esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Data Preprocessing  
The original spectral plot of the samples is shown in Figure 3. Spectral data may exhibit noise and baseline drift due to instrument technology, aging, environmental electromagnetic interference, the temperature, and the humidity [15], which affect the accuracy of the analysis. Therefore, targeted preprocessing was performed in this study. Different SG filtering [16] parameters (window length w = 1, 3, 5; polynomial order \(p\) = 0, 1, 2) were compared for their processing effects on the spectral data, as shown in Table 1. It was found that w = 1 resulted in insufficient noise reduction, while w = 5 easily caused the deformation of characteristic peaks; \(p\) = 0 had a poor fitting capability, and \(p\) = 2 was prone to overfitting. Overall, when w = 3 and \(p\) = 1, the noise standard deviation decreased by 57% compared to that when w = 1 and \(p\) = 1, while the characteristic peak retention rate remained at 95.3%. The processed spectral plot is shown in Figure 3. Even with the optimal parameters (w = 3, \(p\) = 1), the SG filtering still retained residual noise and struggled to accurately process the characteristic peaks. Wavelet transform, however, offers advantages in multi-resolution noise reduction, characteristic peak enhancement, and complex signal  
Figure 2: MIR data sampling system.  
analysis [17]. Consequently, this study employed the db8 wavelet basis with a 3-layer decomposition. The reconstructed spectrum highlighted key features, as shown in Figure 3. The processed spectral data were stratified by their respective labels and then partitioned into a training set (70%) and a test set (30%) using stratified random sampling, ensuring that the sample distribution of each class in both sets is consistent with that of the original data and maintaining class balance.
# Diversity and Functional Evolution of Terpene Synthases in Rosaceae
## 2 Results
### TPS Classification and Motif/Domain Annotation  
All the TPSs containing both PF01397 and PF03936 domains were subjected to classification and motif annotation. TPSs from the three representative Rosaceae species (_P. persica, M. domestica_, and _F. vesca_) were chosen for the visualization of the classification and domain/motif distribution in TPSs. After removing putative TPSs lacking both domains, a total of 117 TPSs in the three species were used for phylogenetic construction (Figures 1 and S1). The phylogenetic topology revealed that all the TPSs were divided into seven known clades TPS a-g. TPS-b and TPS-g clustered with the TPS-a clade and forms a large branch. TPS-e and TPS-f formed sister clades and clustered close to the TPS-c clade. No TPSs clustered with the TPS-d clade, which was only encoded in gymnosperms. The conserved motifs were constructed using the online MEME software and three conserved motifs (motifs 1, 2, 3) were detected in nearly all the TPSs (109, 96, and 102). However, the frequency and distribution of these motifs varied among TPSs. For example, _Ma.dom-TPS11_ only contained motif 2, and lost motifs 1 and 3, whereas _F.ves-TPS28_ contains an extra copy of motif 3. The motif distribution of TPSs in the eight Rosaceae species is listed in Table S1; the significant E-value indicates the reliability of the identified motifs. For different clades, we found that the motif composition of TPS-a/b/g clades is more conservative than that of TPS-c/e/f, as shown in Figure 2B; nearly all TPSs in these clades contain all of the three motifs. In contrast, many TPSs from TPS-c/e/f lost motif 2, indicating the differences of motif distribution among clades. The conserved domain (CD) annotation used by the CD-search tool in NCBI revealed the discrepancy of domain annotation among different clades. CD domains Terpene_cyclase_plant_C1 (accession: cd00684) and Isoprenoid_Biosyn_C1 (accession: cd00385), which both belong to superfamily Isoprenoid_Biosyn_C1 superfamily (accession: cl00210), were annotated in clades of TPS-a, TPS-b, and TPS-g. PLN02279 super family (ent-kaur-16-ene synthase) was annotated in TPS-e/f clades. PLN02592 superfamily (ent-copallyl diphosphate synthase) was annotated in TPS-c clade. Pfam domain annotation results verified that each full-length TPS is characterized by two conserved domains with PF01397 (N-terminal) and PF03936 (C-terminal). The protein lengths of TPSs ranged from 232 AA to 1726 AA, showing a wide distribution of TPS lengths. One of the conserved aspartate-rich motifs in the C-terminal domain that is involved in the coordination of divalent ions, water molecules, and the stabilization of the active site was characterized based on motif sequences alignment (Figure 2C). The conservation of amino acid composition varied among different TPS clades. The TPS-c subfamily is characterized by the \"DXDD\" motif but not the \"DDDXD\" motif that was detected in other clades.
## 2 Background
### Wireless Sensor Network Components and Node Architecture
The hierarchal architecture depends on top-down or bottom-up approaches; it uses the tree structure, provides flexibility, and is preferred for point-to-point communication. In the partition-based method, the clusters are split into more sections; each section represents a cluster and can be used for a small number of nodes. The spectral type, used in the similarity matrix, needs low processing time, utilizes image processing, and is more suitable for a small number of nodes. In grid-based clustering, the area is divided into sections, depending on certain criteria, and the sensor nodes are added to these sections. This needs low processing time and computational complexity and provides high-performance data distribution. In the density-based method, the area with a larger number of clusters is called the high-density area, and it is separated from the low-density area, used with dynamic clustering, and provides better performance in a harsh environment [10].
## 3 Results and Discussion
### Analytical Application
#### 3.2.4 Evaluation of the Recommended Procedure  
Our spectrophotometric procedure was effectively employed for the analysis of Cu in multielement standard solution (# 90243), and steel sample (No.21899) as certified reference materials, in addition to tap water (Taif City, Saudi Arabia) and sea water (Red Sea, Jeddah Governorate, Saudi Arabia). Copper concentrations in multielement solution and steel sample estimated by the proposed spectrophotometric procedure were \(9.91\pm 0.27\) mg L\({}^{-1}\) and \(0.475\%\) (_w/w_), respectively, with small differences from certified values. However, Student's \(t\)-test revealed no significant differences between the concentrations determined by spectrophotometric method and certified values at the 95% confidence level since the tabulated t-value (2.78) was always greater than the calculated values (2.61, 2.55) for five replicate measurements. Water samples were spiked with a known concentration of copper as shown in Table 7, and the recovery method was determined. Acceptable recoveries were obtained, which confirms the accuracy and applicability of the method for copper determination in water samples. A comparison of the results of our spectrophotometric method with those of the standard ICP-OES indicates analytically-acceptable agreement between these procedures. Therefore, the proposed method can be used for the rapid and sensitive detection of Cu(II) in water samples. The rapid color development due to reaction of AMT with copper(II) can be detected by the naked eye, which makes the proposed method suitable for the qualitative analysis of copper(II) in a variety of samples.
## 4 Security
### Integrity of Information  
In addition to the confidentiality of the exchanged information, its integrity can be threatened by attackers adding additional fragments to the packets or manipulating the data. However, malicious behavior is not the only source of packet manipulations; errors due to the harsh industrial environment are also possible. The modifications of the packet content may cause misbehavior of the equipment and thus have inconvenient effects on the production, or even worse.
# Simulation Study of Two Torque Optimization Methods for Direct Torque-Controlled Induction Motors
###### Abstract  
The simplicity and excellent dynamic performance of Direct Torque Control (DTC) make Induction Motor (IM) drives attractive for many applications that require precise torque control. The traditional version of DTC uses hysteresis controllers. Unfortunately, the nature of these controllers prevents the optimization of the inverter voltage vectors inside the flux hysteresis band. The inverter voltage vector optimization can produce fast torque response of the IM drive. This research proposes two torque optimization methods for IM systems utilizing DTC. Analysis and Matlab simulations for the proposed optimization methods prove that the torque and, consequently, the speed responses, are greatly improved. The performances of the drive system controlled by the proposed optimization methods and the traditional DTC are compared. Conversely, the effects of the parameters on the proposed optimization methods are introduced. The proposed methods greatly improve the torque and speed dynamic performances against the traditional DTC technique. However, one of the proposed optimization methods is more sensitive to IM parameter variations than the other.  
induction motor (IM); direct torque control (DTC); voltage vector; optimization Article
# Reconfigurable Antennas: Switching Techniques--A Survey
## 2 Electrical Reconfiguration
### MEMS  
Reconfigurable antennas with MEMS switches have gained tremendous research interest. MEMS switches are devices which operate by the use of mechanical movement to achieve a short or open circuit in RF circuits. MEMS switches can be designed in different configurations based on signal path (series or shunt), the actuation mechanism (electrostatic, thermal or magnetostatic), the type of contact (ohmic or capacitive) and the type of structure (cantilever or bridge). The required force for mechanical movement can be obtained by different mechanisms for actuation such as electrostatic and magnetostatic. RFMEMS switches that are able to handle up to 20 W and operating at a cycle of 1012 have found applications in radar system, network analyzer, satellite communication systems and base stations [36].  
An RFMEMS shunt switch is a type of MEMS switch, unlike a series switch, which consists of a suspended movable thin metal bridge over the center conductor. It is fixed and anchored at both ends to the ground line of the transmission line. MEMS switches for RF applications operate through short and open circuits to transmit signals [37]. In [38], the characteristics of RHCP/LHCP reconfigurable antenna are investigated. Figure 4a depicts the configuration of the design: Its schematic consists of an E-shaped radiation patch with a coaxial probe feeding into it. In order to facilitate the integration of the RF switched and also to achieve a wide impedance bandwidth, a multilayer configuration has been used in the proposed design. As shown, a pair of MEMS switches are employed across the E-shaped patch slots. Figure 4b illustrates the implementation and bias lines of the MEMS. The RHCP and LHCP characteristics are achieved when one switch is ON and the other one is OFF.  
Figure 3: (**a**) Fabricated filtenna (**b**) its S\({}_{11}\) performance (redrawn from [35]).  
A comparison of different switching components is provided in Table 1. MEMS switches offer some advantages over PIN diodes or varactors, including high isolation and linearity, wide impedance bandwidth, low noise figure and low power losses. However, compared with other RF switches, it requires a high-control voltage and has a slow switching speed and a limited life cycle. Extensive studies on various kinds of reconfigurable antennas with electronic switching components are introduced in [39; 40; 41; 42; 43].
# Problematizing the Adoption of Formal Methods in the 4 IR-5 IR Transition
## 3 Literature Review
### Formal Methods in Software Engineering
#### 3.1.3 Challenges of Formal Methods  
Consider the scenario in which customers order products from suppliers. Upon placing an order, a customer may pay a deposit, or the full price, or may defer payment to a later stage. Abstracting away from the details of defining the state space of the customer-supplier system or aspects of system initialization, the following schema specifies the scenario in which a customer places an order but does not make any payment.  
_Place Order_  
\(\blacktriangle\)_Order_  
_0?_ \(\blacklozenge\)_  
_orders_\({}^{\prime}\) _= orders_ U _{_c_?}_  
_payment_\({}^{\prime}\) _= payment_The system receives as input an order (\(o\)?) which is presently not in the system. The set of orders is updated, and no payment is in effect. In Z, an after state is indicated by a prime (\({}^{\prime}\)).  
At a later stage, the customer is required to make a payment, as captured in schema _Pay_Order_.  
_Pay_Order_  
_Order_System_  
_0?: Order_  
_amount_?: R  
_0?_ \(\in\) _orders_  
_payment_ = payment_ U { _o?_ \(\mapsto\) _amount_?}  
_orders_ \({}^{\prime}\) = _orders_  
The order (\(o\)?) has been placed before (\(o\)? \(\in\) _orders_), following schema _Place_Order_. Payment is specified accordingly, and the set of existing orders remains invariant.  
Following Z's schema calculus [32], the two schemas may be combined to specify a robust operation:  
_Place_and_Pay_Order_\(\doteq\) _Place_Order_\(\wedge\) _Pay_Order_  
_Place_and_Pay_Order_  
_Order_System_  
_amount_?: R  
_0?_ \(\in\) _orders_ \(\in\) _orders_  
_orders_ \({}^{\prime}\) = (_orders_ U { _o?_}) \(\in\) _orders_\({}^{\prime}\) = _orders_  
_payment_ = payment_ \(\wedge\) (_payment_ = _payment_ U { _o?_ \(\mapsto\) _amount_?})  
The schema _Place_and_Pay_Order_ is clearly inconsistent, illustrating that care has to be exercised in applying formal methods. Naturally, reasoning about the specification, by discharging proof obligations arising from the specification, would reveal such inconsistencies. Such an exercise could be manual (by hand), interactive, or automated [33].  
We further note that the above system may, in real life, suffer from various database normalization anomalies [34], e.g., an order would not exist in isolation; instead, it would be linked to a customer. Nevertheless, our example is not intended to be an exercise in normalization.
## 6 Conclusions and Future Work  
This paper introduced a capable word-based SISO semisystolic processor to simultaneously compute multiplication and squaring operations over GF(2\({}^{n}\)). Both operations share the same hardware core, resulting in significant savings in the hardware resource area and the consumed energy, especially for large word sizes. We used a systematic approach to find valid nonlinear scheduling and projection functions that can be applied to the DG to produce the expected word-based SISO semisystolic processor. The chosen functions provide more adaptability to adjust the utilization of the processor besides its execution time. The paper also presented a qualitative and quantitative analysis of the proposed multiplier-squarer structure and the efficient multiplier structures in the literature. The acquired results for the recommended field size \(n=409\) proved that the proposed multiplier-squarer structure had the lowest Area-Time product (AT) for word sizes \(k=16\) and \(k=32\) and consumed less energy for all embedded word sizes. Therefore, it is more suitable to realize cryptographic primitives in RFID-based applications with moderate restrictions on area and delay and significant restrictions on wasted energy. In future work, we will improve the hardware design to have lower area and delay complexities to be suitable for applications with high restrictions on these design parameters.  
Conceptualization, A.I. and F.G.; methodology, A.I. and F.G.; software, A.I.; validation, A.I. and F.G.; formal analysis, A.I.; investigation, A.I.; resources, A.I.; data curation, A.I.; writing--original draft preparation, A.I.; writing--review and editing, A.I. and F.G.; visualization, A.I. and F.G.; supervision, A.I.; project administration, A.I. and F.G.; funding acquisition, F.G. Both authors read and agreed to the published version of the manuscript.  
This research was funded by the National Research Council of Canada (NRC) Grant Number 51291-52200.  
Not Applicable.  
The authors would like to acknowledge the financial support of the National Research Council of Canada (NRC) grant under the Collaborative R&D Initiative.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} IoT & Internet of Things \\ RFID & Radio-Frequency Identification \\ ECC & Elliptic Curve Cryptography \\ SISO & Serial-In/Serial-Out \\ SIPO & Serial-In/Parallel-Out \\ PISO & Parallel-In/Serial-Out \\ CPD & Critical Path Delay \\ AT & Area-Time \\ \end{tabular}
## 3 Related Work
### Hardware-Based Countermeasures  
Hardware-based countermeasures aim to reduce physical leakage or obscure the correlation between cryptographic operations and observable signals. Techniques such as masking and hiding are widely employed:  
* Masking: Introduces random noise to intermediate computations, breaking the statistical dependency between leaked signals and secret keys, as demonstrated by [PERSON] et al. (2017).
* Hiding: Utilizes techniques such as dynamic voltage and frequency scaling (DVFS) to reduce the signal-to-noise ratio of the leakage, effectively obscuring attack vectors ([PERSON] and [PERSON], 2017).  
Recent advancements, such as those proposed by [PERSON] et al. (2018), focus on integrating multiple defense layers into chip-level designs. Their On-Chip Monitoring (OCM) circuits enable real-time detection of attack attempts, while Backside Buried Metal (BBM) structures provide enhanced electromagnetic shielding to reduce leakage. While these approaches significantly improve resistance to SCAs and fault injection attacks, they also increase fabrication complexity and costs, limiting their practicality for low-cost IoT devices.
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l l l} ACE & Active Constellation Extension & MSR & Multiple Signal Representation \\ ACPR & Adjacent Channel Power Ratio & OBO & Output Back-Off \\ AM & Amplitude Modulation & OFDM & Orthogonal Frequency Division Multiplexing \\ AWGN & Additive White Gaussian Noise & OOB & Out-of-Band \\ BER & Bit Error Rate & PAPR & Peak-to-Average Power Ratio \\ B5G & Beyond 5G & PDF & Probability Density Functions \\ CCDF & Complementary Cumulative Distribution Function & PLC & Piecewise Linear Companding \\ CCRR & Computational Complexity Reduction Ratio & PM & Phase Modulation \\ CLT & Central Limit Theorem & PSD & Power Spectral Density \\ CP & Cyclic Prefix & PTS & Partial Transmit Sequence \\ CR & Cognitive Radio & RAs & Real Additions \\ DCT & Discrete Cosine Transform & RMs & Real Multiplications \\ DFT & Discrete Fourier Transform & SL & Soft Limiter \\ DHT & Discrete Hartley Transform & SLM & Selective Mapping \\ DST & Discrete Sine Transform & SRC & Square root Raised Cosine \\ EVM & Error Vector Magnitude & TI & Tone Injection \\ FEC & Forward Error Correction & TR & Tone Reservation \\ HPA & High Power Amplifier & VLC & Visible Light Communication \\ IBO & Input Back-Off & WHT & Walsh-Hadamard Transform \\ IFFT & Inverse Fast Fourier Transform & ZCT & Zadoff-Chu Transform \\ mMIMO & massive Multiple-Input and Multiple-Output & & \\ \end{tabular}
## 2 Materials and Methods
### Groove Shape Measurement  
To characterize the balls by the shape of the groove, the length, cross-sectional area, and total volume of the grooves at the panel joint of each ball were measured. The groove length was measured by placing a string over all the grooves. Some soccer balls have more than one type of panel binding groove. Each type of groove was measured three times, and its average value was determined. 3.6% of the maximum variation for each ball in the three measurements. The cross-sectional area and dimensions of the grooves were measured using a KEYENCE VR-3000 one-shot 3D-shape instrument, as shown in Figure 1. The cross-sectional area and depth of the grooves were calculated using the application, as shown in Figure 2. The depth and cross-sectional area were determined with an accuracy of 1.00 \(\times\) 10\({}^{-3}\) m and 1.00 \(\times\) 10\({}^{-6}\) m\({}^{2}\), respectively. The total groove volume was calculated by multiplying the groove length by the cross-sectional area for each groove type. The total groove volume was defined by the following equation:  
\[V=\sum_{i=1}^{n}L_{i}\cdot S_{i} \tag{1}\]  
where \(V\) shows the total groove volume of a ball, \(L_{i}\) shows the length of \(i\) th type of groove, \(S_{i}\) shows the cross-sectional area of \(i\) th type of groove, and \(n\) shows the number of groove types in a ball.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Name** & **Merlin** & **Telstar18** & **Krasava** & **Ordem** & **Finale** & **Vantaggio** \\ \hline Design & & & & & \\ \hline Oil film & & & & & \\ \hline
**Name** & **Evopower** & **Delta** & **Tango12** & **Jabulani** & **Teamgeist** \\ \hline Design & & & & & \\ \hline Oil film & & & & & \\ \hline
2D-PIV & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: A list of the balls measured in this study. Groove and aerodynamic force measurements were performed on all the balls. Oil film and 2D-PIV measurements were performed on the balls marked with a circle in the table.
## 5 Simulation and Analysis  
The simulation is carried out in three steps. In the first step, we focus on the security of each NVP node in various configurations, including the number of SCVs (i.e., \(N\)) and the output threshold (i.e., \(M\)). The defensive ability against repetitive attacks with time (\(t\)) is further analyzed. These simulations correspond to the analysis in Sections 4.1 and 4.2. Then, considering Section 4.3, we demonstrate the defensive ability of migration from two aspects: the steady-state probability and the time of compromising the target. Thus, in the second step, we explore the influences of single-node compromising rate (i.e., \(\mu\)) and migration range (i.e., \(\omega\)). In the third step, by combining the previous analysis altogether, we explore the security of the whole network (i.e., \(MTTA\) and \(MTTR\)), when adopting NVP and VM migration together.  
The network setup is initialized by giving some default settings. The attack chain consists of 30 nodes. For each node, 10 SCVs are prepared as standby SCVs. For each SCV, the probability of being compromised is listed in Table 5. BS indicates the Base score obtained according to the Common Vulnerability Scoring System when the proposed model is adopted in a real-world system. Other default values used in the proposed SRN model are listed in Table 6. Besides, the single-step attack rate is set to \(r=0.05\) per hour for repetitive attacks. This simulation scenario with the given parameters was adopted from distinct recent papers [18; 24; 60; 67] and adjusted according to our application systems.