esa-sceva/satcom-chunk-collection huggingface dataset:
# The Impact of the Human Factor on Communication During a Collision Situation in Maritime Navigation
## 4 Discussion  
The presented measurements were based on 20 maritime traffic collision scenarios. The model was implemented and verified by using selected maritime collision scenarios, which were repeatedly modified and replicated to obtain a significant number of statistical tests.  
For each scenario, messages produced by the navigator (human voice) and by means of speech synthesis techniques were used and transmitted by the radio voice communication methods available onboard the vessels. The above sentences were then transformed by the prototype model from natural language into ontology, as described in this article. Our model performed a linguistic analysis of the sentence by identifying the parts of the speech. In the subsequent steps, the model matched the selected parts of the speech with specific items in the communication or navigation ontology using classification models trained by means of machine learning methods.  
The prototype software successfully executed the translations for the communication scenarios loaded for testing. The introduced metaontology and the objective function performed well in completing the task. The authors will continue to explore and widen its applications, e.g., for land and air modes of transport.  
The accuracy results by presented the transformation model in terms of the overall perspective specified by the total accuracy rate coefficient yielded around 90%. The total error rate coefficient, which indicates the level of error, was about 10%. The receiver operating characteristic (ROC) curve also showed high accuracy for the training set that was significantly over the average line. In brief, this means that satisfying accuracy results were reached by using the proposed transformation model in terms of the presented accuracy coefficients. Because the domain of application of the method is critical for the safety of maritime traffic, it is necessary to continue research on the presented model to increase its overall accuracy.  
Natural language processing using artificial intelligence, i.e., enabling a computer to understand, use, and generate human language externally, is already in practice. The transformation of natural language and associated processes are used in various fields of knowledge [41; 42; 43; 44; 45]. The authors focused on the possibilities of combining ontology with natural language in this study. Due to the safety requirements in maritime transport, NLP can be implemented through an automatic communication system, as proposed by the authors [46]. Ontology as a structure contains a set of concepts and their interrelationships in a specific area, which can help to resolve and standardize terminology, which is key to the connection between systems and entities and the connection in the transport chain [47]. Further work will be aimed at developing and improving the developed models and operating algorithms. Our goal is to access sources that can be connected to the level of maritime safety.
# Unsupervised Offline Changepoint Detection Ensembles
## 2 Materials and Methods
### Numerical Experiment  
During the experiment, we compare the CPD procedure with a single cost-function inside (non-ensemble approach) and the proposed CPDE procedure (ensemble approach). The experiment setup in parts of cost functions, scaling functions, aggregation functions, and search methods is shown in Figure 5. We selected for the experiment not all common aggregation functions because some of them are not suitable for the provided algorithms or relate to data-centered ensembles (_FeatureBagging_), some need more cost functions to be used or at least more scores to be generated for proper use (_AverageOfMax_, _MaxOfAverage_), and some duplicate the result of selected ones (_Average_, _WeightedAverage_, _MajVote_). Selection of cost functions was based on the recommendations from [19; 22]. The motivation for using selected cost functions under a single ensemble comes from a desire to avoid the time-consuming and laborious process of proper model selection. Furthermore, in most engineering applications, various signals are generated from various statistical models. Likewise, a single signal may have various normal operation states generated by various models. We find a combination of different cost functions useful for diagnostics tasks in technical engineering systems that is one of the possible applications of our research. We do not use a constraint because the number of changepoints for the experiment is assumed to be known. For our experiment, the number of changepoints is equal to 1 for TEP and 4 (for most datasets) for SKAB.  
Figure 5: The numerical experiment setup for CPD ensemble and non-ensemble approaches.
# Optimal CNN-Hopfield Network for Pattern Recognition Based on a Genetic Algorithm +
## 3 Recall of the Hopfield Neural Network  
One of the well-known recurrent neural networks is the Hopfield network (HNN) [25; 26; 27; 28; 29]. This NN can be seen as an associative memory and has been applied in a large number of applications, such as classification [23; 30], optimization [31; 32; 33], image processing [34], control [35], and solving blind detection problems [36; 37].  
A Hopfield network consists of a set of interconnected neurons \(N\) that update their activation values asynchronously and independently of other neurons. A neuron \(i\) is characterized by its state \(S_{i}=\pm 1\). The principle of HNNs is to save binary patterns of the form \(\{+1,-1\}^{N}\), and then to use a rule, called Hebb's rule, to learn them. In the inference phase, these patterns are then predicted via a noisy input vector. Its robustness to noise is very interesting in several kinds of applications. The \"energy\" of the Hopfield networks is defined by:  
\[E=-\frac{1}{2}\sum_{i,j}^{N}S_{i}S_{j}w_{ij} \tag{1}\]  
where \(w\) is the weight associated with neurons \(i\) and \(j\). \(S_{i}\) is the state of neuron \(i\). This quantity is considered as a Lyapunov function; it remains stable or decreases when the network states are updated. The HNN will converge to a local minimum in the energy function under repeated updating. So, the optimal values of the weights are those that minimize the energy function. Furthermore, the theoretical storage capacity of the HNN, under the assumption of the stability of all the patterns, is defined by [25]:  
\[P_{max}=\frac{N}{4\ln N} \tag{2}\]  
where \(P_{max}\) denotes the maximum number of uncorrelated patterns that can be stored in the \(N\)-neuron recurrent network [38]. Each pattern stored corresponds to a local minimum of the energy defined in (1). Recently, many works proposed new learning rules and new energy functions that improved Hopfield networks' properties. The storage capacity was  
Figure 2: Overview of our hybrid CNN–RNN architecture. (**a**) training process; (**b**) inference process.  
about \(0.138\times N\)[32; 33; 39]. In [40], \(N\) patterns could be stored when the learning rate was not related to [PERSON]'s rule. The use of new energy functions, such as interaction functions of the form of \(F(x)=x^{n}\), as in [21; 41], provided a storage capacity proportional to \(N^{n-1}\). A modern energy function based on interaction functions of the form of \(F(x)=exp(x)\) yielded a storage capacity of \(2^{N/2}\) in [22]. In [24], the authors proposed a new update rule that ensures rapid global convergence, and they generalized the energy function of Demircgil [22] to continuous patterns; thus, the HNN became differentiable and could be integrated into deep learning architectures. In this case, the storage capacity of this model became proportional to \(c^{\frac{N-1}{4}}\) (for c = 1.37 and c = 3.15).
## 3 Model Validation and Results
Similarly, a higher alpha factor naturally results in stronger spectral broadening due to SPM (as mathematically implied by the phase of the electric field in Equation (3)), which in turn helps the BFL exploit this effect and subsequently overcome the pattern-dependent performance limitations of the directly modulated RSOA more efficiently. Finally, the optimization of the RSOA-BFL design could be assisted by employing genetic algorithms for parameters extraction [35] and alternative metrics for performance evaluation [7]. Figure 11: Error Probability (\(EP\)) vs. RSOA direct modulation data rate at the RSOA and BFL output: (**a**) experimental and (**b**) theoretical. \(EP\) limit is set to \(10^{-9}\). In post-filtering techniques that are implemented with optical modules, such as the BFL, and used to enhance the direct modulation capability of RSOAs, an important figure-of-merit is the net gain (\(NG\)) [17]. The \(NG\) is the difference between the RSOA average gain (which hence incorporates the RSOA average output and input power) and the BFL reduction in the amount of amplification that the RSOA would provide if it were alone. When running the model, we found that the \(NG\) varies from 9.46 dB to 8.67 dB for a modulation range between 4 Gb/s and 11 Gb/s, respectively. These values imply that the encoded signal could travel inside a standard single-mode fiber at distances which lie within the reach of the target applications that exploit RSOAs as intensity modulators [26]. The practical needs of these applications can be sufficiently served by modulation rates in the order of 11 Gb/s at least for the midterm. Such applications, such as those concisely mentioned at the beginning of Section 1, exploit RSOAs' attractive features of their wavelength-agnostic, self-seeding, and uncooling operation to enable two-way fiber-based transmissions. Thus, in the downlink direction, a sender (for instance, a central office) sends lightwave-modulated data, which may also be an optical version of an RF signal, and these are first amplified by an RSOA before arriving at the receiver (for instance, at a subscriber premises or at a mobile base station). In the opposite uplink direction, the signal from a physical or hardware entity controls the injection current of the RSOA and through the (external) modulation of the latter is imprinted on a CW optical signal that was delivered together with the downstream data. In this manner, the end-users' information is contained in the upstream data that are returned to the sender, and the loop of the optical distribution network configuration is closed. Figure 12: Error probability vs. RSOA carrier lifetime. Figure 13: Error probability vs. RSOA alpha factor.
## 2 Results and Discussion  
### Aroma Profile of Different Aging Years of Baijiu Samples  
A visual investigation of the aroma profile characteristics of Baijiu samples (G-15, G-5, G-0) was conducted using nine recognized odor descriptors (Figure 1). Overall, the aged sample (G-15) exhibited significantly lower intensities in burnt and sour aromas compared to the younger sample (G-0), while higher intensities were observed in the remaining seven sensory attributes. Notably, G-15 demonstrated statistically elevated intensities in fruity, sweet, jiao-aroma (cellar-derived aroma), and floral notes relative to G-0, accompanied by a marked reduction in burnt intensity. To elucidate the underlying causes of aroma divergence among samples with varying storage durations, a comparative analysis of flavor compounds was performed using comprehensive Aroma Dilution Analysis combined with Gas Chromatography-Olfactory (cADEA-GC-O).
## 4 Results and Discussions
#### 4.1.2 Experiment with 2 Hardware and 2 Software Routers  
To research the packets processing time by A-B routers in the experiment a network of two Cisco 2801 routers was configured and they were connected to a traffic generator installed on a personal computer with two network interface cards to calculate the difference between the moments of sending and receiving each package (Figure 15). A similar experiment was conducted with the two developed software routers (Figure 16).  
Comparison of the average packets delay caused by the two Cisco 2801 hardware routers and two software routers is shown in Figure 17. Comparison of the probability density functions of packets processing time caused by the two hardware and two software routers is shown in Figure 18.  
Figure 16: Experimental scheme for determining the packets processing time by two software—based routers.  
Figure 15: Experimental scheme for determining the packets processing time by two Cisco 2801 hardware routers.  
In the experimental research it is found that transmission of the same traffic over two hardware routers has an average packet delay \(D_{HR(A-B)_{2}}\) of 805.4 us, while the average packet delays over two software routers \(D_{SR(A-B)_{2}}\) is 803.46 us. As we can see in the comparison of packets delays the measured difference between the hardware and software routers is approximately 1.94 us, the same as in the first experiment. However, it is necessary to focus on the average packets delay at the first and second experiments, because the value of the delay caused by hardware routers during two experiments differs by \(805.4-695.91=109.49 us\). As a result, it was found that the packet processing time caused by only one router of the CISCO 2801 series is 109.49 us (\(d_{proc.}+d_{queue}\)), and the packet delay of 586.42 us is caused to the computer's operating system when generating traffic and depends on its performance (\(d_{frag.}+d_{trans.}+d_{prop.}\)). From Figure 20 we can see that the probability density function of packets processing time of the software routers approaches that of the hardware routers of Cisco 2801 series.  
Figure 17: Comparison of the average packets delays caused by two Cisco 2801 hardware routers and two software routers.  
Figure 18: Comparison probability density functions of packets delays caused by two hardware and two software routers.
## 3 Results and Discussion
### Profiles of VOCs  
According to Table 3, a total of 31, 27, and 29 VOCs were detected in the three SEY samples treated by steaming, baking, and microwaving, respectively. These VOCs included 15 aldehydes, 9 pyrazines, 3 alcohols, 2 esters, 6 hydrocarbons, and 6 acids, as well as one  
Figure 2: Multi-sensory analysis of SEY. PCA of E-nose (**a**); DFA of E-nose (**b**); PCA of E-tongue (**c**); Aroma profile by quantitative descriptive analysis (**d**). each of ketone, furan, thiazole, and sulfide. Notably, the baking group exhibited higher levels of acids and alcohols compared to other groups, whereas no alcohols were found in the microwaving group. Acids generally had a high flavor threshold with limited impact on flavor in SEY. It has been suggested that acetic acid is produced as a by-product during the breakdown process of long-chain fatty acids [40]. In addition, hexanoic acid was detected, which can be attributed to the enzymatic synthesis of UFAs via the lipoxygenase pathway. Alcohols mainly originate from the thermal oxidation of lipids and the degradation of carbohydrates with vegetal and aromatic odors [41]. 1-Octen-3-ol was a common compound present in traditionally prepared SEY but not detected during microwave heating.
# Design, Implementation, and Field Testing
## 3 System Design
### System Design Rationale
#### 3.2.3 Realtime Alert Design  
On detecting a non-compliance activity, the event is delivered immediately to the smartwatch worn by the nursing assistant, which will generate a vibration alert with a companion text display on the watch's screen. The vibration is discreet in that other nursing assistants or the supervisor will not notice. We hypothesize that this form of alert will be more acceptable by users, which is confirmed by the participants in our field study survey.
### Design of the HF-Band RFID System
#### 1.2.5 Identifier  
The most common form of ID is a plastic card or key ring (Figure 4).  
It consists of a coil embedded in plastic and an electronic circuit (chip), the task of which is to encode the data stored in its memory and keying the coil based on it. Identifiers in the HF-band RFID system usually do not have their own power source. They are powered by an electromagnetic field generated by the reader [10; 11; 12]. The most common integrated circuits are the NTAG213, NTAG215 or NTAG 216 family with a total memory of 180 to 924 bytes [13; 14; 15]. The memory structure of this chip is shown in Figure 5.  
Figure 4: Example TAGS identifiers: (**a**) RFID card, (**b**) key ring, (**c**) sticker.  
Figure 5: Structure of data stored in the identifier layout (NTAG213).  
The data from the system are output serially, but unlike LF-band RFID systems, the identifier is waiting for the reader to send a query. Before sending, the data are usually encoded in the Manchester system [16] shown in Figure 6.
# Improving Adversarial Robustness via Distillation-Based Purification
## 4 Evaluation
### Results and Analyses  
First, to evaluate the superiority of our proposed purifier, we purified the AEs generated by the adversarial attacks described above, and then fed the purified images into a pre-trained classification model, ResNet56, to measure the classification accuracy. The accuracy of this classification model on CIFAR-10 is 89.46%. We employed NRP [29] and SOAP [30] as baseline purifiers for comparison.  
Table 1 reports the experimental results. The proposed purifier generally performed satisfactorily against the gradient-based attacks PGD, FGSM, BIM, and AA. However, it performed slightly worse than SOAP against the PGD16 (\(\epsilon=\frac{16}{255}\)) and the BIM attacks, which add slightly stronger noise than PGD8 which was used for training. We also observed that our purifier did not perform well on samples subjected to CW attacks, which is likely due to the fact that CW is a different type of attack than the gradient-based attack used in the adversarial training of the AT teacher model.  
Next, we performed an ablation study. Table 2 reports the results. The last row of the table is the proposed method (training two teacher models, AT and NT, and distilling their knowledge to our purifier model), and the two rows above it are versions of distilling the knowledge of only one teacher model, AT or NT, to the purifier, respectively. Finally, the first row is a purification method using only adversarial training without knowledge distillation. Through this experiment, we aim to gain insight into whether the performance of the purifier (student) model changes depending on the number of teacher models used and their learning strategies.  
The experimental results show that the proposed method generally performs best, and that using only one of the two teacher models or no knowledge distillation leads to lower performance. In particular, distilling the knowledge of the NT teacher model resulted in good performance, which suggests that the knowledge of image restoration is helpful in the purification task. However, the NT teacher model's knowledge alone was not sufficient to improve adversarial robustness of the student model, and we found that ensemble knowledge distillation from both AT and NT teachers was most effective.
# Characterization and Optimization of a Conical Corona Reactor for Seed Treatment of Rapeseed
## 2 Materials and Methods  
### Plasma Source  
The reactor used in this work was a modified version of the corona plasma jet device with a conical nozzle reported by [32]. In contrast to the previous version, here, the plasma jet horn nozzle was placed directly on a glass Petri dish leaving no open gap between them, thus forming a virtually closed (but not air-tight) system. The schematic setup of the reactor is presented in Figure 1. It consists of an upside-down borosilicate glass funnel (80-mm-diameter and angle of 60\({}^{\circ}\)) mounted on top of an 80-mm-diameter glass Petri dish where seeds were placed for treatment. A tungsten pin electrode (1 mm thick and 10.3 mm long) was inserted in the straight part of the funnel, as shown in Figure 1. The pin passes through an insulating piece and is connected to a high voltage generator (AL-1400-HF-A, Amp-Line, West Nyack, NY, USA). Below the Petri dish, a 5.85-mm-thick glass plate was used as an additional dielectric layer under which a grounded electrode was located. Argon (4.0 slm) was introduced on the top part along with the pin electrode. When high voltage was applied, the electric field at the electrode's tip increased and discharge filaments (streamer corona discharge) were formed. The filaments tended to be homogeneously distributed throughout the horn volume and at higher voltage they stretched out, reaching the bottom of the Petri dish. The reactor was operated with a frequency of 10 kHz and the voltage was amplitude modulated (burst mode) into bursts with a repetition period of 100 ms. The applied voltage amplitude and/or signal duty cycle were varied during the experiments in Section 3.1.  
Figure 1: Experimental setup of the funnel reactor used in this study (**A**). Details of the discharge characterization setups for temperature and OES measurements are shown in (**B**).
## 1 Introduction
* Protective Competition: Larger market players may possess sufficient resources to outcompete smaller incumbents, potentially leading to a concentration of market share. Regulators must remain vigilant when designing auctions to ensure that monopolistic behaviors or non-competitive strategies are not employed by these players. * Spectrum Fragmentation: Auctions may result in incumbents owning licenses to fragmented slices of spectrum across different frequency ranges. This fragmentation can complicate network implementation and hinder interoperability. * Technological and Regulatory Uncertainties: The increasing complexity of available services and technologies makes it particularly challenging to anticipate the needs and obligations of incumbents. Regulators must balance the need for flexibility with the assurance of stability with respect to spectrum rights. Spectrum auctions are a critical tool for managing a limited and valuable resource. Grounded in game theory and economics, as discussed in works such as [4; 5]), auctions can facilitate efficient allocation, generate substantial revenue, and ensure that the advantages of spectrum services are fully realized. This paper evaluates and compares the effectiveness of primary auction structures, analyzing both their theoretical efficiency and their actual revenue outcomes according to country and format. Section 2 of this document introduces the historical aspects of auctioning and focuses on the structural weaknesses that prompted advancements in new auction formats. Section 3 provides a theoretical overview of the primary auction formats employed in spectrum auctions, along with the potential challenges that may arise and the most effective solutions currently available. Section 4 compares three major auction structures in terms of allocation efficiency and provides empirical comparisons of auction performance over the past 24 years. It also examines revenue, efficiency of allocation, and the various types and options implemented by different countries and regulators in North and South America. Section 5 discusses the conclusions drawn by the authors.