esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
The contribution of this paper focused on two aspects. Firstly, we derived the sensor data rate required for the safe overtaking by considering the driving path and comfortable braking and showed that as the velocity became higher, the required sensor generated data rate increased drastically. At a low velocity, the effect of considering the driving path became dominant, and at a high velocity, comfortable braking became dominant. Secondly, techniques for cooperative perception with 30 and 60 GHz millimeter-wave communication made it possible to safely overtake at a high velocity such as around 50 and 65 km/h due to the availability of sharing a large amount of sensor information in real time. From this analysis, we concluded that, considering the actual data rate, using 60 GHz for cooperative perception is a promising way to perform safe overtaking at a high velocity. Finally, since the recognition process is classical, future tasks are the adoption of global or local descriptors for the recognition process.  
Conceptualization, K.S.; methodology, R.F.; software, R.F.; validation, K.S. and R.F.; formal analysis, R.F.; investigation, R.F.; resources, K.S.; data curation, K.S.; writing--original draft preparation, R.F.; writing--review and editing, R.F.; visualization, R.F.; supervision, K.S.; project administration, K.S.; funding acquisition, K.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
V2V Vehicle-to-Vehicle  
V2X Vehicle-to-Everything  
3 GPP 3 rd-Generation Partnership Project  
5 GAA 5G Automotive Association  
ETSI European Telecommunications Standards Institute  
ITSs-S Intelligent Transport Systems-Station  
FOV Field-Of-View
# From Phase Space to Non-Equilibrium Dynamics: Exploring [PERSON]'s Theorem and Its Implications
## 3 Out-of-Equilibrium Dynamics
### Non-Negativity and Constraints of the Extended Entropy \(\overline{S}\)
#### 3.6.2 Energy Constraints and Positivity of \(S^{(a)}\)  
The entropy contribution \(S^{(a)}(X^{(a)})\) for subsystem \(a\) is non-negative if its argument \(X^{(a)}=E^{(a)}-[\text{kinetic/potential terms}]-U^{(a)}_{\text{mce}}\) satisfies \(X^{(a)}\geq 0\). Physically, this requires the following:  
\[E^{(a)}\geq\frac{(p^{(a)})^{2}}{2n^{(a)}}+\frac{(J^{(a)})^{2}}{2I^{(a)}}+q^{(a )}V^{(a)}-q^{(a)}(\mathbf{A}^{(a)}\cdot\mathbf{v}^{(a)})+U^{(a)}_{\text{mce}}, \tag{14}\]  
which holds if \(E^{(a)}\) represents the _total energy_ of subsystem \(a\), including all kinetic, potential, and external work terms. This ensures \(S^{(a)}(X^{(a)})\geq 0\), as entropy is a monotonic function of energy.
## 5 Types of Phage-Based Biosensors
### Phage-Based Optical Biosensors
#### 5.1.1 Surface Plasmon Resonance Sensors  
Surface plasmon resonance (SPR) sensors are optical sensors that use distinct plasmon electromagnetic waves to detect (quantify) analytes based on molecular interactions with the biosensor. SPR biosensing, as a spectroscopic method, allows the real-time and quantitative detection of the binding agents or molecules freely without any kind of labeling [7]. The optical system of this type of biosensor consists of a light-emitting diode (LED), a photodiode array, a glass prism, and an optical surface. The molecular networking at the surface of this sensor drives angular changes in the reflected light, which changes the refractive index (Figure 4). The photodiode array detects the shift in angle and provides the result as a response unit (RU), which is equivalent to the whole mass of the bound ligands [75]. Foodborne microbes can be detected using binding proteins from bacteriophages and the phages themselves, which are incorporated into the SPR sensor system as biosensors. For instance, [PERSON] and colleagues utilized the tail spike protein of an engineered phage (P22) immobilized onto a gold surface for the accurate and fast detection of _Salmonella_ with a sensitivity of \(10^{3}\) CFU/mL [52]. [PERSON] et al. isolated a novel bacteriophage, KFS-SE2, from an eel farm for the detection of _Salmonella_ Enteritidis on a food sample using the SPR platform. However, detailed information about its application in food has not been demonstrated [17].  
Figure 4: Schematic diagram illustrating the working principle of a surface plasmon resonance sensor (SPR) using bacteriophages.  
[PERSON] and [PERSON] developed a novel 6 HN-J-functionalized SPR biosensor comprising a segment of tail fiber protein derived from the lambda phage. This biosensor provided the fast, label-free detection of _E. coli_ K-12 in the range of 2 \(\times\) 10\({}^{4}\)-2 \(\times\) 10\({}^{9}\) CFU/mL and showed a lower detection limit of 2 \(\times\) 10\({}^{4}\) CFU/mL within 20 min [76]. However, the researchers reported a nonspecific binding with _P. aeruginosa_. The SPR sensor has also been shown to be efficient in the detection of methicillin-resistant _S. aureus_ (MRSA), _E. coli_ O157:H7 [77], _E. coli_ K12, _S. aureus_[78], and hepatitis B virus (HBV) [79]. _S._ Typhimurium has been detected by an SPR device prepared via the immobilization of full-length engineered Det7 phage tail proteins (Det7T) on gold-coated surfaces by amine-coupling. This platform was able to detect _S._ Typhimurium quickly (within \(\sim\)20 min) with a detection limit of 5 \(\times\) 10\({}^{4-5}\) CFU/mL in 10% apple juice and water [80].
## 6 Defenses against Model and Data Attacks
### Blockchain  
Blockchain technology plays a crucial role in enhancing security within FL environments [116; 117; 118]. It serves as a decentralized and immutable ledger that securely records all the data exchanges and model updates between participating entities, ensuring transparency and accountability [119]. Smart contracts, which automate tasks like model aggregation and verification, operate without relying on a central authority [120]. This decentralized approach mitigates the risk of single points of failure and deters malicious actors from corrupting the system [49]. By leveraging blockchain, FL safeguards against threats such as data tampering and adversarial attacks, ensuring the verifiability of contributions and prompt identification of anomalies [121]. This integration significantly bolsters the security and reliability of collaborative learning environments, addressing the inherent challenges of transparency and trust in distributed settings. The schematic representation of this process is shown in Figure 12.  
For instance, in the paper by [PERSON] et al. [122], a blockchain-based anomaly detection framework for FL was proposed to safeguard against model poisoning attacks. Incorporating a blockchain network enables identification and filtration of malicious or low-quality local model updates from compromised participants. Updates undergo validation using datasets and an accuracy threshold before aggregation, ensuring only high-quality contributions impact the model. Moreover, the framework employs incentives to deter malicious behavior, enhancing anomaly detection and protecting against accuracy compromise.  
Similarly, [PERSON] et al. [123] introduced Block-FeST, a blockchain-based federated split learning framework for anomaly detection. This framework integrates FL and split learning using Transformers to maintain data privacy and address client resource constraints.  
Blockchain orchestrates FL and split learning processes, ensuring model hash transparency and immutability. A smart contract on the blockchain coordinates training, generates audit trails, and prevents tampering, thereby enhancing the anomaly detection efficiency.  
Furthermore, [PERSON]'s work [124] introduced a defense framework for IoT device failure detection using blockchain-based FL. Blockchain anchors hashed client data, ensuring data integrity and privacy. Smart contracts incentivize client participation and transparently record contributions, effectively defending against data leakage and non-participation attacks.  
Moreover, [PERSON] et al. [125] proposed Hierarchical Blockchain-based Federated Learning (HBFL) for cyber threat intelligence (CTI) sharing. HBFL employs blockchain to host global model aggregation and monitor updates, ensuring security and privacy while facilitating collaborative intrusion detection model training.
# New Materials and Effects in Molecular Nanomagnets
## 4 Single-Molecule Magnets
prepared coordination compounds with three different spin carriers (2p, in addition to 3d and 4f), resulting in three different exchange interactions between each pair of spin carriers. By adding a 2p radical (Rad) to gain a three-dimensional Co\({}^{\Pi}\)Dy\({}^{\Pi\Pi}\)Rad derivative, they reached slow relaxation below 30 K and  
Figure 4: Molecular structures of the complexes containing (**a**) Co\({}^{\Pi}\)\({}_{2}\)-Er\({}^{\Pi\Pi}\)\({}_{2}\) and (**b**) Co\({}^{\Pi}\)\({}_{2}\)-Ln\({}^{\Pi\Pi}\)\({}_{2}\). Hydrogen atoms are omitted for clarity. Reprinted from [82], copyright 2020, with permission from Elsevier. attributed the improved magnetic properties mainly to the Co\({}^{\text{II}}\)-Rad interaction [93], while Zn\({}^{\text{II}}\)Dy\({}^{\text{III}}\)Rad showed a significantly lower energy barrier and the linear Co\({}^{\text{II}}\)Dy\({}^{\text{III}}\)Rad compound showed no SMM behavior at all [94]. A broad overview of the possible metal centers for single-molecule and single-ion magnets, from 3d and 5d to 4f and 5f, is given in a recent review by [PERSON] and [PERSON] [95]. [PERSON] et al. performed theoretical investigations that examined the under-barrier relaxation at high temperatures, which prohibited the use of SMMs at room temperature [96]. While anharmonic phonons were found to be responsible for the previously mentioned effect, spin-phonon coupling led to spin relaxation in the prototypical mononuclear SMM [(tpa\({}^{\text{Ph}}\))Fe]\({}^{-}\)[97,98]. Ab initio calculations of single-molecule magnets have been performed by [PERSON] et al. [99] and [PERSON] et al. [100]. For spin-based devices based on SMMs, controlling the spin relaxation is of the utmost importance. [PERSON] et al. reported on the effect of a non-linear to pseudo-linear change in the crystal field symmetry in a dysprosium complex, leaving the residual chemistry unaltered, and found a strong reduction in the tunnel splitting at very low temperatures in the milliKelvin range [101]. Switching the magnetic anisotropy reversibly was enabled in lanthanide complexes as a function of external magnetic field and temperature [102,103]. Redox-active tetrathiafulvalene (TTF)-based ligands, on the other hand, allowed for designing coordination lanthanide complexes in different oxidation states, resulting in different magnetic properties for such SMMs [104]. An overview of such TTF-based ligands is given in [105]. Another important parameter for future applications is spin relaxation barriers. In particular, high spin-reversal barriers of 413 cm\({}^{-1}\)[106] and 450 cm\({}^{-1}\)[107] were measured in the approximately linear Co\({}^{\text{II}}\) complexes (sIPr)CoNDmp (with sIPr = N-heterocyclic carbene and NDmp = arylimido ligand) and Co(C(SiMe\({}_{2}\)ONaph)\({}_{3}\))\({}_{2}\) (with Me = methyl and Naph = naphthyl group), respectively.
## 2 Imposed Loads
### Storm Surges
#### 2.2.2 Quasi-Static Wave Force  
Before 1975, the estimation of wave forces on a vertical wall was based on the deep-water theory of a standing wave by [PERSON] [7] or shallow-water breaking wave following [PERSON] [8]. In 1975, [PERSON] published a unified formulation to estimate the wave forces [9; 10]. The estimation of wave forces striking a vertical breakwater starts with the estimated or measured period of the wave regime in seconds (T\({}_{1/3}\)) and the maximum wave height (H\({}_{\text{max}}\)). The equivalent wavelength in deep water (Lo) is approximately calculated as follows:  
\[Lo=1.56T_{1/3}^{2} \tag{1}\]  
As the wave approaches the shore and the water depth reduces, the wavelength also reduces accordingly. The wavelength L at a water depth is determined from published tables based on the wave period T\({}_{1/3}\) of Goda [10]. Figure 9 shows [PERSON]'s notation of the breakwater geometry and estimated wave pressures. Equations (1)-(13) are from Goda, with the variables defined in the list of notations.  
\[P1=0.5(1+cos\beta)\Big{(}\alpha 1+\alpha 2 cos^{2}\beta\Big{)}\rho H_{Max} \tag{2}\]  
\[P2=\frac{P1}{\alpha sh(2\pi h/L)} \tag{3}\]  
\[P3=\alpha_{3}P1 \tag{4}\]  
Figure 8: Hydrostatic pressures under storm conditions.  
\[\alpha_{1}=0.6+\frac{1}{2}\bigg{[}\frac{4h/L}{sinh(4\pi h/L)}\bigg{]}^{2} \tag{5}\]  
\[\alpha_{2}=min\left\{\frac{h_{b}-d}{3h_{b}}\bigg{(}\frac{H_{Max}}{d}\bigg{)}^{2},\ \frac{2d}{H_{Max}}\right\} \tag{6}\]  
\[\alpha_{3}=1-\frac{h^{\prime}}{h}\bigg{[}1-\frac{1}{cosh(2\pi h/L)}\bigg{]} \tag{7}\]  
The elevation to which the wave pressure is exerted is given as follows:  
\[n^{*}=0.75(1+cos\beta)H_{max} \tag{8}\]  
The above pressure intensities are deemed not to change even if overtopping takes place.  
The uplift pressure (Pu) in Figure 9 assumes that the breakwater is installed on a rubble blanket above the seabed. For tidal range barrages, this will need to be an impermeable layer; the pore water pressure in the foundation material is likely to be the average sea level. The hydrostatic forces shown in Figure 8 are deemed to apply.  
The total horizontal wave force is given by the following equation:  
\[Ph=0.5(P1+P3)h^{\prime}+0.5(P1+P4)h^{*}_{c} \tag{9}\]  
The moment of this horizontal wave force about the base of the caisson is given as follows:  
\[M_{Ph}=\
icefrac{{1}}{{6}}(2P1+P3)h^{\prime 2}+\
icefrac{{1}}{{2}}(P1+P4)h^{ *}_{c}+\
icefrac{{1}}{{6}}(P1+2P4)h^{*2}_{c} \tag{10}\]  
In which  
\[P4=\left\{\begin{aligned} P1\bigg{(}1-\
icefrac{{h_{c}}}{{}_{{}_{ \prime}}}&{}_{n^{*}}\bigg{)}\ \text{where}\ n^{*}>h_{c}\\ 0&\ \text{where}\ n^{*}\leq h_{c}\end{aligned}\right. \tag{11}\]  
\[h^{*}_{c}=min\{\ n^{*}\,\ h_{c}\} \tag{12}\]  
The height at which the horizontal wave force acts above the base is given as follows:  
\[h_{Ph}=\
icefrac{{M_{Ph}}}{{}_{{}_{\prime}}}/\mathit{pl} \tag{13}\]  
Figure 9: Wave forces from Goda [10].
# Antimicrobials in Dentistry
## 2 Antimicrobials Used in Dentistry
### Graphene and Graphene Oxide  
The nanomaterials, graphene and graphene oxide (GO), possess unique properties, which have stemmed enormous interest in their use as nanofillers in composites, drug delivery and other biomedical applications [121; 122]. Nanosheets of GO are two-dimensional structures with a honeycomb carbon framework. The potential application of graphene and its derivatives [124; 125] in oral healthcare have highlighted the antimicrobial properties and low cytotoxicity of graphene and its derivatives. GO has been reported to exhibit antibacterial properties, and GO nanosheets inhibit the growth of dental pathogens [126]; thus, it is a promising alternative in minimising antibiotic resistance and reducing the prevalence of biofilm formation. GO and graphene have also been used to form polymer nanocomposites and evaluated for their antimicrobial activity. Poly (methylmethacrylate), a widely used polymer in dental applications, was developed into a composite with GO, which exhibited superior mechanical properties and antibacterial activity (Figure 6) [116; 127; 128]. The use of GO in coating titanium implants is another potential application of interest that minimises the adhesion of bacterial species on the surface. In addition, silver nanoparticles (Ag-NPs) deposited onto functionalised hybrid graphene demonstrated an increase in antimicrobial activity against S. aureus and E. coli. [PERSON] et al. [129] used a similar system and developed a silver-graphene oxide system as a root canal irrigant for microbial killing and biofilm disruption with the preliminary results indicating that Ag-GO can be potentially used as an auxiliary disinfect.
# Feature Integration Strategies for Neural Speaker Diarization in Conversational Telephone Speech
## 4 Results and Analysis
### Multi-Speaker Scenario
#### 4.2.3 Domain-Focused Adaptation Analysis
This insight enables more efficient utilization of adaptation resources by focusing on the most relevant subset of available data rather than maximizing adaptation data volume indiscriminately.
# Towards Automatic License Plate Recognition in Challenging Conditions
## 3 Methodology
### The LP Recognition  
After a license plate is detected, normally the conventional LP identification methods segment the plate characters to recognize LP. These steps usually combine image processing techniques or video sequences, and their calculations depend on the true recognition rate and the error recognition rate. As discussed earlier, LP recognition is a difficult task due to the huge variety of plate formats and severely varying outdoor illuminations during the image acquisition phase. Many methods perform well in standard circumstances, for instance, controlled illuminations, restricted vehicle speeds, prespecified roads, and static backgrounds. Several algorithms have been designed to achieve LPR in images. In addition, issues such as processing time, computational complexity, and recognition rate are also important parts of the LPR algorithm. Algorithm 3 shows the pseudocode of the proposed LP recognition algorithm.  
```
1. Input: Vehicle image confined by bounding box
2. For satisfactory luminance, do;
3. begin LP Localization
4. transform the vehicle-detected image to the HSV domain using Equations (1)-(3)
5. do segmentation
6. define HSV threshold limits for every channel
7. obtain mask images
8. If\(H_{Image}\geq HT_{low}\) and \(H_{Image}\leq HT_{high}\)then\(H_{mask}=1\)
9. if\(S_{Image}\geq ST_{low}\) and \(S_{Image}\leq ST_{high}\)then\(S_{mask}=1\)
10. If\(V_{Image}\geq VT_{low}\) and \(V_{Image}\leq VT_{high}\)then\(V_{mask}=1\)
11. obtain the LP blob image (\(A\))
12. if\(HSV_{masks}=1\)
13. end segmentation
14. use mathematical morphology by Equations (4) and (5)
15. analyze dimensions through aspect ratio and LP spatial area
16. else
17. use PCA and form a luminance vector
18. calculate the luminance vector
19. approximate \(threshold_{high}\) and \(threshold_{low}\)
20. If\(mean>threshold_{high}\to\) decrease the luminance,
21. elseIf\(mean<threshold_{high}\to\) increase the luminance,
22. Obtain improved output image (\(X^{\prime}\))
23. end LP Localization
24. Output:\(I^{\prime}\) = LP localization
```  
**Algorithm 2**License plate detection procedure.  
As can be seen in Algorithm 3, our developed method contains interconnected steps and performs miscellaneous operations after the LP bounding box is fed to the recognition module. Since the area contained by the LP is normally small, for better visibility, contrast is enhanced using contrast from basic image processing methods. The improved contrast image is binarized and segmented by applying the morphological operations using Equations (4) and (5), respectively.  
On the basic pretrained model, the LP characters are predicted to build the possible LP strings that may appear inside the LP bounding that was processed in the initial stage of the LP recognition module. Algorithm 3 generally depicts the core theme of the LP recognition scenario. All the operations used herein, such as contrast, deblurring, and binarizing the image, are essentially handy for the recognition task.
# Revisiting Text Guide, a Truncation Method for Long Text Classification
## 1 Introduction
However, the authors of Text Guide have indicated that the performance of their method could presumably be improved through additional experiments. In this study, we aimed to explore selected possibilities for improving the results obtained with the Text Guide method. In particular, we wanted to answer the following research questions (RQ):  
* What is the influence of the CTC model quality on the Text Guide? * Does the quality of the Text Guide depend on the adopted machine learning interpretability method? * Is considering more than one occurrence of essential token found in the original text instance by Text Guide is beneficial? * Is Text Guide useful for text instances only slightly exceeding the model limit? * Does Text Guide obtain results with a selected transformer model transferable to other models? * Is use of Text Guide beneficial for other text classification data sets? The reasons for formulating and approach to answering the above research questions were the following:  
Regarding RQ1, Ref. [11] has used a BoW model for obtaining token features. Here, we explore the influence of the quality of the model used for obtaining token features for final classification performance based on text instances, as created by Text Guide. Considering RQ2, in [11], only one tool for extracting the feature importance of selected important tokens was used. In this study, we tested whether a more recent approach called Shapley Additive Explanations (SHAP) [12] might result in performance improvements. Regarding RQ3, we observed that Text Guide searches the original text instance only for the first occurrence of a token previously identified as important; if found, Text Guide extracts the token together with its neighboring tokens and adds the resulting \"pseudo sentence\" to the new text instance. Here, we explored the performance of Text Guide when not only the first occurrence was considered. Considering RQ4, based on [11], we believed Text Guide's performance for text instances exceeding the model limit to only a small extent required further exploration. For this purpose, we introduced a threshold parameter defining the instance length for which the Text Guide method should be used. RQ5 was formulated to verify whether it can be assumed that since using Text Guide for one model is beneficial, it will also be for other models. The answer to this RQ was obtained by testing four other transformer models. Finally, regarding RQ6, testing a method on various data sets can confirm its utility. In order to prove Text Guide can be helpful for other data sets, we apply the Text Guide to another well-known data set. For all experiments, we used a publicly available Text Guide implementation [13]. We believe the main contribution of our research is testing and optimizing various modifications and parameter values of the very recently introduced truncation method.