Control and Link Selection for Wireless Relay Networks with Hybrid Energy Sources
## 3 Optimal Control for Expected Total Rewards
### MDP Model for Expected Total Rewards
#### 3.2.2 Reward Function  
In an MDP model, the reward function is defined as \(r(j,a_{i})\). It indicates that the system gets the reward with action \(a_{i}\) at state j [27]. This is denoted \(o_{i}\in O,i\in\mathbb{N}\) as the rule for selecting relay power in slot i. Thus, the rules over N slots can be expressed as \(\pi\)\(=(o_{1},o_{2},o_{3},\cdots o_{N})\), and the set of all possible rules is denoted as \(\prod\). Given the initial state k and strategy \(\pi\in\prod_{m}^{O}\), the expected total rewards can be also written as:  
\[V_{N}(\pi,s_{1})=\sum_{i=1}^{N}\sum_{a_{i}\in A_{i},j\in S}P_{\pi}[s_{i}=j, \Delta_{i}=a_{i}]s_{1}=k]r(j,a_{i}), \tag{28}\]  
where \(s_{i}\) and \(\Delta_{i}\) are the states of the relay system and the selected action in the slot i, respectively. \(P_{\pi}[s_{i}=j,\Delta_{i}=a_{i}]s_{1}=k]\) is the conditional probability of using strategy \(\pi\in\prod_{m}^{O}\), starting from state k, selecting action \(a_{i}\), and moving to state j at slot \(i\). Our aim is to find the optimal action selection scheme as \(\pi^{*}\)\(=(o_{1}^{*},o_{2}^{*},\cdots,o_{N}^{*})\), which makes \(V_{N}(\pi^{*},s_{1})\) the maximum value.
# Artificial Intelligence Applications in Smart Healthcare: A Survey
## 5 Challenges and Existing Solutions
### Security of AI Models
#### 5.6.2 Existing Solutions  
In the healthcare sector, effectively detecting and preventing sensor spoofing attacks is essential for maintaining data integrity. For instance, remote monitoring systems, such as heart rate monitors and glucose meters, must guard against data falsification. By integrating sensor data with electronic health records, healthcare systems can use anomaly detection algorithms to spot potential spoofed data. For example, machine learning models can track unusual fluctuations in heart rate data to identify and mitigate spoofing attempts, thus enhancing diagnostic accuracy [132]. Additionally, data fusion techniques play a critical role. For example, Dexcom's continuous glucose monitoring system merges real-time glucose levels with historical medical records and lab results to detect anomalies and prevent the influence of spoofed data on diabetes management decisions. Similarly, integrating various sensor data, like ECG and SpO2, with clinical data can improve the detection of spoofed data and boost the overall reliability of healthcare systems [132].  
Addressing image scaling attacks is crucial in medical imaging for maintaining diagnostic accuracy. The choice of scaling algorithms significantly impacts image quality. Hospitals can use robust algorithms, such as area scaling, to ensure high-quality images during scaling. For example, in automated breast cancer image analysis, this approach helps recover manipulated data, ensuring accurate diagnoses [133]. Additionally, median filters are valuable for repairing damaged medical images. In scenarios where CT or MRI images are affected by scaling or compression, median filters can effectively remove noise and restore original image details. For instance, applying median filters to chest CT scans can correct artifacts caused by processing errors, ensuring accurate diagnostic information [133]. This method is widely utilized in image analysis software to enhance image quality and diagnostic precision.  
In healthcare, defending against adversarial attacks, including poisoning and evasion attacks, is critical. Poisoning attacks may introduce malicious samples into training data, compromising model performance. Techniques such as adversarial retraining and input reconstruction can improve model resilience. For example, cleaning and re-labeling training data helps mitigate the impact of malicious samples [134]. For evasion attacks, generative adversarial networks (GANs) can bolster the security of predictive models. In breast cancer risk prediction, GANs generate adversarial examples to simulate potential attacks, enabling models to detect and counter malicious inputs. These examples enhance model robustness against real-world evasion attacks. Furthermore, GANs can also produce high-quality medical images to train models for recognizing various lesions, thereby improving their ability to withstand malicious inputs [134].  
Figure 5: The overview of the security of AI models.
## 3 Results and Discussion
### Packing Height  
The height of the packings determines gas-liquid mass transfer area and residence time of fuel gas in the packings, which has an important effect on the removal of pollutants. Figure 6 shows the desulfurization and denitration efficiency of the system at various packing heights. As the packing height increases from 40 cm to 80 cm, the denitration efficiency increases rapidly from 58.4% to 70.8% and the desulfurization efficiency increases from 99.1% to 100%. When the packing height is in the range of 80-120 cm, the denitration efficiency gradually increases from 70.8% to 71.8%.  
With the increases in packing height, the gas-liquid mass transfer area and mass transfer time are extended. The desulfurization and denitration efficiency increases with the increase in packing height. For NO\({}_{x}\) removal, when the filler height exceeds 80 cm, the remaining NO\({}_{x}\) is mainly NO; however, NO has low solubility in water and does not react with NaOH; therefore, the denitration efficiency increases slowly with the increases in packing height in the range of 80-120 cm.
## 3 Algorithm Description
### Sending and Reading phase  
* The reader initiates tag interrogation using the data channel if there is an available TS in its table (IsEmpty(TSEnable) = 0). Otherwise, it selects an available TS from the list and stores it in TSEnable.
* Subsequently, the reader broadcasts a REQUST1 message through the control channel to identify the nearest neighbor potentially affected by RTI. This serves as an alert to neighbors to avoid using the reader's TS.
* After a delay period of Tmin, if the reader receives a RESPONSE1 message from the targeted neighbors, it retains the first received message. The reader then activates the sender of the RESPONSE1 message through a TS_select message, enabling them to select their TS and initiate the subsequent discovery process.
* If no response is received within Tmin, the reader broadcasts a new REQUEST2 message to discover other readers, utilizing existing neighbors as relays.
* In the case of receiving a RESPONSE1 message, the reader retains the first message and sends a TS_select message to the new neighbor, initiating the next phase of the process.
# 3D Monitoring of Buildings Using TerraSAR-X InSAR, DInSAR and PolSAR Capacities
## 3 Measuring the Height by Interferometry
### Height Estimation Results for Two-Images-Repeat-Pass Interferometry  
The height estimates are presented in Figure 4. This figure demonstrates that some estimated heights have very unrealistic values: the height of the Eiffel Tower has been estimated to more than one kilometer and negative heights are sometimes obtained for all the buildings. These unrealistic results show that the phase can not always be modeled alone by Equation (1), even if some previous studies have used it for measuring the exact height of the Eiffel Tower using only one repeat-pass interferometric pair [5; 6].  
After finer analysis of the results, these aberrant values appear are found for absolute values of \(k_{z}\) below 0.2, when the baseline is smaller than 170 m and the phase difference is more sensible to deformation. Therefore, it can be assumed that the erroneous estimates come from the very small deformations that we have neglected in Equation (1). These very small deformations, of the order of millimeters, can exist on most structures and impact the repeat-pass interferometric phase, as already put forward for the Mirabeau bridge in [5]. This hypothesis is analyzed in Section 4 where the impact of the atmosphere on the phase will also be discussed.  
This study shows nevertheless that an estimation of heights from a single repeat-pass couple of images can lead to false results. It is therefore necessary to use a more robust method for the elevation estimation from repeat-pass acquisitions as presented in the next section.
## 2 Results and Discussion
### Optimization of Extractive Alkylation Conditions
#### 2.1.2 Sample pH, Sample Volume, PTC Concentration  
Optimal values for pH, sample volume, and PTC concentration were evaluated concurrently in a multifactor fractional factor model (center composite face, CCF), and results are shown for each parameter in Figure 2. LOD was significantly affected by pH (\(p<0.01\) for all thiols), with the lowest LOD values achieved under the most alkaline conditions (pH = 12). As expected, LODs also decreased with increasing wine volume from 10 to 40 mL (\(p<0.01\) for 4-MMP and 3-MH; \(p<0.1\) for 3-MHA). We also determined that signal would continue to scale up proportionally when 160 mL of wine was used instead of 40 mL (data not shown). For convenience, the smaller volume of 40 mL was chosen for the optimized protocol, but improved LOD are attainable by using greater sample volumes. Significant interaction terms (\(p<0.05\)) were observed for pH \(\times\) volume, pH \(\times\) catalyst and catalyst \(\times\) volume, although this information was not further used for method optimization. Surprisingly, the presence of the PTC (18-crown-6 ether) did not improve LODs for any of the thiols (Figure 2). It is not clear why the reaction proceeded efficiently even without a PTC, but as a result no PTC was used in the optimized protocol. [PERSON] _et al_. have previously reported an improvement in PFBBr derivatization following methoximation of 4-MMP [10]; however, because we observed acceptable sensitivity for 4-MMP without this step, we did not explore methoximation.
## 3 Materials and Methods
### Data Pre-Processing  
Both of our own online EEG datasets representing SSMVEP and MI signals were recorded at a sampling frequency of 250 Hz. Three filters were then applied on the datasets to eliminate the impact of non-physiological artifacts [28]. In this case, a 0.5 Hz to 60 Hz band-pass filter was first applied on the raw SSMVEP and MI datasets to remove the effect of noise, followed by a 50 Hz notch filter utilized to minimize the effect of line noise [29]. Lastly, to remove the effect of noise emerging from the electrodes positioned on the surface of the scalp, a common average reference (CAR) filter was applied.  
The BCI competition IV-a dataset was pre-filtered using a band-pass filter at a cut-off frequency between 0.5 Hz and 100 Hz to reduce the impact of background noise, while the effect of line noise was reduced by applying a notch filter on the EEG signals at a cut-off frequency of 50 Hz [30; 31].
# New Displacement Method for Free Embedded Cantilever Walls in Sand
### Application of the Formula to the Laboratory Test by [PERSON] and [PERSON] [23]  
[PERSON] and [PERSON] [23] conducted an experimental study using a test tank with dimensions of 1.22 m in length, 0.33 m in width, and 0.47 m in depth. The objective of the study was to investigate the displacement and pressure distribution on a cantilever embedded wall, as depicted in Figure 12. The embedded wall, made of steel, had a thickness of 0.04 m and a width of 0.33 m. It was embedded 0.35 m into dense sand with a unit weight of 15.7 kN/m\({}^{3}\) and an internal friction angle of 47\({}^{\circ}\). To simulate the weight of the sand on the retained side, an equivalent surcharge load was applied. Horizontal and vertical point loads were also applied at 1/3 of the equivalent wall height H to simulate lateral earth pressure and soil-wall friction. Pressure distributions along the embedded part and wall displacements were measured. The simulated wall heights for the test were set at 0.83 m, 1 m, and 1.17 m by adjusting the surcharge and point loads.  
Figure 11: The plots of wall displacements from the site test ([PERSON] and [PERSON] [22]) and the formula (this study).  
Figure 12: The general setup of the laboratory test by [PERSON] and [PERSON] [23].  
The wall displacement formula corresponding to a wall height of \"1 m\" is applicable to the test conducted by [PERSON] and [PERSON] [23]. The unit weight (\(\gamma\)) and internal soil friction (\(\varphi\)) values were set to the same values reported by [PERSON] and [PERSON] [23] (Table 6). Since there was no additional surcharge load, the \"L\" value was set to zero. The soil modulus value (E) was not reported in the reference study; hence, it was assumed to be between 5 and 10 MPa. This range is reasonable considering that the E value increases with confining stress, and the total simulated sand depth does not exceed 1.5 m.  
Figure 13 illustrates the rotation of the wall relative to the normalized embedment depth. The values provided by the formula align well with those obtained from the test reported by [PERSON] and [PERSON] [23], indicating agreement between the two sources.
# Routing Attacks Detection in 6 LoWPAN-Based Internet of Things
## 2 Background
### Routing Protocol for Low-Power and Lossy Networks (RPL)
#### 2.1.2 Selective Forwarding Attack  
A Selective Forwarding Attack is a security threat wherein an attacker manipulates network traffic by selectively forwarding or dropping packets. The attacker gains control over one or more nodes in the network and selectively forwards packets to some nodes while dropping or delaying packets to others. This disrupts the communication between the nodes and can compromise the security of the network. The attacker can selectively drop packets containing critical information, leading to data loss and the disruption of the communication between nodes. This type of attack can be particularly harmful in WSNs and IoT networks where nodes have limited resources and rely on efficient and reliable communication. To ensure network security and reliability, effective defense mechanisms must be developed to detect and prevent selective forwarding attacks.
## 2 Materials and Methods
### Network Embeddings  
Network embeddings allow representing nodes in a graph in the form of low-dimensional representation (embeddings) to maintain the relationship of nodes [29; 30; 31]. In this paper, we first compute the network-based distributional thesaurus (AmDT) and later compute the network embeddings from the DT using DeepWalk [32] and Role2 Vec [33] algorithms. These two state-of-the-art network embedding algorithms have been selected for this study as they belong to different categories as explained below.  
* **DeepWalk**: The latent node embeddings produced by DeepWalk [32] encodes the social representations, like neighborhood similarity and community membership of graph vertices by modeling a stream of truncated random walks.
* **Role2 Vec**: The Role2 Vec [33] framework introduces the flexible notion of attributed random walks. This provides a basis to generalize the traditional methods, which rely on random walks, to transfer to new nodes and graphs. This is achieved by learning a mapping function between a vertex attribute vector and a role, represented by vertex connectivity patterns, such that two vertices belong to the same role if they are structurally similar or equivalent. The embeddings have been computed using the karatectub [34] Python library. The network embeddings are trained using the hyperparameter configuration of the package shown in Table 2.
# A Hierarchical Security Event Correlation Model for Real-Time Threat Detection and Response
## 1 Introduction  
[PERSON] [1] defines alert correlation as _\"...the interpretation, combination and analysis of information from all available sources about target system activity for purposes of intrusion detection and response.\"_ The goal of correlation is to reduce the number of events by aggregating and fusing those events that are related in some way. Additionally, through the correlation, it is possible to discover multistep and complex attacks whose information is carried in separate events. These events may also be generated by separate devices at various times and locations. From an intrusion detection perspective, correlation can be performed on either events or alerts. Alerts and events are related but fundamentally different concepts. The event is the record of what happened. The alert is a message that is raised when an interesting event has been encountered. The interestingness of an event depends on the application domain. From the Intrusion Detection Message Exchange Format (IDMEF) [2] specification, an alert is a message that is generated by a tool when it encounters an event that it was configured to look out for. Most of the active research is around alert correlation rather than event correlation. Event correlation is carried out before an IDS makes a detection decision. Alert correlation is performed outside the IDS after alerts have already been raised, by a third-party system, tool, or application. This paper deals with thecorrelation of the actual events before an analysis is performed by an IDS. We hypothesize that since the output of the correlation is a reduced data set due to aggregation, the input to the correlation component of an IDS is few correlated events. Consequently, the number of alerts should also be reduced.
## 5 Trace Sampling Based on Type, Duration, and Errors
### Sampling by Trace Type

Table 1: The number of traces before and after the sampling. Figure 3: The sampling of traces of Figure 2 for different values of the parameter \(\alpha\). The correct value of \(\alpha\) that will satisfy a user requirement could be estimated by computing the \(G(\alpha)\) values. Figure 4 explains the procedure. Let us assume that the required sampling rate is 10%, meaning that we want to store around that percentage of traces. \(\alpha=0.044\) provides a sampling rate \(r=0.099\). This algorithm should be applied if trace types carry important information for an application, and both rare and frequent types should be preserved for further analysis. Mathematically, it means no more than, say, 50 trace types with diverse frequencies. Diversity can be inspected via the Gini index or entropy. If those measures are close to zero, the sampling will try to maximize them and store equal portions from all types.