esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 System Architecture, Spectrum Sharing Technique, and Interference Management
### Dedicated Access  
This access method can be interpreted as the traditional spectrum allocation method to MNOs where each MNO has the sole right to use the allocated spectrum exclusively without facing any interference issues from other MNOs. Such dedicated access to spectrum guarantees the QoS. However, an MNO with dedicated access can usually operate only at its allocated spectrum bandwidth, which results in facing the scarcity of spectrum to serve all its users, particularly, in the peak hours. Moreover, since the spectrum is allocated to a dedicated MNO, and no reuse of the same spectrum can be made to other MNOs, there is a high possibility of wasting spectrum resources during the light load demand. Furthermore, because of having an exclusive right to the allocated spectrum, the license fees of the dedicated spectrum are also very high. The dedicated access method can be realized using the eICIC technique in small cells deployed in 3D buildings as follows.  
In dedicated access, we assume that the whole spectrum of an MNO, i.e., MNO 1, is reused to operate small cells deployed in each building simultaneously with its macro UEs. We employ eICIC techniques to avoid the co-channel interference due to the presence of macro UEs within any building with small cells and their UEs such that any in-building macro UEs can be scheduled during ABSs and in-building small cell UEs can be scheduled during non-ABSs of every APP. An optimal value of ABSs can be derived based on the ratio of the rate of in-building macro UEs to the rate of in-building small cell UEs. Since the same spectrum of an MNO is reused to in-building small cells, the dedicated access can be regarded as intra-operator spectrum sharing technique.
## 4 Results
### Case Study: Itoiz Reservoir--A Transect Below 300 m  
Itoiz Reservoir is of specific interest to evaluate the performance of the FFSAR processing chain over short transects because, even though it has the narrowest reservoir transect among the target collection, we obtained an accuracy in terms of MAD around 4 cm.  
Figure 6 shows the WL\({}_{a}\) time series from Itoiz Reservoir, with the in situ values during the study period represented by a dashed line, and the position and width of the satellite's window depicted as a gray rectangle. The reservoir historically has a WL\({}_{t}\) range of 52.2 m, and thus its full WL\({}_{t}\) variation cannot be entirely monitored by S6's window, which has a width of 48.55 m over the target. Furthermore, since the window is not accurately positioned in the middle of the reservoir's historical WL\({}_{t}\) range, it led to a loss of 23 out of 70 passes during the study period. The historical average WL\({}_{t}\) of the reservoir is 568 m, but the satellite's window is positioned at 586 m.  
Nevertheless, the WL\({}_{a}\) time series obtained by the previously described FFSAR algorithm is in excellent agreement with the values from the in situ station, as shown in Figure 12 (left panel). In fact, the error measured by all the presented statistical indicators is reduced by one order of magnitude compared to the WL\({}_{a}\) time series obtained from operational L2 products (Table 2).  
Figure 12 (right panel) shows that the obtained WL\({}_{a}\) error does not depend on the in situ WL\({}_{t}\) of the reservoir, as the fitted slope is nearly zero. However, we could not analyze this dependence during the lowest WL\({}_{t}\) season of the reservoir due to S6's window position.  
Figure 11: Water level error (WL\({}_{t}\)E) of the FFSAR water level derived from each waveform measured over the water mask of Yesa Reservoir during the 2-year study period. Black pixels indicate a WL\({}_{t}\)E equal to or larger than 100 cm. Only satellite passes where the satellite range window was well positioned have been included.  
Figure 12: Accuracy of FFSAR water levels for each pass over the Itoiz Reservoir. Left panel: Comparison between satellite-derived water level and in situ water level, with a slope of 0.99977 \(\pm\) 0.00087, \(R^{2}=0.99997\). Right panel: Satellite water level error dependence on the measured water level, slope \(=-0.023\pm 0.087\) (cm/m). For these calculations, the outlier of more than 100 cm shown in Figure 7 was removed.
# A Review on Bioactive Compounds and Pharmacological Activities of _Citrus unshiu_
## 3 Botanical Description and Bioactive Compounds of _C. unshiiu_ Marc. Satsuma mandarin (C. _unshiiu_ Marc.) is a widely cultivated citrus that originated in Southeast Asia (mainly in Korea and Japan) [25] and is considered the most adaptable and cold-resistant citrus with high commercial value [26; 27]. Satsuma mandarin is often seedless [28], and has two tissue types: flesh (pulp or endocarp) and rind (peel or pericarp) [28; 29]. The mandarin pulp consists of vascular bundles and segment membranes containing juice vesicles. The mandarin peel is divided into the external colored layer (flavedo or exocarp) and the internal white layer (albedo or mesocarp) (Figure 1). Three different groups of flavonoids (flavanones, polymethoxylated flavones, and flavonols), carotenoid \(\beta\)CX, alkaloid synephrine, and dietary fiber pectin were isolated from Satsuma mandarin, as illustrated in Table 1. The chemical structure of these compounds is shown in Table 2. The flavanones were present in two forms: glycosylated (hesperidin, naringin, narirutin, neopnicirin) and aglyonated (hesperetin, naringenin). The immature peel of Satsuma mandarin was rich with hesperidin in areas located around the vascular bundles and between the albedo and flavedo layers [30]. Hesperidin, naringin, and narirutin were found in high amounts in the dry peels and/or pulps of various Satsuma mandarin cultivars [31]. Subcritical water extraction in combination with pulsed electric field resulted in enhanced extraction of narirutin and hesperidin from _C. unshiiu_ peels [32]. Using subcritical water extraction at temperatures ranging from 140 \({}^{\circ}\)C to 160 \({}^{\circ}\)C enhanced the yield of hesperidin and narirutin in _C. unshiiu_ peels [33]. Microwave-assisted extraction improved the overall recovery of narirutin and hesperidin from _C. unshiiu_ peels by 1310 mg/100 g and 5860 mg/100 g, respectively [34]. Hesperidin, narirutin, and naringin were the greatest amounts of flavanones found in the dry extract of _C. unshiiu_ peels made with 70% ethanol [35]. Higher levels of hesperidin, narirutin, and naringin were seen in Satsuma mandarins exposed to ozone during fruit storage after harvest [36]. Nobiletin, tangeretin, and 3,5,6,7,8,3\({}^{\prime}\)A\({}^{\prime}\)-heptamethoxyflavone (HMF) were considered the major polymethoxylated flavones of Satsuma mandarin. Nobiletin and tangeretin levels are higher in the dry peels of five distinct Satsuma mandarin types than in the pulps and juices [31]. The peels of _C. unshiiu_ had higher levels of nobiletin and tangeretin than the pulps when the extracts were subjected to high temperatures (normal atmospheric temperature plus 4 \({}^{\circ}\)C), both during the day and at night [37]. The peels of _C. unshiiu_ were found to contain more nobiletin and tangeretin during the early stages of development than during the later stages of maturation [38]. Figure 1: Anatomy of Satsuma mandarin [28; 29].
## 5 Analysis of Internet of Things' Properties
to the propagation of the radio signal, which reduces the power of interference from network nodes by reducing the number of available nodes located in a limited area. A feature of a high-density IoT network is that a large number of nodes are at a small distance from each other. This leads to mutual interference leading to a decrease in the signal-to-noise ratio. Therefore, this requires a reduction in the distance between neighboring nodes in the route. Exactly, this is what happens when modeling a high-density IoT network with a Poisson point process (Poisson field). In reality, for example, in an urban structure, the situation may differ significantly from such a model. The nodes of the network are unevenly distributed due to the presence of obstacles in the form of walls, buildings, and other structures. On the one hand, such obstacles divide the entire set of network nodes into groups (clusters) that are remote from each other and reduce mutual interference between them, thereby improving the quality of channels within these clusters. On the other hand, if necessary, communication between clusters has to increase the length of the routes because of the need to bypass these obstacles. The length of the route in such a network (in the number of transits) is random. If we characterize the network by the probability density of the route length between nodes, then for a given model and a Poisson field model, different distributions should be expected. The proposed model can be adapted to simulate various conditions by choosing the appropriate fractal curve, its length, the proportion of the occupied area, the number of network nodes, their parameters, and the signal attenuation model. Typically, users in an access network or nodes in a wireless sensor network are modeled by point processes, such as a Poisson field or fields with other properties. It should be noted that in this case, the researcher does not have a numerical characteristic that would describe the features of the real environment and allow them to be related to the properties of the model. In comparison with the known models, the proposed model allows one to take into account the features of the environment, which are numerically expressed in the form of the Hurst parameter or fractal dimension. These properties can be estimated numerically in the target environment and also reflected numerically in its model. This allows you to approximate the properties of the model to the properties of the environment it describes.
# Feature-Selection-Based DDoS Attack Detection Using AI Algorithms
## 3 Methodology
### DDoS Dataset  
Every AI model requires effective datasets for training and testing in order to calculate the percentage of authenticity. Accurate data collection is a major problem worldwide; thus, in this area, researchers and institutions are making a contribution. We use CICD-DOS2019 [26] datasets, as well as having separate training and testing files, which have 88 columns and millions of rows. They consist of 12 different DDOS attack types in total (NTP, DNS, LDAP, MSSQL, NetBIOS, SNMP, SSDP, UDP, UDP-Lag, WebDDoS, SYN, and TFTP), as well as 7 attack types for testing (NetBIOS, LDAP, MSSQL, UDP, UDPLag, and SYN). Basically, SYN and NetBIOS are the two types chosen for this experiment. The size of these two types of data are five Giga bytes (5 GB). The features that are considered are shown in Table 2.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Features** & **Descriptions** \\ \hline Source Port & Number on sender’s side of a communication \\ Destination Port & Number on the receiver’s side \\ Flow Duration, & Sequence of packets between source and destination \\ Total Fwd Packets & Total number of packets sent \\ Total Backward Packets & From destination to source \\ Bwd Packet Length Min & The minimum length of packets \\ Flow IAT Min & Minimum time between two consecutive packets in a flow \\ Bwd IAT Min & Interarrival time of packets in the backward \\ Fwd PSH Flags & The sender has finished sending data \\ Fwd Header Length & Header in the forward direction \\ Bwd Header Length & Header in the backward direction \\ Bwd Packets/s & Backward packets per second \\ Init\_Win\_bytes\_backward & The size of the receiving window during the initial phase of the connection in the backward direction \\ \hline \hline \end{tabular}
\end{table}
Table 2: Extracted features.  
Figure 1: Architecture of the proposed model.
# Experimental Assessment of the Spatial and Temporal Distribution of Non-Contact Tonometer Airflows
## 4 Discussion
### Limitations  
The pressure sensor used had an input depended on the measurement uncertainty. Due to the sensor noise, the maximum airflow pressures varied in the range of the uncertainty. To reduce the influence of the measurement uncertainty for each airflow pressure value, at least ten consecutive measurements were conducted and averaged. The novel measurement procedure described in this study had two main sources of random errors: the electrical components, sensor and digital oscilloscope, and the procedure to obtain spatially and distance distribution of the respective airflow. To minimise the random errors, the detected signal has undergone a smoothing procedure and the relative nozzle to sensor position was controlled by using real-time monitoring and capturing the position for each measurement. Additionally, environmental conditions such as temperature, humidity and noise level were kept as stable as possible. A systematic error of the measurement procedure was not detected.  
The extrapolation of information obtained in this study to the situation on the human cornea is limited. Although a curved surface simulating the average healthy, adult cornea has been used, the real eye has a more complex structure. Influences from the tear film, lid aperture, eye lids and lashes as well as the positioning of the patient's head have not been considered in this experimental set up.
## 3 Beamformer Design  
We design the beamformer vector set \(\mathbf{w}_{R,i}\) and \(\mathbf{w}_{T,i}\) for \(i=1,2\) such that the average throughput \(R\) is maximized with the two relays and two NOMA user terminals decoding the signals with the SIC manner. However, the fact that the relay \(R_{i}\) transmit beamformer vector \(\mathbf{w}_{T,i}\) affects the SINRs \(\gamma_{j}^{R,i},j=1,2\) at \(R_{j}\) makes the optimization problem complicated and does not yield a straight-forward solution. Depending on the way they handle the cross-relay-interference (CRI), we propose two sub-optimal beamformer designs in the following subsections. Both designs let the relay receive beamformers \(\mathbf{w}_{R,i},i=1,2\) to zero force the CRI perfectly and leave the transmit beamformers \(\mathbf{w}_{T,i},i=1,2\) to take care of the NOMA user SINRs. This condition requires \(\mathbf{w}_{R,i}=(\frac{\mathbf{P}_{G}\mathbf{w}_{T,i}(\mathbf{h}_{i})}{| \mathbf{P}_{G}\mathbf{w}_{T,i}(\mathbf{h}_{i})|})^{H},i=1,2\) to make the SINR at the relays in (4) to be free of CRI as  
\[\gamma_{j}^{R,i}=\frac{|\mathbf{w}_{R,i}^{H}\mathbf{h}_{i}|^{2}\rho_{j}P_{s}}{| \mathbf{w}_{R,i}^{H}\mathbf{h}_{i}|^{2}\sum_{m=j+1}^{2}\rho_{m}P_{s}+1}. \tag{6}\]
# Dynamic Structural Response of a Corrugated Blast Wall Under Hydrogen Blast Loads
## 3 Methods
### Blast Load Scenarios of Hydrogen and Hydrocarbon Explosions  
Blast load scenarios were selected based on the blast load characteristics analyzed in the preceding section and prior research. Dynamic structural response of the target structure was observed in numerical simulations of these scenarios. The blast load scenarios are summarized in Table 4. The peak overpressure for hydrocarbons was determined by referencing the nominal blast overpressure provided by API [64]. The peak overpressure for hydrogen was determined based on the observed characteristic that the maximum peak overpressure (Max. \(p_{\text{peak}}\)) in Figure 1b was approximately 3.5 times greater than that of hydrocarbons. The duration was initially determined as the range in which the dynamic response caused by inertial force was observed [65; 66], through a parametric study. Peak overpressure and duration were assumed to be independent, and all combinations of these two load characteristics were included in the scenarios. This assumption was justified by the blast load characteristics that, even for explosions with a similar level of peak overpressure, different durations can be observed depending on other explosion conditions, as shown in Figure 2. The inversely proportional trend between peak overpressure and rising time-to-duration ratio observed in Figure 3 was also considered in the blast load scenarios. Accordingly, load models for each peak overpressure are summarized in the fourth row of Table 4. The 0.25\({}_{\text{d}}\) model represents the triangular impulse load with a rising time-to-duration ratio of 0.25. A visual example of this model is provided in the fifth row of Table 4. As a result, 40 and 130 blast load scenarios were selected for hydrocarbon and hydrogen explosions, respectively.  
Figure 8: Validation of the numerical model using experimental data: comparison of deformations for (**a**) overall panel and (**b**) connection.  
In the following section, the dynamic structural response of the blast wall was analyzed from the perspective of maximum and permanent y-displacements at the mid-span of the blast wall (_Disp_maximum and _Disp_permanent) and maximum and residual strain energies of the corrugated panel (_S.E._maximum and _S.E._residual). For clarity, Figure 9 shows y-displacement at the mid-span and strain energy of the corrugated panel in the blast load scenario with peak overpressure of 0.5 MPa and duration of 0.1 s. In addition, it presents the definitions of the maximum and permanent y-displacements, as well as the maximum and residual strain energies.
# New and Efficient Algorithms for Producing Frequent Itemsets with the Map-Reduce Framework
## 2 Background and Related Work
### Incremental Computation in Map-Reduce  
Most of the time, when data is changed or added, the result of the algorithm also changes. Map-Reduce doesn't provide built-in tools in the model or framework to support result updates. There are some attempts by researchers to enhance the Map-Reduce model to support it.  
One of the attempts is the Incoop system [28]. This paper proposes a way (that is almost transparent to the user of Map-Reduce) to keep the results of the algorithm updated as new data is added. The system treats the computations as a Directed Acyclic Graph (DAG) of data that flows from input to output, and on the way, it is transformed by user functions. When data is updated, the system re-runs only the part of the graph that has some new input. This system uses a Memoization technique to keep the data-algorithm-result dependencies. This works well for the Mapper (map job), as only a few data records affect a small number of Mappers. In the case where a new key-value pair is generated for the Reducer (reduce job), then it will need to re-run its function on the whole previous input (new value and all old values). To treat this problem, Incoop also has a new stage called Contraction, which allows the user to supply additional functions that can combine several reducer input/outputs. It allows for dividing larger inputs into smaller parts and re-running the algorithm only on the updated part.  
A similar approach can be found in DryadInc [29]. The idea was developed for a system called Dryad, which is a more general version of Map-Reduce. The main difference is that Dryad allows any DAG of computations, not only Map and Reduce. In the incremental version, there's also a Cache server that keeps input/output relations and a new Merge function which can merge outputs of any function (and not only of Map as in Incoop).  
Since Memoization and incremental caching of Incoop are not part of the standard Map-Reduce, and since their source code is not publicly available, we decided to do our experimental evaluation using the standard Map-Reduce framework only. Yet, we can assume that these enhancements may not work well for FIM (especially Apriori) algorithms. The basis for this claim is that new input records for Apriori may generate new frequent itemsets of any length, and in the following iterations the new input records generate even more frequent itemsets based on them. So, a small change in the input may propagate to a very large part of the output. Another reason is that each step requires a recount of frequent itemsets over the whole \(\mathcal{D}^{+}\) (with new records), so none of the above systems would be able to use their Memoization/Caching data and will have to run all calculations from the beginning.
## 6 Numerical Simulation and Analysis
### Tracking Performance of the ST Approach  
**Definition 6**: **(Tracking efficiency \(\mathbf{\phi}\))**_. The proportion of the key cells tracked in the total cells tracked by ST approach, which is the indicator of tracking performance._  
The cells that A* algorithm passes through are modeled as the key regions, and Figure 9a shows the marked results. Using basic PIO algorithm to track these key cells, the tracking results under 3 OC, 4 OC, and 8 OC are shown in Figure 9b. In some slit areas, \(\phi\) will be reduced because the key cells cannot be tracked completely under the 3 OC. The A* algorithm and Dijkstra algorithm can not satisfy the 3 OC in Area 1 and Area 2. To maximize the rewards, there will be some adaptive path selections, which via a few steps ahead of the turn or lag a few steps back to track the key cells. The \(\phi\) for different OCs are given in Figure 10. Its abscissa represents the unit length after subdividing Figure 9a. The search efficiency of 8 OC is higher than that of 3 OC and 4 OC. As the grid map is subdivided, The \(\phi\) of 3 OC and 4 OC increases.
## 3 Link Model
### Receiver Sensitivity  
Given the required BER \(P_{\rm e}\), we can estimate the receiver sensitivity \(P_{\rm sens}\) as the average power \(P_{\rm avg}=P_{\rm sens}\) required by first inverting (17) to obtain the required value of \(\gamma\). The inverse of the \(Q\) function can be written in terms of the inverse \(\mathrm{erf}^{-1}(y)\) of the error function \(\mathrm{erf}\), which can be estimated through standard Python libraries:  
\[\gamma=Q^{-1}(P_{\rm e})=\sqrt{2}\mathrm{erf}^{-1}(1-2P_{\rm e}). \tag{23}\]  
Once the target value of \(\gamma\) is obtained through (23), we can substitute Equations (21) and (20) in (18) to obtain the following solution for the required receiver sensitivity \(P_{\rm sens}\):  
\[P_{\rm sens}=\delta^{2}(a_{0}+a_{1})+2\delta\sqrt{\delta^{2}a_{0}a_{1}+\sigma _{\rm th}^{2}}, \tag{24}\]  
where  
\[\delta=\frac{\gamma}{2\mathcal{R}}\frac{r_{\rm e}+1}{r_{\rm e}-1}\,a_{0}=\frac{4q_{\rm e} \mathcal{R}B_{\rm e}}{1+r_{\rm e}}\,a_{1}=r_{\rm e}a_{0}. \tag{25}\]  
Equations (24) and (25) can be used to determine the receiver sensitivity given the receiver parameters. One can then determine the required received power \(P_{\rm sens}\) using (15) and then estimate the required transmitter power using (11). It is critical to note that these sensitivity values assume ideal synchronization conditions. In practice, timing recovery (e.g., Gardner loops) and adaptive equalization [35] are mandatory to mitigate BER degradation from LEO-specific impairments.  
In our calculations, we deal with the uncoded BER and do not account for forward error correction (FEC). FEC may enhance the system's BER performance once the uncoded BER exceeds a given threshold value \(P_{\rm e}^{\rm th}\). Hence, we may set the target BER in (17) equal to \(P_{\rm e}^{\rm th}\) to obtain the required optical power. In addition, we do not account for signal processing algorithms that can enhance the BER performance [35]. This is because for the constellation altitudes and inter-satellite link distances considered, the optical channel is approximately flat.  
In Figure 6, we illustrate the values obtained for \(P_{\rm sens}\), assuming the parameters in Table 2. As expected, the sensitivity value increases with the data rate reaching just below \(\cong\)\(-20\) dBm at \(R_{\rm b}=10\) Gbps.  
The various model elements discussed in Sections 2 and 3 were implemented as part of the Pyminisat Python module [28], which is publicly available under an open-source license. Details about the inner workings and the model implementation can be found in Appendix A. The model's Github repository further describes how the initialization parameter can be adapted to the constellation at hand.