esa-sceva/satcom-chunk-collection huggingface dataset:
# Decision-Tree-Based Horizontal Fragmentation Method for Data Warehouses
## 4 Results and Discussion
### Application of FTree in the Tourist Data Warehouse  
FTree was applied and tested in the tourist DW. The fact table fragmented was _Hotel_activity_. We performed the first experiment using the predicate use matrix (PUM) of Table 10.  
Figure 5: The multidimensional model of the tourist DW. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Name** & **Type** & **Attributes** & **Tuples** \\ \hline Hotel\_activity & Facts & 8 & 35,424 \\ International\_visitors & Facts & 5 & 576 \\ Location & Dimension & 4 & 123 \\ Time & Dimension & 4 & 144 \\ Tourism & Dimension & 3 & 4 \\ Tourist & Dimension & 2 & 2 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Tables of a tourist DW. In this case, the upper limit of partitions (W) allowed by the DWA is 4. The predicates considered are presented in Table 11. Using the PUM of Table 10, the partition tree of Figure 6 was obtained by FTree (every column represents a fragment and the * indicates an empty partition). Table 12 depicts the comparison between \(data\_set_{2}\) with fragments \(f_{1}=\{p_{1},p_{4},p_{5},p_{7}\}\),\(f_{72}=\{p_{2},p_{3},p_{6}\}\) and \(data\_set_{3}\) with fragments \(f_{1}=\{p_{1},p_{4},p_{7}\}\), \(f_{72}=\{p_{2},p_{3},p_{6}\}\),\(f_{73}=\{p_{5}\}\). We used 8-fold cross-validation. The best scheme is \(data\_set_{2}\). The decision tree obtained by FTree is visualized in Figure 7. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Q/Pr** & \(p_{1}\) & \(p_{2}\) & \(p_{3}\) & \(p_{4}\) & \(p_{5}\) & \(p_{6}\) & \(p_{7}\) & \(F_{i}\) \\ \hline \(q_{1}\) & 1 & 1 & 1 & 0 & 0 & 0 & 0 & 20 \\ \(q_{2}\) & 1 & 0 & 0 & 1 & 0 & 0 & 0 & 15 \\ \(q_{3}\) & 1 & 0 & 0 & 0 & 1 & 0 & 0 & 15 \\ \(q_{4}\) & 0 & 1 & 0 & 0 & 0 & 1 & 0 & 35 \\ \(q_{5}\) & 0 & 1 & 1 & 0 & 0 & 0 & 0 & 35 \\ \(q_{6}\) & 1 & 0 & 0 & 0 & 0 & 0 & 1 & 30 \\ \(q7\) & 0 & 0 & 0 & 1 & 0 & 0 & 0 & 10 \\ \(q_{8}\) & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 10 \\ \(q_{9}\) & 0 & 0 & 0 & 0 & 1 & 0 & 0 & 10 \\ \(q_{10}\) & 0 & 0 & 1 & 0 & 0 & 0 & 0 & 10 \\ \(sel_{j}\) & 2952 & 8856 & 2952 & 2952 & 2952 & 2952 & 2952 & \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Predicate usage matrix for the first experiment. Figure 6: PT for the first experiment. Other classification algorithms were used to select the fragmentation scheme. Table 12 presents the results of the evaluation metrics precision (P), recall (R), F-measure (F), and ROC area (ROC) with naive Bayes and multilayer perceptron with one hidden layer with six units, sigmoid activation function, learning rate = 0.3, momentum = 0.2, and number of epochs = 500. As seen in Table 12, all the algorithms agree that the two-fragment scheme is the most suitable. In addition, the model obtained by FTree (Figure 7) is easier to interpret, which allows the fragmentation of the data warehouse based on the attribute _frequency_. We performed a second experiment. Table 13 presents the OLAP queries executed in the tourist DW. The PUM for this workload is shown in Figure 8.
# Relationship between Total Antioxidant Capacity, Cannabinoids and Terpenoids in Hops and Cannabis
## 4 Materials and Methods
### Measuring Antioxidant Activity with the Ferric Reducing Antioxidant Power (FRAP) Assay  
The antioxidant capacity of hops and cannabis was additionally determined using the ferric reducing antioxidant power (FRAP) assay based on methods developed by [PERSON] (1996) and [PERSON] et al. (2021) for cannabis, with some modifications. The standard curve was prepared using different serial dilution concentrations (10-0.004 mM) of Trolox (Sigma-Aldrich, Saint Louis, MI, USA). The FRAP reagent was prepared from 300 mM sodium acetate buffer (pH 3.6), 20 mM 2,4,6-tri(2-pyridyl)-s-triazine (TPTZ) (Sigma-Aldrich, Saint Louis, MI, USA) solution in 40 M hydrochloric acid (Thermo Fisher Scientific, Waltham, MA, USA) and 20 mM ferric chloride (FeCl\({}_{3}\)) (Sigma-Aldrich, Saint Louis, MI, USA) solution in proportions of 10:1:1 (\(v/v\)), respectively. The FRAP solution was prepared fresh daily and warmed to 37 \({}^{\circ}\)C in a water bath for 10 min prior to use. An aliquot (100 \(\mu\)L) of extracted samples or standards was placed in 15 mL Falcon tubes and 2900 \(\mu\)L FRAP stock solution was added. After vigorous vortex (Thermo Scientific vortex, Waltham, MA, USA) for 30 sec, the mixture was incubated for 60 min at room temperature and in darkness. Absorbances were measured at 593 nm using the Ultropec 2100 pro ultraviolet/visible spectrophotometer (Biochrom Limited, Cambridge, England). The FRAP solution was used as a control and HPLC-grade methanol was used to zero the spectrophotometer. The experiment was carried out in triplicate. FRAP inhibition was calculated using Equation (4). The FRAP value (antioxidant activity) was calculated using the calibration curve and Equation (5). Results are reported as FRAP value (M) per gram dry matter sample using Equation (6).  
\[\text{FRAP inhibition (AU)}=\text{Absorbance}_{\text{sample}}-\text{ Absorbance}_{\text{control}} \tag{4}\] \[\text{FRAP value (M)}=\frac{\left(\frac{\text{FRAP inhibition}-0.1263}{1.228}\right)}{1000} \tag{5}\]  
\[\text{FRAP value (M) dry matter (g)}^{-1}=\frac{\text{Extraction volume (0.04 L)}\times\text{FRAP value (M)}}{\text{Analysis volume (0.0001 L)}\times\text{(sample mass }-(\%\text{ mc}\times\text{sample mass))}} \tag{6}\]
# Basis Functions for a Transient Analysis of Linear Commensurate Fractional-Order Systems
## 4 Time-Domain Response via Basis Functions: Real s\({}^{1}\)-Domain Poles  
For the case of an \(m\)-tuple real pole \(s=a\) of the transfer function of an IOS, the corresponding partial fractions of decomposition (2) are of the familiar form  
\[\sum_{k=1}^{m}\frac{P_{k}}{\left(s-a\right)^{k}} \tag{41}\]  
where \(P_{k}\) are the residues belonging to the root \(a\).  
The partial fractions of the decomposition of associated transfer function (4) of a commensurate FOS have the same form as (41), differing only in substitution (2), so they can be written as follows:  
\[\sum_{k=1}^{m}\frac{P_{k}}{\left(s^{\alpha}-a\right)^{k}}. \tag{42}\]  
From the well-known formula  
\[\frac{s^{\delta}}{\left(s^{\alpha}-a\right)^{k}}\widehat{=}\frac{t^{\alpha k- \delta-1}}{\left(k-1\right)!}E_{\alpha,\alpha-\delta}^{(k-1)}(at^{\alpha}),k, \delta\in\mathrm{N};\alpha\in\mathrm{R}+, \tag{43}\]  
published in various notations in a number of works, e.g., [13], the relations can be derived between the time and Laplace domains, relevant to partial fractions (41) and (42), for single and multiple real poles, as shown in Table 2.  
From a comparison of the relations in Table 2, analogies begin to emerge between the basis functions for IOS and FOS. For a simple pole, the classical exponential function of an IOS corresponds to a MLF of type \(E_{\alpha,\alpha}\) multiplied by a factor \(t^{\alpha-1}\). However, this is a generalized exponential function (26). For the multiple pole, the classical exponential function is accompanied by a multiplicative term \(t^{k-1}\). In the fractional domain, this is reflected both by the derivative of the function \(E_{\alpha,\alpha}\) of order \((k-1)\) and by the multiplicative term \(t^{\alpha(k-1)}\). It is easy to see that the resulting waveform can then be smartly expressed by function (28) of type \(e_{k-1}\). A more detailed analysis will be carried out in Section 6.
# Edge Computing: A Survey On the Hardware Requirements in the Internet of Things World
## 3 Ultra-Low Power MCU Architectures
### Communication and Security  
This section deals with Internet communication systems and security issues arising from the IoT paradigm. Many standards have emerged in recent years to cope with the low power requirements of IoT nodes. Depending on the application, in particular on the required data rate and data range, it is possible to select the best transmission technology. Sometimes, it is possible to create a local network and to use an aggregator for sending the data to the cloud for further processing or for being stored. However, future IoT nodes will heavily depend on cellular communication, especially with the forthcoming 5G, as the prominent player [42; 43]. Indeed, 5G aims to be a revolution for machine-to-machine communication, in particular in situations which were not optimally handled by previous cellular communication systems. In particular, 5G communications can be tailored around the application to improve reliability, reduce latency and energy consumption, and to increase device density. The adoption of 5G in new IoT platforms promises to enable a true pervasive fully-connected era. However, always connected devices through wireless connections can be prone to external hacking attempts, which is a major issue when dealing with sensible data and/or dangerous situations [44; 45; 46; 47; 48; 49]. Attacks can involve sensors nodes to collect precious data from users, which would otherwise be unavailable for privacy or secrecy reasons [50]. These data could be used for analysis purposes or to profile users. On the other hand, attacks can involve actuator-based systems, like Autonomous Electric Vehicles [51; 52; 53; 54], or Microgrids (small electrical sources able to better distribute the electrical power through the load) [55; 56] or healthcare devices (like implantable cardiac devices) or literally every electrical item that will potentially be equipped with a network access. For this reason, a substantial amount of research is devoted to ensure connection security and to avoid eavesdropper stealing precious data. IoT devices will be built with hardware cryptographic accelerators for optimizing power consumption and latency of data transmission. These hardware accelerators can (and should) also be used for anti-tamper protection and to circumvent IP stealing attempts, encrypting the data in inter-chip communications to null reverse engineering efforts.  
Figure 3: Mobile Industry Processor Interface (MIPI) I3C bus efficiency and throughput. (**a**) energy consumption (mJ/Mbit); (**b**) raw bitrate (Mbit/s).
# An Analysis of Commercial Noni Juice: LC-MS/MS Phenolic Profiles and Cytotoxic Activity
## 3 Results and Discussion
### Quantitative Phenolic Profiling by LC-MS
\begin{table}
\begin{tabular}{c l c c c c} \hline \hline
**No.** & **Putative Identification** & **CIN** & **TON** & **DHN** & **FN** & **LHN** \\ \hline
1 & Gallic acid & 0.28 & 0.62 & 0.33 & 0.41 & 0.18 \\
2 & Protocatechuic acid & 0.30 & 0.93 & 0.67 & 1.74 & 0.95 \\
3 & Gentisic acid & 0.15 & 0.97 & 0.47 & 3.21 & 5.53 \\
4 & Neochlorogenic acid & ND & ND & 0.02 & ND & ND \\
5 & 4-hydroxybenzoic acid & 5.84 & 4.36 & 15.53 & 8.92 & 4.76 \\
6 & (+)-Catechin & ND & ND & ND & ND & ND \\
7 & Caffeic acid & 0.29 & 1.32 & 0.73 & 2.20 & 1.37 \\
8 & Chlorogenic acid & ND & 0.01 & 0.06 & ND & 0.01 \\
9 & Cyanidin 3-glucose & ND & ND & ND & 0.03 & 0.05 \\
10 & Salicylic acid & 0.26 & 1.15 & 0.53 & 1.84 & 2.11 \\
11 & Vanillic acid & 0.01 & 0.24 & 0.14 & 0.34 & 0.21 \\
12 & Syringic acid & ND & ND & ND & ND & ND \\
13 & \(p\)-Coumaric acid & 0.23 & 0.71 & 0.55 & 1.23 & 0.97 \\
14 & Malvidin 3-glucoside & ND & ND & ND & ND & ND \\
15 & Ferulic acid & 0.10 & 0.29 & 0.29 & 0.94 & 0.51 \\
16 & Vitexin & ND & ND & ND & ND & ND \\
17 & Ruitin & 0.09 & 41.18 & 28.41 & 2.96 & 6.82 \\
18 & Sinapic acid & ND & ND & ND & 0.20 & 0.15 \\
19 & Quercetin 3-glucoside & 0.01 & 1.42 & 1.27 & 0.55 & 1.70 \\
20 & Ellagic acid & ND & ND & 0.05 & ND & 0.07 \\
21 & Myricetin & ND & ND & ND & ND & ND \\
22 & Resveratrol & ND & ND & ND & ND & ND \\
23 & Pelargonidin & 0.06 & 0.18 & 0.06 & 0.16 & 0.14 \\
24 & Delphinidin & 0.20 & 0.51 & 0.28 & 0.59 & 2.50 \\
25 & Quercetin & 0.14 & 0.51 & 0.27 & 0.42 & 2.13 \\
26 & Luteolin & ND & ND & ND & ND & ND \\
27 & Cyanidin & ND & 0.04 & ND & 0.05 & 0.08 \\
28 & Kaempferol & ND & ND & ND & 0.01 & 0.09 \\
29 & Naringenin & ND & ND & ND & ND & ND \\
30 & Apigenin & ND & ND & ND & ND & ND \\ \hline \hline \multicolumn{5}{c}{Sum of identified compounds} & 7.96 & 54.44 & 49.66 & 25.8 & 30.33 \\ \hline \hline \end{tabular} ND = not detected. Abbreviations: FN = Fijian Noni; LHN = Life Health Noni; DHN = Dynamic Health Noni; TON = Tahitian Organic Noni; CIN = Cook Island Noni.  
\end{table} 
Table 6: Quantitative profiling of phenolic compounds in the noni juice samples using the developed LC-MS/MS method (values given as mg L-1 of the undiluted juice).
# Post-Quantum Key Exchange and Subscriber Identity Encryption in 5G Using ML-KEM (Kyber) 1
## 5 Our PQC Incorporation and Implementation
### Post-Quantum Cryptography for Subscriber Concealed Identifier (SUCI)  
In this section, we describe the SUCI generation using post-quantum cryptography. Unlike Section 5.1, the post-quantum cryptography algorithms use a KEM to generate a shared key. We describe the related background information for the KEM in Section 2.2.4 and build on it. Although we use a KEM, the information elements of the SUCI format are intact; hence, we omit redundant information about the fields in the SUCI because it is already described in Section 5.1. The steps for generating the protection scheme output for the SUCI are given below:  
The CN generates the public-private key pair using the _KEM.KeyGen_ function. The resulting CN's public key is stored in the USIM.  
1. Public-Private Key Generation (UE): The UE generates the public-private key pair using the _KEM.KeyGen_ function.
2. Encapsulation (UE): To encrypt SUPI/IMSI, the UE uses the function _KEM.Encapsulate_ to generate initial randomness, derive the shared key from this randomness, and compute a ciphertext using the CN's public key. The generated ciphertext is the encapsulated shared key.
3. Symmetric Encryption: After generating the shared key, the UE encrypts the plaintext (SUPI or IMSI) with a shared key that is computed using function _KEM.Encapsulate_. The UE then sends the encapsulated shared key and encrypted data to the CN.
4. Decapsulation: When the CN receives the registration request, the CN uses its private key and encapsulated shared key to generate a shared key by using the decapsulation function _KEM.Decapsulate_, and further uses that generated shared key for decryption to verify the subscriber.  
In our implementation, we use steps 1, 2, and 4 to measure the computational performance of the post-quantum KEM. We also calculate the byte size of the public keys because the key lengths in post-quantum cryptography are much greater than the pre-quantum ciphers. Since the SUCI generation occurs at the USIM, which is an integrated chip and has memory size limitations (e.g., 256 KB), we show the byte size of the public keys in our comparison in Table 4.
## 1 Introduction
Table 1 lists the available circuits and compares them according to the relevant standards. The additional performance comparisons with previous circuits are summarized in Tables 2 and 3. As shown in Tables 1-3, the proposed VM NBPFs and VM QSOs exhibit high-input and low-output impedance and independent amplitude control, which are important for easily cascading the VM operation and independent control of the amplitude gain. The range of the measured QSO amplitude can be adjusted from 1.51 to 24.05, and the measured oscillation frequency can be varied from 82.15 kHz to 1629 kHz. This means that the proposed circuits are sufficient for the typical impedance sensor applications [23]. Because the proposed isomorphic circuits have independent amplitude control functions and a wide tunable frequency range, the applications will be beneficial in the literature [23; 24].
## 4 Discussion
### Some Limitations  
The data were recorded in imperfect circumstances, in a laboratory that was not sound-proofed or temperature-controlled. Nonetheless, external noise was minimised as far as possible, and an attempt was made to keep the space at a comfortable temperature over the course of the year during which data were collected.  
Our'sham' (160 pps) TEAS was not completely without physiological effect, which may have biased our results. In retrospect, although various sham stimulation methods have been explored over the years by different researchers, some subthreshold and some suprathreshold [54; 55; 56], we should have amended our own experimental set-up to ensure no current whatsoever reached the participants. Unfortunately, we made a false assumption based on initial pilot experiments in which sham stimulation was, indeed, subthreshold for the test participants. This was not so for all those who took part in our final study. Nonetheless, the output was set to 'zero' on the Equinox device, so considerably lower during this imperfect sham stimulation than during the 'active' stimulations.  
Only 66 participants took part in this study--a small dataset for a DL study. However, the use of training, validation and test sets, and of 5-fold validation, should have compensated for this.  
In this paper, we did not tackle the question of whether our findings were the result of neural or volume conduction, or whether they indicated a central frequency-following response to peripheral stimulation. Moreover, our analysis did not investigate the EEG electrode-specific effects of TEAS, nor, indeed, the effects over different scalp regions. In addition, we did not explore how EEG might change during and following TEAS at different frequencies.  
Unsupervised DL methods suffer from the problem of interpretability. This was exacerbated in the present study by communication difficulties between the clinicians and computer scientists involved, whom all had very different skills, mindsets, interests and languages. This project provided us all with a challenging and immersive learning experience. We hope not too many misinterpretations remain.
# Health Project Prioritization in Meta, Colombia, Using Anp-Dematel
## 4 Results
### Correlation and Compatibility Analysis  
The authors analyzed variable rank relationships using both [PERSON]'s and [PERSON]'s correlation methods, identifying Kendall's Tau-b as the primary statistical indicator due to its robustness and effectiveness with small samples and tied observations [36]. To evaluate priority vector consistency within the AHP/ANP framework, the authors applied two validated compatibility metrics: [PERSON]'s S index and [PERSON]'s G index [37], which applies a physical interpretation to vector inner products [38; 39]. Table 11 presents Kendall's Tau-b coefficients with \(p\)-values and compares individual versus group priorities using S and G compatibility indices. This dual-metric approach supports rigorous analysis of both rank order and directional alignment.  
The compatibility analysis reveals that Expert 1 (E1) is highly aligned with the group consensus, as shown by the Kendall's Tau-b coefficient of 0.8. This concordance positions E1 as a reference point for validating group priorities and guiding expert discussions.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **E1** & **E2** & **E3** & **Group** \\ \hline A04 & 24.93\% & A04 & 25.00\% & A01 & 30.11\% & A05 & 29.84\% \\ A05 & 23.16\% & A02 & 21.32\% & A05 & 25.11\% & A04 & 20.00\% \\ A03 & 22.83\% & A05 & 19.85\% & A04 & 19.82\% & A03 & 17.95\% \\ A02 & 20.95\% & A01 & 18.85\% & A02 & 15.21\% & A02 & 16.11\% \\ A01 & 8.13\% & A03 & 14.98\% & A03 & 9.75\% & A01 & 16.10\% \\ \hline \hline \end{tabular}
\end{table}
Table 10: Prioritization of alternatives.  
In contrast, Expert 2 (E2) shows low compatibility ([PERSON] = 0.2), suggesting possible divergence in criteria interpretation or evaluative emphasis. Targeted alignment sessions could help harmonize E2\({}^{\prime}\)s contributions and improve overall group coherence.  
Expert 4 (E4), with a negative Kendall coefficient (\(-0.2\)), reflects significant divergence; however, this difference can enrich analysis if addressed through inclusive dialogue and collaborative workshops.  
The E1-E2 comparison ([PERSON] = 0.4) indicates low compatibility, possibly due to divergent heuristics or methodological preferences. Facilitating exchanges between these experts may help bridge conceptual gaps. Conversely, moderate compatibility between E2 and E4 ([PERSON] = 0.6) suggests potential complementarity and an opportunity for shared insights.  
The results from the S and G indices reinforce the observed variability in expert priority vectors. These variations may stem from disciplinary or cognitive differences in evaluation; therefore, implementing consensus-building strategies such as joint reviews, shared criteria interpretation, and iterative feedback is essential to ensure methodological cohesion.  
In summary, promoting leadership among highly compatible experts (e.g., E1) while actively including divergent perspectives fosters a more robust and participatory decision-making process. Such strategies reinforce analytical rigor and enhance shared ownership of prioritization outcomes.
## 2 Optical Inspection Technologies in Railways
### Sensor Systems
#### 2.3.10 Rolling Stock  
In so-called train-monitoring portals, the measuring sensors are permanently installed, and the moving train passes through the portal. Swiss Federal Railways SBB uses a combination of cameras and laser scanners to compare the measured train profile with a reference profile. This is intended to identify incorrectly loaded or defective vehicles, to avoid collisions with the railroad infrastructure and when trains are passing each other, and to prevent fires and damage to property caused by contact with overhead wires [72]. For example, an incorrect inclination of the wagons can affect the stability of the train and increase the risk of derailment, or an insufficient twisting of the wagons can lead to poor curving and impair safety. One laser scanner designed for this task is the Sector Profile Scanner (SPS) from Fraunhofer IPM [49]. The SPS was developed and implemented according to the requirements and specifications of Ansaldo STS S.p.A. (now Hitachi Rail STS). A total of four of these scanners are combined to form a portal and can therefore  
Figure 9: Wheel damage—flat and cracks [71].  
detect the entire train as it passes (see Figure 10). At a train speed of 360 km/h, the High Profile Density (HPD) setup provides 900 data points per profile at 3200 profiles per second, giving a density of 32 profiles per meter and a distance uncertainty of about 10 mm at a range of 10 m. Ref. [48] mentions a successful detection of the train profile at 330 km/h without specifying a point density or accuracy.  
The great advantage of these systems is that the train can pass through the portal at relatively fast speeds. The portals mentioned here are limited to detecting the upper part of the train and do not detect the underside. Due to the characteristics of laser scanners, reflective surfaces are difficult or impossible to detect. Weather dependency due to the unprotected outdoor installation also influences the measurement quality. A permanent power supply must be provided.