esa-sceva/satcom-chunk-collection huggingface dataset:
# FPGA Implementation of a BPSK 1D-CNN Demodulator
## 4 Experiment and Results
### Results of the Implementation  
The Xilinx KC705 evaluation board is equipped with a xc7k325t FPGA. The resource occupancy of the 1D-CNNs demodulator is shown in Table 2. We can see that the chosen FPGA can meet the requirements of the implementation. However, the occupancy rate of DSP48E1s is very high, so it was urgent to simplify the multiplication.  
Figure 12: Performance under different length of input vector. BER: bit error rate.  
Figure 13: Performance under different numbers of neurons.  
In order to fully demonstrate the performance of the 1D-CNN demodulator, complexity, power consumption, and latency of the 1D-CNN demodulator and a of coherent demodulator were compared. In order to eliminate the influence of the hardware platform, the coherent demodulation algorithm was implemented in the same FPGA chip. A Costas loop and Gardner algorithm were employed in the coherent demodulation. The results are shown in Figure 14 and Table 3.  
Hardware resource utilization is listed in Figure 14 to illustrate its complexity. The 1D-CNN demodulator consumes fewer slice registers, slice LUTs, block RAM, and more DSP481 Es. This means that the 1D-CNN demodulator has simpler logic and a larger amount of calculation. Table 3 shows that the 1D-CNN demodulator saved power consumption by 9.07%. As for latency, because of the nonexistence of carrier synchronization, the 1D-CNN demodulator greatly shortened the delay, by 96%.  
In order to illustrate the adaptability to frequency offset and symbol rate offset, the 1D-CNN demodulator and coherent demodulator were tested under the condition of the offset channel. In the offset channel, two of the abovementioned five factors, \(o_{c}\) and \(o_{b}\), were set as 0.1 MHz and 5 kHz, respectively. The BER result is shown in Figure 15. It indicates that the 1D-CNN demodulator has the better adaptability to offset, especially in the case of low SNR.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Logic** & **Used** & **Available** & **Utilization** \\ \hline Slice registers & 9552 & 407,600 & 2\% \\ Slice LUTs & 68,860 & 203,800 & 33\% \\ Fully used LUT-FF pairs & 2960 & 75,452 & 3\% \\ Bonded IOBs & 2 & 500 & 0\% \\ Block RAM/FIFO & 127 & 445 & 28\% \\ BUFG/BUFFGTRLs & 6 & 32 & 18\% \\ DSP481 Es & 721 & 840 & 85\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Resource occupancy of the 1D-CNN demodulator.  
Figure 14: Resource utilization of the 1D-CNN demodulator and coherent demodulator.  
BER performance under an AWGN channel is generally regarded as an evaluation criterion for a demodulator. Theoretical value, simulation value, and actual tested value under the condition of an AWGN channel were carried out. The results are shown in Figure 16.  
The experimental results show that under the condition of an AWGN channel, the demodulation loss of the 1D-CNN demodulator could be kept almost within 2 dB. This can be regarded as a relatively good performance, which can fully meet most of the requirements in wireless communication.
# Lab-to-Field Generalization Gap: Assessment of Transfer Learning for Bearing Fault Detection
## 3 Results and Discussions
### Correct Predictions per Unbalance Level  
Expanding on the sensitivity and specificity analysis, Figure 9 provides a detailed comparison of the classification accuracy across different unbalance levels for both the ML and TL models. The y-axis represents the percentage of correctly classified samples per unbalance level under various data availability conditions. These values were calculated by dividing the number of correctly predicted samples for each unbalance level within the same data availability condition by the total number of samples in the group. Each unbalance level is represented by three points on the y-axis, corresponding to the high (100%), low (25%), and lowest (10%) data availability.  
The ML model maintained over 60% accuracy across all unbalance levels and exceeded 90% for levels 2, 3, and 4. In contrast, the TL model exhibits greater variability, with accuracy dropping to approximately 10% for unbalance Level 3 under low data availability but improved significantly with more data, achieving up to a 60% increase. While the ML model reaches relatively stable accuracy levels across different data conditions, its overall accuracy is lower due to the high misclassification rate for balanced cases. This occurred because the dataset was balanced; the number of samples for Level 0 equaled the combined samples of all other levels, amplifying the impact of misclassified balanced cases on the overall ML accuracy.  
These findings align with the sensitivity vs. specificity analysis and highlight the need for further fine-tuning the TL model to enhance performance in borderline unbalance conditions, especially under data constraints. One possible improvement is to treat each unbalance level separately through multi-class classification or domain adaptation techniques tailored to specific unbalance levels. This would allow the model to learn more precise decision boundaries and better capture small differences between unbalance levels, especially for early-stage faults like Level 1. In addition, future work could explore stratified sampling techniques to ensure balanced representation of all unbalance levels during training, and metric learning approaches (e.g., triplet loss or contrastive learning) to improve feature separability. Despite some limitations, the TL model consistently achieved a higher  
Figure 9: Comparison of the percentage of correct predictions of ML and TL models per unbalance level for various values of data availability.  
classification accuracy than the ML model, reinforcing its suitability for condition-based maintenance applications.
# Lightweight Hybrid Deep Learning Architecture and Model for Security in IOT
## 4 Results and Discussion  
### Preprocessing and Testing  
The data are obtained and exported in readable format, therefore, before the execution process, the data are to be preprocessed. The proper format of data is taken. The sheer volume and dimensionality of the data make it compulsory to perform proper data preprocessing to clean and format data before any training. The first thing to account for is that some of the data may be missing or corrupt. After this initial cleansing and formatting of the following dataset, the horizontal complexity imposes some preliminary feature selection to guide the test. The suggested proposed system has been tested in the lab and compared to a CNN and RNN-based IDS. The data set for the proposed methodology is used for the ratio of 70% and 30%. The 70% is used for training and the other 30% is used for testing. The developed scheme analyzes the dataset for features and distinguishes between attack and normal conditions.  
This is one of the latest IIoT datasets, which is widely used to assess the effectiveness of cybersecurity systems based on machine learning. There are a total of 257,673 samples in this dataset, with 164,673 inaccurate results and 93,000 normal values. There are 49 features and 9 types of threat in this dataset. Following are the types of attacks such as backdoor, analysis, reconnaissance, exploit, generic, fuzzer, DoS, worm, and shellcode assaults. The authors in [12] identify the network size of a packet, IP addresses of both source and destination, ports, set of rules, and so on, that can be used to forecast harmful malicious activity. The UNSW-NB15 dataset is used in the cited paper to train and test their model. For creating their model and assessing predictability, the four machine learning classifiers are used. The results revealed that the machine learning classifiers' decision tree has better results, with a 93 percent accuracy rate. Some of the datasets given did not prefer a realistic testbed, and some of the threat scenarios were not varied. The model requires the following dataset for the tests due to its attributes such as regular updates, substantial attack heterogeneity, and integration of the traffic generated by IoT. To begin, both the training and testing datasets are loaded and the fundamental libraries needed to run the program are imported. The dataset is checked for missing values during the preprocessing step. The resulting dataset is well organized, therefore, there are no null values in the dataset. As a result, utilizing functions separates the different attacks from normal traffic data. The fundamental problem with this particular dataset is that data entries of the malicious traffic are much larger than the regular traffic data entries. The legitimate and malignant traffic data pieces were reduced to a 70-30 ratio as a result of the trimmed or pruned data of any attack. As a result of huge positive and few negative examples during training in binary classification, the model will prefer to forecast. In that situation, the model is quite accurate.
## 4 Why and How to Measure Volatile Organic Compound Emissions from a Material?
### How to Sample and Analyse VOCs
#### 4.1.1 VOC Sampling

Table 3: _Cont._* a solid-phase microextraction (SPME) fiber;
* a stir bar sorption extraction system (SBSE);
* a passive dosimeter containing a sorbent, as previously mentioned, with various ge-ometries (Radiello tube, badge G.A.B.I.E, 3M type 3500, PerkinElmer tube). The choice of sampling method depends on the matrix under study and the sensitivity of the detection equipment. If the detection equipment is sufficiently sensitive, sampling without preconcentration may be sufficient for the sample to be directly analyzed. On the other hand, if matrix is very low concentrated in VOCs, it is preferable to consider a preconcentration step. It may also be possible to sample compounds without preconcentration, then concentrate them a posteriori by sorption (e.g., sampling in a bag and adsorption on SPME fiber). For some VOCs, it is necessary to go through a derivatization stage before analysis, during sampling. This is particularly the case for certain carbonyl compounds [96]. Derivation consists in grafting a group visible with conventional detection methods onto non-detectable compounds. There are a number of different derivatization methods, as well as various derivatization agents. For carbonyl compounds, 2,4-dinitrophenylhydrazine (DNPH) and O-(2,3,4,5,6-pentafluorobenzyl)hydroxylamine (PFBHA) are frequently used. If analytes are sampled using a SPME fiber, the derivation can be carried out either in the sampling matrix or directly on the SPME fiber. If the analytes are sampled on an adsorbent cartridge, the derivatization agent is located directly on the cartridge (DNPH-impregnated silica gel cartridges). There may be artefacts linked to the presence of water or ozone in the sampled atmosphere. [PERSON] et al. [97] demonstrated that the presence of ozone in samples taken from canisters led to a decrease in the concentration of certain unsaturated reactive compounds such as ethylene and propylene. This can also be observed in the study of terpene compounds collected on Tenax TA(r) cartridges [98]. [PERSON] et al. [99] also demonstrated that there is a loss of compounds on Carbotrap/Carbosieve adsorbent cartridges when these are exposed to humidity (90% relative humidity). It should be noted that emissions from a material can also be studied by thermodesorption and pyrolysis analysis [100; 101].
# Construction and Application of an Ecological Quality Evaluation System Based on a PIE-Engine +
## 3 Results and Discussion
### Change Analysis of ETI in the Taihu Basin  
The overall change in the ETI in the Taihu Basin was significant (Figure 6). From 2001 to 2009, it experienced a fluctuating decline, with the ETI at 0.465 in 2001 and reaching its lowest point of 0.434 in 2009, reflecting a decrease of approximately 6.6%. The ETI gradually increased from 2009 to 2020, reaching 0.492 in 2020, which was approximately 12% higher than in 2009, with an increase of less than 0.06. Land use change is the most important threatening factor to the quality of the habitat. From 2001 to 2009, the cropland in the Taihu Basin decreased by around 6700 km\({}^{2}\), while the grassland increased by approximately 6300 km\({}^{2}\), and the impervious surface expanded by about 2600 km\({}^{2}\). The increase in grassland is the main reason for the decrease in ETI during this period. From 2009 to 2020, cropland in the Taihu Basin expanded by approximately 900 km\({}^{2}\), while the grassland decreased by about 3500 km\({}^{2}\) and the impervious surface increased by about 2300 km\({}^{2}\). Therefore, the reduction in grassland and the expansion of urban space emerged as the primary factors contributing to the increase in ETI.
## 2 III-V-on-Silicon Integration Technology
### III-V on Si Device Processing Technology  
The processing of the 200 mm silicon-on-insulator wafers is carried out in imec's CMOS pilot line. Several etching depths are available, depending on the device requirements. Fiber-to-chip grating couplers are typically used to interface with the photonic integrated circuit, allowing for wafer-scale measurements. After waveguide processing, the wafer is covered by a thick SiO\({}_{2}\) layer, and the subsequent chemical mechanical polishing process planarizes the wafer, leaving about 20 nm SiO\({}_{2}\) above the silicon waveguide.  
After bonding of the III-V substrate onto the silicon PIC as described in Section 2.1, the III-V substrate is removed. Since most device demonstrations in this paper are based on InP-based III-V layer stacks, in this section we will elaborate on the process flow for InP-based heterogeneously integrated devices. The process flow is schematically described in Figure 2. The InP substrate is removed using HCl:H\({}_{2}\)O (3:1, 40 \(\,\mathrm{\SIUnitSymbolCelsius}\)) (Figure 2c) until the InGaAs etch stop layer is reached. After etching the InGaAs sacrificial layer using H\({}_{2}\)SO\({}_{4}\):H\({}_{2}\)O\({}_{2}\):H\({}_{2}\)O (1:1:18), a SiN layer of 200 nm is deposited on top of the p+ contact layer by PECVD. The III-V waveguide pattern is transferred into this SiN hard mask by optical lithography and reactive ion etching (RIE) (Figure 2d). Then, the pattern is further etched into the p+ contact layer by ICP-RIE (Figure 2e). Since the designed coupler (see Section 3.1) between III-V and silicon waveguides requires narrow III-V tips (500 nm), wet etching with HCl:H\({}_{2}\)O (1:1) is used to etch the thick p-InP layer, forming an inverted-trapezoid-shaped waveguide that has a much smaller waveguide width at the bottom (Figure 2f). Once again, a thin layer of SiN (\(\sim\)200 nm) is deposited and patterned (by optical lithography and RIE) to protect the III-V waveguide while exposing the active III-V layers from two sides (Figure 2g). Etching of the active layers is carried out using H\({}_{2}\)SO\({}_{4}\):H\({}_{2}\)O\({}_{2}\):H\({}_{2}\)O (1:1:10) (Figure 2h). Ni/Ge/Au contacts are evaporated onto the InP n+ contact layer by a lift-off process (Figure 2i). In order to electrically isolate the devices, the n+ contact layer between neighboring devices is etched using HCl:H\({}_{2}\)O (1:1) (Figure 2j). A photoresist mask is used to protect the III-V waveguide during this etching process. Afterwards, DVS-BCB is spin-coated on top of the wafer to planarize the top surface (Figure 2k). Etch-back using RIE thins down the DVS-BCB layer thickness to expose the SiN hard mask on top of the p+ contact layer (Figure 2l). After removing the SiN layer by RIE (Figure 2m), the metal p-contact layer (Ti/Au) is formed on top of the p+ contact layer by lift-off (Figure 2n). Optical lithography and RIE etching are used to etch deep vias into the DVS-BCB layer down to the n-metal contact (Figure 2o). As the final step, Au is deposited on top of the p and n metal contacts by lift-off or plating for probing (Figure 2p).
## 1 Introduction  
The resistance of common pathogens to standard antibiotic therapies is rapidly becoming a major public health problem all over the world [1], and consequently, there is a need to develop new structural and mechanistic classes of antibiotic agents. In this regard, the development of new antibiotics inspired in natural product scaffolds seems the best short-term solution to address antibiotic resistance [2].  
The Celastraceae family is distributed mainly in tropical and subtropical regions of the world including North Africa, South America, and East Asia, and their species have a long history in traditional medicine [3]. The most representative genus in this family is _Maytenus_, with more than 225 species [4]. In the Amazonian region, species of this genus are well known for their use in the treatment of rheumat, gastrointestinal diseases, and as an anticumoral for skin cancer [4]. The therapeutic potential of _Maytenus_ species has been mainly attributed to celastroloids, chemotaxonomic markers of the family [5]. The term celastroloid refers to methylenequinone nor-triterpenes with a 24-nor-\(D\):\(A\)-friedo-oleanane skeleton. Celastrol [6] and pristimerin [7] are the first and most frequently reported celastroloids, and later on, this term was extended to related phenolic nor-triterpenes [5,8] and their dimer and trimer congeners [9]. This particular class of natural products shows a wide range of bioactivities, including cytotoxic [10; 11], anti-inflammatory [12], antioxidant [13], antiparasitic [14], and insecticidal [15] properties. Concerning their antimicrobial activity, pristimerin, tingenone, celastrol, and netzahualcoyone (Figure 1) exhibit inhibitory activity against Gram-positive bacteria [8; 16; 17], and the mode of action of netzahualcoyone against _Bacillus subtilis_ and _Escherichia coli_ has been investigated [18; 19]. Regarding the phenolic nor-triterpenes, studies on their antimicrobial activity, mechanism of action against Gram-positive bacteria, and preliminary structure-activity relationship have been reported [20; 21; 22; 23; 24; 25].  
As part of an intensive investigation into antimicrobial metabolites from Celastraceae species, we report herein on the minimal inhibitory concentrations (MICs) of five natural phenolic nor-triterpenes (**1-5**) and four derivatives (**6-9**), three of them reported for the first time, against Gram-positive and Gram-negative bacteria, and the yeast _Candida albicans_. The structure-activity relationship study of compounds **1-9** was expanded by the known antimicrobial activity of a series of phenolic nor-triterpenes (**10-26**), previously reported by our research group [20; 21; 22; 23; 25], to deepen our knowledge of the structural requirements for their activity.
# Signature Split Method for a PQC-DSA Compliant with V2V Communication Standards
## 2 Preliminaries  
### Post-Quantum Cryptography  
Postquantum cryptography is a next-generation public-key cryptographic system, an alternative to existing public-key cryptography, whose hardness is threatened by the quantum-circuit algorithm presented by [PERSON] in 1994. The NIST is holding competitions in two fields, public key encryption/key encapsulation mechanism (PKE/KEM) and digital signature algorithm (DSA), to standardize secure PQC in the quantum computing environment, and the algorithms targeted for final standardization have now been announced. The cryptographic scheme used in V2 Verifier, the target of this paper, is a DSA, and the final standardized algorithms are Crystals-Dilithium [8] and Falcon [9], both of which are lattice-based algorithms. Table 1 shows the parameters of the two algorithms.  
The shortest vector problem (SVP) problem is known as the NP-hard problem, and in the case of the problem in the NTRU lattice used by Falcon, it can be thought of as an instance of finding a nonzero short vector in the NTRU lattice, assuming that the NTRU lattice contains short vectors. The learning with errors (LWE) difficulty has security based on reduction to SVP. That is, it belongs to lattice-based encryption at a higher level. The LWE problem can be viewed as solving a linear system of \(Z_{q}\) to which a secret (noise) vector is added. When there is no noise in \(Z_{q}\), it can generally be easily solved using Gaussian elimination, but when there is a noise, the difficulty increases. In general, the LWE problem constructs the linear system as a matrix and the elements of the matrix as polynomials (module LWE). This structure leads to the advantage that the internal operation is the same regardless of the security level. Therefore, for the public matrix \(\mathbf{A}\), the secret vector \(\mathbf{s}\), and the noise \(\mathbf{e}\), (\(\mathbf{A}\), \(\mathbf{b}=\mathbf{As}+\mathbf{e}\)) and uniform random numbers (\(\mathbf{A}^{\prime}\), \(\mathbf{b}^{\prime}\)) are computationally indistinguishable. Security proofs of Crystals-Dilithium lean on the learning with errors(LWE) scheme. Falcon [9] is a PQC-DSA with a small key size and signature size based on the NTRU lattice, and the security proofs of Falcon lean on the short vectors problem (SVP).
## 2 System Design and Principle  
### System Design  
The photon-counting UOWC system based on deep learning is shown in Figure 1. The sending end performs On-Off Keying (OOK) modulation on the data to be sent and then loads it on the drive circuit, which will drive the LED to turn on and off. The light signal emitted by the LED passes through a collimating lens, which converts the originally divergent light signal into a parallel light signal, and then passes through the underwater channel. Due to the influence of absorption and scattering, the light signal is very weak while arriving at the receiving end. The receiving end uses a focusing lens to focus the optical signal into the SPAD. The discrete single photon pulse signal output by SPAD passes through a starting position determination module, and then a suitable starting sampling point is found. At the same time, the sampling module starts sampling the discrete single photon pulse output by SPAD, and the sequence after sampling is converted into binary sequence; then, the binary sequence is input into the previously trained neural network. The trained neural network extracts the feature of the binary sequence and recovers the time slot synchronous clock. Finally, the baseband signal is recovered according to the time slot synchronous clock.