esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Organic Acid Profiles of Fermented Samples  
The concentrations of lactic, acetic, citric, and formic acids produced by probiotic strains are presented in Table 2. The LC-p sample, produced using the _L. casei_ strain, exhibited the highest lactic acid concentration (0.783 g/100 mL); also, the LA-p, produced using the _L. acidophilus_ strain, showed a similar level (0.744 g/100 mL). These findings align with the literature emphasizing the adaptability of the _L. casei_ strain to various fermentation conditions [29]. The titratable acidity of the LA-p and LC-p was also high, consistent with these results. Also, there are some studies in the literature regarding the lactic acid production of the strains of the species used in the current study. For example, in a study [30], it was determined that a strain of _L. casei_ produced 0.616 g/100 mL lactic acid. As for acetic acid, it imparts a sharp flavor to fermented products but can produce undesirable tastes in excessive amounts. Generally, acetic acid is produced in lower quantities compared to lactic acid during lactic acid bacteria fermentations. The _B. bifidum_ produced the highest acetic acid levels (0.607 g/100 mL), attributed to its heterofermative metabolism, which generates both lactic and acetic acids as end products [31]. This dual acid production contributes to flavor balance and extends the shelf life of fermented products. Secondly, in this study, the ST strain also produced relatively high levels of acetic acid (0.594 g/100 mL), providing a sharper flavor compared to other strains [32]. This showed that some strains of _S. thermophilus_ with proteolytic activity can exhibit high acetic acid concentrations and the metabolites they produce cause different chemical reactions to start and different compounds to be formed [33]. In the literature, it was determined that generally, the acetic acid production by LAB is lowest as compared to other organic acids [34]. Fermentation processes carried out by _Limosilactobacillus fermentation_, _Pediococcus pentosaceus_, _Weissella ciraria_, and _L. casei_ strains at 37 \({}^{\circ}\)C for 20 h reported an acetate production in the range of 0.0128-0.0141 mmoL/mL [35]. But it is known that the bacterial growth and its metabolites are highly dependent on medium composition. So, in a study, in anchovy infusion broth, _L. acidophilus_ and _Lactobacillus delbrueckii_ subspecies _lactis_ strains showed high acetic acid production viz. ; 822 mg/L and 803 mg/L, respectively. In MRS broth, LAB species _L. acidophilus_ and _L. delbrueckii_ do not show acetate production [34; 36]. Although citric acid is not a primary metabolite of LAB, nevertheless, it plays a role in flavor enhancement and as a substrate for secondary metabolic pathways. The LB-p, BB-p, and ST-p exhibited the highest citric acid concentrations (0.166, 0.166, and 0.168 g/100 mL, respectively). This elevated citric acid production aligns with their roles in aroma development, as citric acid serves as a precursor to diacetyl, contributing to buttery
## 3 Results
### Histological Examination  
Toluidine blue staining of the rat knee joint slices revealed articular cartilage degeneration, subchondral bone destruction, and osteophyte formation in the DMM group (Figure 3A). The average OARSI scores were 0.28 \(\pm\) 0.39 in the control group, 0.28 \(\pm\) 0.44 in the sham group, 5.3 \(\pm\) 2.61 in the DMM group, 0.56 \(\pm\) 0.34 in the DMM + BO 1% group, 1.33 \(\pm\) 1.25 in the DMM + BO 3% group, and 1.08 \(\pm\) 0.71 in the DMM + BO 5% group. The DMM group had a significantly higher score than the control group (_p_ < 0.01). In addition, administration of BO to the DMM rats decreased the OARSI score at any concentrations (_p_ < 0.01 vs. DMM + BO 1%, \(p\) < 0.05 vs. DMM + BO 3%, DMM + BO 5%). In this study, the DMM + BO 1% group showed the lowest score in the BO-administered groups, but there was no difference between groups in terms of BO concentration (Figure 3B).  
TRAP staining of the medial tibia revealed osteophytes and multinucleated TRAP-positive osteoclasts in the subchondral bone in the DMM group (Figure 4A,B). The average number of these cells were 2.0 \(\pm\) 0.8 in the control group, 4.9 \(\pm\) 1.5 in the sham group, 8.8 \(\pm\) 2.6 in the DMM group, 1.7 \(\pm\) 1.1 in the DMM + BO 1% group, 3.7 \(\pm\) 1.9 in the DMM + BO 3% group, and 4.1 \(\pm\) 1.4 in the DMM + BO 5% group. The number of osteoclasts was significantly increased in the DMM group compared to the control group, which was significantly decreased when BO was administered at any concentrations. In this study, the DMM + BO 1% group had the lowest cell count in the BO oral group, but there was no difference between groups by BO concentration (Figure 4C). Interestingly, the number of TRAP-positive osteoclasts and the OARSI score were strongly correlated (r = 0.70, \(p\) < 0.001) (Figure 5).  
Figure 3: Histological analysis for the development of KOA assessed with OARSI score. (**A**) Representative pictures of the toluidine blue–stained right knee samples in each group. Magnification: \(\times\)40. Scale bars = 200 \(\upmu\)m. (**B**) At least 3 slices of the paraffin–embedded knee samples were prepared, stained by toluidine blue, and pictured with Olympus BX 53 microscope (Olympus, Tokyo, Japan). Articular cartilage degeneration (0–15 points), subchondral bone damage (0–5 points), and osteophyte formation (0–4 points) were measured and total score of the three parameters (0–24 points) was identified as the OARSI score of each rat. \({}^{\ast\ast}\)_p_ < 0.01 vs. Control, \({}^{\text{tt}}\)_p_ < 0.01 vs. DMM by Tukey-Kramer test. BO; Bioggito, DMM; Destabilization of the medial meniscus, KOA: knee osteoarthritis, OARSI; Osteoarthritis Research Society International.
# Knowledge Reasoning via Jointly Modeling Knowledge Graphs and Soft Rules
## 2 Related Work
### Hybrid Reasoning  
Both rule-based and embedding-based methods have their own advantages and disadvantages. Recent works have integrated these two types of reasoning methods together. [PERSON] et al. [42] tried to learn a KGE from rule groundings and triples in combination. They utilized TransE to obtain entity and relation embeddings and computed the truth value for each rule using t-norm fuzzy logic. Then, they ranked the rules by their truth values and manually filtered those ranked at the top. [PERSON] et al. [43] enhanced KGEs by injecting rules. Although their works also learned embeddings jointly, they could not support the uncertainty of soft rules. [PERSON] et al. [44] ordered relations approximately by maximizing the margin between negative and positive logical rules to capture the transitivity and asymmetry of rules. However, it cannot directly benefit from the forward reasoning of rules and does not utilize the confidence information corresponding to the rules. [PERSON] et al. [17], [PERSON] et al. [18], and [PERSON] et al. [45] obtained KGEs with supervision from soft rules, proving the effectiveness of logical rules. Whereas, they did not consider combining KGEs to filter out erroneous facts introduced by these rules during the iteration process. [PERSON] et al. [46] used an MLN to model logical rules and inferred new triples to enhance KGEs. One main difficulty here is the large search space in determining rule structures and searching for support groundings. [PERSON] et al. [16] aimed to improve sparse data representation through iterative learning and update the confidence of rules through embeddings. Nevertheless, their method evaluates sparsity based on the frequency of entities participating in triples, and only conclusions related to entities with high sparsity are injected into the original knowledge graph. This design choice is specifically tailored to address the challenges posed by sparse data in the test set but did not take into account the potentially erroneous introduced by the rules. In contrast, Iterlogic-E models the conclusion labels as 0-1 variables and uses confidence regularization loss to eliminate uncertain conclusions. Our approach employs confidence in soft rules and embedding models to evaluate conclusions inferred from soft rules. During iterative training, the embedding model learns to select the most likely true conclusion from all available conclusions. These selected conclusions are labeled as true, while the remaining facts are filtered out as potentially erroneous. This method avoids introducing erroneous facts and maintains training efficiency. Also, no extra memory is required as no additional model parameters are introduced.
## 2 System Model
### Multi-Parameter Weighted-Type Fractional Fourier Transform Model  
The MP-WFRFT is defined as follows:  
\[S_{0}(n)=F^{\alpha,V}[X_{0}(n)]=\omega_{0}(\alpha,V)+\omega_{1}(\alpha,V)+ \omega_{2}(\alpha,V)+\omega_{3}(\alpha,V) \tag{6}\]  
where \(F^{\alpha,V}\) is the transformation operator of the MP-WFRFT, \(X_{0}\) denotes the original baseband signal to be encrypted, and \(X_{i}(i=1,2,3)\) denotes the \(i\)-th transformation of \(X_{0}\). \(\omega_{l}(\alpha,V)(l=0,1,2,3)\) represents the weight factors, \(\alpha\) is the transform order, and \(V=[MV,NV]\) is the transform's scale vector, where \(MV=[m_{0},m_{1},m_{2},m_{3}]\) and \(NV=[n_{0},n_{1},n_{2},n_{3}]\). \(S_{0}(n)\) is the transformed MP-WFRFT signal. While conventional Fourier transform (FT) provides a fixed mapping from the time to the frequency domain, fractional Fourier transform (FRFT) introduces flexibility by rotating a signal to any intermediate time-frequency domain. The MP-WFRFT enhances this security framework significantly. It not only performs a rotation, governed by the order \(\alpha\), but also applies complex transformations like twisting, stretching, and shearing, which are determined by the scale vectors \(V=[MV,NV]\). Therefore, perfect signal reconstruction is only possible for a legitimate receiver who can apply the inverse MP-WFRFT with the precise keys \(\alpha\) and \(V\).  
The weight factors are as follows:  
\[w_{l}(\alpha,V)=\frac{1}{T}\sum_{k=0}^{T-1}\exp\biggl{\{}\pm\frac{2\pi\dot{l} }{T}[(Tm_{k}+1)\alpha(k+Tn_{k})-lk]\biggr{\}},\ (l=0,1,\ldots,T-1) \tag{7}\]  
where \(T\) represents the transform period of the MP-WFRFT. Given the invertibility of the MP-WFRFT, it is possible to extract the original discrete signal \(X_{0}(n)\) from Equation (8) provided that the signal \(S_{0}(n)\) is known.  
\[\begin{split} X_{0}(n)&=F^{-\alpha,V}[S_{0}(n)]\\ &=\omega_{0}(-\alpha,V)S_{0}(n)+\omega_{1}(-\alpha,V)S_{1}(n)+ \omega_{2}(-\alpha,V)S_{2}(n)+\omega_{3}(-\alpha,V)S_{3}(n)\end{split} \tag{8}\]  
Figure 4: Block diagram of PAPM receiver.
# Modification of Nonlinear Controller for Asymmetric Marine Vehicles Moving in Horizontal Plane
## 4 Numerical Simulations
### Vehicles and Test Conditions
#### 4.1.1 Results for QV Control Algorithm
The mean values of the kinetic energy are: \(K_{m}=13.144\) J (and \(K_{m1}=11.512\) J, \(K_{m2}=1.611\) J, \(K_{m3}=0.021\) J). The velocity deformations caused by the couplings at the beginning of the vehicle movement have large values, but, at all times, they are comparable for the longitudinal and lateral movements, as evidenced by the variables \(\Delta\zeta_{1},\Delta\zeta_{2}\) shown in Figure 5l. **Analysis of results based on indexes**. To estimate the qualitative results obtained in the QV driver test, an analysis was performed based on the selected indexes. 1. mean of the elements (mean), i.e., mean(\(\sum\Delta\zeta_{12}\)), where \(\sum\Delta\zeta_{12}=|\Delta\zeta_{1}|+|\Delta\zeta_{2}|\); mean(\(\sum\Delta\zeta_{34}\)), where \(\sum\Delta\zeta_{34}=|\Delta\zeta_{3}|+|\Delta\zeta_{4}|\); mean(\(\sum\Delta p_{xy}\)), where \(\sum\Delta p_{xy}=|\Delta p_{x}|+|\Delta p_{y}|\);
2. the standard deviation of the elements (std), i.e., std(\(\sum\Delta\zeta_{12}\)), std(\(\sum\Delta\zeta_{34}\)), std(\(\sum\Delta p_{xy}\));
3. root mean square of the tracking error, i.e., \(RMS=\sqrt{\frac{1}{f_{\tau}-l_{0}}\int_{t_{0}}^{l_{f}}\left\|e(t)\right\|^{2}}dt\), with \(\left\|e(t)\right\|=\sqrt{\Delta p_{x}^{2}+\Delta p_{y}^{2}}\) (\(\Delta p_{x}\), \(\Delta p_{y}\))--the position errors in the reference frame);
4. mean integrated absolute control, i.e., \(MIAC=\frac{1}{f_{\tau}-l_{0}}\int_{t_{0}}^{l_{f}}|A(t)|dt\), where \(A=\tau_{u},\tau_{\tau}\). The index values are summarized in Table 4.  
The mean error values are mostly smaller for the XX AUV than for the Kambara. There are also smaller values of the thrust force of the vehicle \(\tau_{u}\). These values are related to the mass of the vehicle. For the position and velocity state errors, this is not necessarily the case. It follows that these states are not crucial for the actual errors but indicate how well the algorithm is working. In contrast, the RMS and mean(\(\sum\Delta p_{xy}\)) show this explicitly. Another observation is that the control tracking accuracy depends on the realized trajectory. For both vehicles, better tracking accuracy was obtained for curvilinear trajectories than for linear trajectories. This may suggest that tracking the latter is more difficult for the QV-based algorithm.
## 3 Results
### Air Resistance  
Figure 12 illustrates the pressure drop measured according to the airflow rate applied. The respiratory limit induced by the lungs is added as a red line. The calibration curve is also added as a green line.  
Graphically, each device has a parabolic profile due to the pressure drop square root (Equation (3)), then verified with the R-square (0.9904-0.9996) when air resistances are extracted. Two groups are clearly identifiable. The first one reaches the respiratory limit for airflow rate between 2 and 11 L min\({}^{-1}\) and curves are dispersed. The second group have low-pressure drops and do not exceed 2000 Pa at 10 L min\({}^{-1}\) with a small variability. Their curves look to be overlapped and are hardly separately observable. Finally, the air resistances are determined. They are subtracted by the empty setup air resistance (obtained by the calibration). Figure 13 illustrated the treated air resistance.  
The air resistance values have a wide range of values (between 0.15 and 33 Pa\({}^{0.5}\) min L\({}^{-1}\)). Therefore, eleven values are below 2.5 Pa\({}^{0.5}\) min L\({}^{-1}\) with one value upper than 1.2 Pa\({}^{0.5}\) min L\({}^{-1}\). and seven values below 0.3 Pa\({}^{0.5}\) min L\({}^{-1}\). In the other side, fifteen values are upper than 7 Pa\({}^{0.5}\) min L\({}^{-1}\) with five values upper than 25 Pa\({}^{0.5}\) min L\({}^{-1}\), five between 11 and 18 Pa\({}^{0.5}\) min L\({}^{-1}\) and five between 7 and 9 Pa\({}^{0.5}\) min L\({}^{-1}\). The two groups are clearly observable and an air resistance of 4.8 Pa\({}^{0.5}\) min L\({}^{-1}\) could be used as a limit between them.  
Figure 12: Pressure drop curves of the twenty-six tested devices according to the airflow rate.
## 3 Material & Methods
### Pentesting
#### 3.3.3 GNSS Jamming  
A radio jamming attack aims to completely cut off radio communications between two points by sending powerful radio waves (noise) on the same frequencies as those used by the targeted system [44]. Thus, a jammer could target Wi-Fi, telephone communications, or RADAR, depending on the chosen frequency. Similar to GNSS spoofing attacks, jamming attacks have become more common with the advent of smaller, inexpensive solutions that can be easily set up and hidden in a bag or mounted on a wall. It should be noted that jammers are prohibited in Switzerland and more generally in Europe, from their use to their mere possession [50]. These strict measures are intended to prevent any blockage of radio waves, which are used by emergency services and aviation, among others. However, SDR devices are not subject to such restrictions, since their use as jammers is not their primary function. Thus, despite the law of 1 st of January 2018 banning the import of conventional jammers, these SDR devices can be easily obtained. A BladeRF-type device cost CHF 500 (\(\approx\)USD 545.2) at the time of writing, compared to several thousand francs for a conventional jammer.  
The jamming attack was performed using RfCat (see Figure 8) in order to create noise on the desired radio frequency. This tool was used as it allows easy scripting to customise the operations of SDR platforms, whether for recording, replaying, or creating signals, as is the case here. As we already know which frequency to jam, this one is simply stored in a constant (JAMMING_FREQUENCY_IN_HZ), making this script a point jammer. If needed, it would also be possible to add in an incremental loop in order to make it a sweep jammer.  
Figure 6: Script used to obtain ephemerises and create the gps-sdr-sim bitstream.  
Figure 7: Script used to spoof the GNSS positionning.  
Running the script resulted in a successful loss of position on both the Android phone (in \"GPS only\" mode) and the iPad.
## 4 Discussion
### Limitations and Suggestions for Future Studies  
Although the behavioral findings (i.e., in SRTn) in the NHV group were significantly different from the NH group, no significant differences were shown in temporal activity to SIN, plausibly because of the relatively easy SNR (0 dB) that was used for the SIN condition. Future studies may want to replicate the current study using several SNR levels, including those that are expected to yield less than 70.7% correct recognition of speech, to further explore the effect of noise on speech processing mechanisms for NH people. Similarly,future investigations may wish to use additional types of background noise to expand the implications of the current study. For example, multi-talker babble noise produces informational and not only energetic masking, and thus may burden speech understanding in both NH and CI participants. Finally, to better understand the similarities and differences in cortical activation in response to speech between prelingual and post-lingual CI users, future studies may want to design a testing protocol that will directly compare these two groups using fNIRS and behavioral measures.
## 2 Naturally Occurring Coumarins and Their Role in Plants
### Classification of Naturally Occurring Coumarins and Their Role in Plant Protection
#### 2.2.6 Bicoumarines  
Bicoumarines have been isolated from plants _Tribnasia trifolia_ (Rutaceae) [113], _Dysoxylum parasiticum_ (Osbeck) Kosterm (Meliaceae) [114], and _Pleurosperum rivulorum_ (Umbelliferae) [115]. The best-researched bicoumarin is dicoumarol (Figure 5). Dicumarol is generated by the hydroxylation of the 4-position of the coumarin. This is followed by capturing of a molecule of formaldehyde, and subsequently by condensation with another molecule of 4-hydroxycoumarin. Finally, the enolization of the keto group forms dicumarol [68]. Dicumarol is discovered as constituent of sweet clover hay that caused the death of cattle due to bleeding disorders. Dicumarol is an anticoagulant that acts as a vitamin K antagonist [116]. The dicounrol, also formed by bacterial fermentation of  
Figure 4: Naturally occurring 4-phenylcumarins.  
yellow sweet clover, was isolated for the first time from the decomposed leaves of _Meliottus albus_ (Fabaceae/Leguminosae) [68].  
The presence of natural coumarins in different species of plants and microbes, and their biological effects are summarized in Table 1. It is evident that the diversity and structural complexity of the coumarins constitute is a consequence of higher plant evolution. Simple coumarins are the most common in fungi and all angiosperms. They exhibit a wide range of biological effects related to plant protection from pathogen microbes, fungi, and nematodes. Furanocoumarin is the second most prevalent type of coumarin. Furanocoumarins are found in the family Citrus, Apiaceae, and Rutaceae, with most of them showing nematicidal properties. Pyranocoumarins are present in Apiaceae and Fabaceae, where they exhibit antifungal, phytotoxic, and antifeedant effects. Phenylcumarins are the most abundant in the plant family Fabaceae, but there is no literature on their functions in plants.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Phylum/** & **Species** & **Group of Coumarins** & **Specific Compounds** & **Known Biological Activities** & **Ref.** \\ \hline Bacteria & & & & & \\ \multirow{2}{*}{Streptomyces} & _Streptomyces_ & 3-amino-4-7- & clorobiocin, novobiocin, & & \\  & _rosechromogenes_ var. & dihydroxycumarins & coumermycin & antibacterial & [15] \\  & & _oscits_ & & & \\   Porifera & & & & & \\   Axinellidae & _Axinella cf. corrugate_ & simple & esculetin-4-carboxylic acid & anti-SARS-CoV & [22] \\   Fungi & & & & & \\   Pleosporaceae & _Alternaria alternata_ & simple & isofraxidin & antibacterial & [61] \\   Trichococcae & _A Aspergillus fumigatus_ & simple, bicouramis & 4-hydroxycumarin, & biosynthesis of & [65; 66] \\  & & & & & \\  & & &
## 2 Optical Angle Nano-Positioning Mechanism Configuration
### Physical Design  
The mechanism features a uniaxial straight circular hinge, constituting a four-link flexure hinge mechanism as shown in Figure 3, and is simplified using the pseudo-rigid body model. The four links, denoted \(l_{i}\) (\(i\) = 1-4), are interconnected via four flexible hinges. Here, \(l_{1}\) is equal to \(l_{3}\), and \(l_{4}\) acts as the fixed installation surface. The lines of \(l_{1}\) and  
Figure 2: Optical angle nano-positioning mechanism.  
converge at the waist, forming an isosceles triangle with \(O\) as its vertex, where \(\varphi_{0}\) represents the initial angle of the lower bar \(l_{3}\) with respect to \(l_{4}\). The swing arm is rigidly connected to \(l_{2}\), maintaining a constant perpendicular angle to \(l_{2}\). An external force is applied to the swing arm at a distance \(L\) from the point \(O\). When the PZT actuator drives the moving platform to displace by \(\Delta\), the rotation angles of \(l_{1}\) and \(l_{3}\) are denoted as \(\varphi_{1}\), and the rotation angle of \(l_{2}\) is \(\varphi_{2}\), which is the mechanism output angle.  
Following a rotation of \(l_{3}\) by an angle \(\varphi_{1}\), it can be maneuvered based on its geometric relationship with \(\varphi_{2}\), which is as follows:  
\[\varphi_{2}=\pi-\varphi_{1}-\arccos{(\frac{l_{2}^{2}+l_{4}^{2}-2l_{1}l_{4} \cos{(\varphi_{0}+\varphi_{1})}}{2l_{2}\sqrt{l_{1}^{2}+l_{4}^{2}-2l_{1}l_{4} \cos{(\varphi_{0}+\varphi_{1})}}})}-\beta-\varphi_{0} \tag{1}\]  
Among which:  
\[\beta=\arccos{\sin{(\frac{l_{4}\sin{(\varphi_{0}+\varphi_{1})}}{l_{1}^{2}+l_{4} ^{2}-2l_{1}l_{4}\cos{(\varphi_{0}+\varphi_{1})}})}} \tag{2}\]
## 4 Results of System Performance
### Evaluation Metrics  
All systems were evaluated to diagnose abnormal cell images of the cervical cancer data set using measures of accuracy, specificity, sensitivity, AUC and precision as in Equations (7) to (11). Each system yields a confusion matrix containing all samples of the data set called TP, TN, FP and FN. The equations contain variables TP and TN representing images that the systems have correctly diagnosed and variables FP and FN representing images that the systems have incorrectly diagnosed [46].  
\[\text{Accuracy}=\frac{\text{TN}+\text{TP}}{\text{TN}+\text{TP}+\text{FN}+ \text{FP}}\ *100\% \tag{7}\]  
\[\text{Specificity}=\frac{\text{TN}}{\text{TN}+\text{FP}}\ *100 \tag{8}\]  
\[\text{Sensitivity}=\frac{\text{TP}}{\text{TP}+\text{FN}}\ *100\% \tag{9}\]  
\[\text{AUC}\ =\frac{\text{True Positive Rate}}{\text{False Positive Rate}} \tag{10}\]  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}}\ *100\% \tag{11}\]  
where: TP Indicates WSIs of abnormal cells properly classified as cervical cancer. TN indicates WSIs of normal and properly classified cells. FP indicates WSIs of normal cells but is incorrectly classified as abnormal cells of cervical cancer. FN indicates WSIs of abnormal cells, but they were incorrectly classified as normal cells.