esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Comparison
### PNR-TDNN Experiments
#### 4.2.4 Discussion of Other Noisy Situations  
To assess the generalization capability of the PNR-TDNN, ESC50 (primarily environmental sounds) and a subset of cn-celeb_v2 (mainly Chinese speech) are selected to generate noise interference for the zhvoice. The ESC50 encompasses animal sounds, natural phenomena, water noises, human non-verbal cues, as well as indoor and domestic sounds. These closely resemble the noise sources used in Section 4.2.1 (soundsnap and actual conference room recordings), enhancing the realism of the testing conditions. Furthermore, the impact of human speech interference is significant and cannot be overlooked. To simulate this, a random selection of speech segments from the cn-celeb_v2 dataset is used as a second noise source. The speech in zhvoice and cn-celeb_v2 is sourced from different individuals, reflecting realistic conditions in which multiple speakers may be present.  
Table 8 reveals that PNR-TDNNs outperformed all baselines in terms of recognition accuracy when exposed to ESC50 noise. Despite the presence of vocal interference, the S-Hu + EC + FAM yields robust recognition outcomes. In addition, S-Hu + EC + FAM substantially outperforms Hu + EC + FAM on all three datasets, which corroborates the conclusion in Section 4.2.2 that the small pre-trained model is more effective at capturing speech feature information in a strongly noisy environment with a narrow range of SNR. This demonstrates the strong generalization capability of the proposed method in diverse scenarios, thereby showcasing the model's adaptability to different conditions.  
Figure 12: Histogram of significant differences between the four baseline models and the THu+ EC+FAM model on dataset A (a, b, c, d, and e represent the comparison notation between the groups corresponding to the proposed T-Hu+EC+FAM, Gemini, RedimNet, CAM++, and ECAPATDNN, respectively).  
Figure 13: Best EERs of the four baseline models and the T-Hu + EC + FAM model (blue for dataset A, red for dataset B, and gray for dataset C).
## 4 Results and Analysis
### Evaluation of Hybrid Encryption Algorithm Strength
#### 4.1.1 Entropy Analysis of Encryption  
In cryptography, entropy is recognized as an important measure of the randomness or unpredictability of data. High entropy indicates a greater degree of randomness, ensuring that encrypted data are well secured [46]. Essentially, it refers to how uncertain or non-repetitive the contents of a message are. For example, the message \"AAAAAA\" has very low entropy due to its highly predictable, repetitive characters, resulting in similar byte distributions. In contrast, the message \"G7$k@PIQ\" has high entropy because of its complexity and unpredictability in both characters and byte distribution.  
In this study, Shannon entropy was calculated for 31 encrypted messages. By computing the entropy for each encrypted message, we aimed to quantify the randomness and unpredictability of the encrypted data. This information is important for programmers, as it helps in the assessment of the algorithm's performance in obscuring the message contents [47]. In the context of information theory, entropy can be derived mathematically, as shown in Equation (1):  
\[H(X)=-\sum_{i=1}^{n}P(x_{i})\log_{b}P(x_{i}) \tag{1}\]  
where the following denotions are used:  
* \(H(X)\) is the entropy of the random variable \(X\).
* \(P(x_{i})\) is the probability of the outcome \(x_{i}\) occurring.
* \(n\) is the total number of possible outcomes for \(X\).
* \(\log_{b}\) denotes the logarithm to the base \(b\), typically \(b=2\) (binary entropy) or \(b=e\) (natural entropy).  
The entropy results were plotted on a graph to effectively visualize the variations observed in the dataset. Additionally, comprehensive statistical analyses were conducted and key descriptive metrics of the entropy data were calculated to provide a thorough understanding of the underlying patterns and behaviors, as shown in Figure 7.  
The entropy values for 31 different messages are shown in Figure 7, and the corresponding statistics are presented in Table 1. The mean entropy is 0.989152, and the median is 0.989167. The mean and median are close to each other. In a perfectly symmetrical distribution centered on the mean, this, along with a low standard deviation of 0.001774, suggests uniform randomness across all contributions. There is no significant deviation from the mean entropy. In cryptographic terms, this indicates the generation of highly unpredictable and random ciphertext patterns due to the applied encryption process. High entropy is desirable to maintain security [48] and shows the effectiveness of encryption algorithms in producing random data resistant to attacks. These consistent entropy values are crucial in ensuring that all encrypted data receive equal security strength [49].  
In cryptographic terms, the algorithm's ability to retain the entropy values at 1 allows each bit to have an equal probability of 0 or 1. This means an attack against the algorithm would not find it that easy to identify patterns or obtain useful information from the ciphertext.
## 2 Mechanisms of Acidogenic Fermentation
### Key Microbial Communities Involved in VFA Production
_Propionibacterium freudenreichii_, which naturally produces isovaleric acid in environments such as Swiss cheese fermentation, has shown potential for engineered pathways that extend to valeric acid production [56]. Additionally, _Megasphaera_ species are recognized for their role in chain elongation processes, transforming shorter-chain volatile fatty acids into longer chains like valeric acid [57]. Isovaleric acid production through microbial fermentation has gained attention due to its applications in the fragrance and pharmaceutical industries. _Propionibacterium freudenreichii_, for example, contributes to isovaleric acid formation as part of its metabolic processes, often seen in dairy fermentation [58]. Additionally, Escherichia coli has been engineered to enhance isovaleric acid output by manipulating the branched-chain amino acid pathway, where intermediates like ketovaline are converted into isovaleric acid through specific enzymatic steps. This engineered pathway provides an effective biotechnological route for isovaleric acid synthesis, offering an alternative to traditional chemical processes [59]. Caprocic acid, a medium-chain fatty acid, is also produced by microbial fermentation and has various applications, from artificial flavor production to potential use as a biofuel precursor. This acid can be synthesized via reverse \(\beta\)-oxidation pathways using bacteria such as _Clostridium kluyveri_, which elongates shorter-chain fatty acids under anaerobic conditions [5]. Through the fermentation of substrates like lactic acid derived from lignocellulosic biomass, these microbes can efficiently generate caprocic acid, supporting sustainable production methods for use in food, pharmaceuticals, and biofuels. The adaptability and diversity of these microbial communities are key to the efficiency and stability of acidogenic fermentation. Mixed microbial consortia, rather than monocultures, are typically favored in industrial applications due to their robustness against fluctuating substrate compositions and environmental changes [60]. The dynamic interactions among these microbes ensure consistent VFA production even when the conditions vary, making the process resilient and adaptable to different waste streams. This adaptability is particularly important in the treatment of heterogeneous waste materials, where the composition of the feedstock may shift depending on the source, and maintaining a stable microbial community is crucial for optimizing the production of bioplastic precursors and other valuable chemicals [61].
## 3 Result and Discussion
### Photovoltaic Analysis
Directly measuring how the electrolyte is affecting the dye energy levels is challenging; however, if the assumption that the dye \(E_{\text{(S+/S)}}\) energy level remains unchanged, as it does when comparing solution and TiO\({}_{2}\) film measurements for these dyes, then the excited state energy levels are shifting dramatically upon the addition of the electrolyte. For example, in the case of **AP12**, a driving force for electron injection (\(\Delta\text{G}_{\text{inj}}\)) into the TiO\({}_{2}\) CB of 330 mV is estimated from both solution and film measurements when the TiO\({}_{2}\) CB is taken as \(-\)0.50 V vs. NHE (Table 1). However, using the IPCE onset value to calculate the excited-state energy level of **AP12** gives a \(\Delta\text{G}_{\text{inj}}\) value of only 160 mV. This lower \(\Delta\text{G}_{\text{inj}}\) value could have a dramatic influence on electron injection efficiencies and may also contribute to lower peak IPCE values. _Methods to both predict and control dye energetics within a full DSC cell environment are critical to move toward rational dye design and away from pseudo-empirical dye design protocols where the energetics within a DSC device only mirror that of the solution or film estimates on an unpredictable case-by-case basis._ The inability to directly account for the dye-electrolyte energetic changes has rendered a very promising broad absorbing dye (**AP12**) as estimated energetically from the solution and film measurements, low performing in DSC devices (2.9% PCE). Figure 5: (**a**) \(J\)–\(V\) curves for DSC devices with **AP6**, **AP8**, **AP9**, and **AP12**. (**b**) IPCE curves for **AP6**, **AP8**, **AP9**, and **AP12**-based DSC devices.
###### Abstract  
This paper analyzes the use patterns of a residential smart grid pilot in the Netherlands, called PowerMatching City. The analysis is based on detailed monitoring data measured at 5-min intervals for the year 2012, originating from this pilot which was realized in 2007 in Groningen, Netherlands. In this pilot, smart appliances, heat pumps, micro-combined heat and power (\(\mu\)-CHP), and solar photovoltaic (PV) systems have been installed to evaluate their efficiency, their ability to reduce peak electricity purchase, and their effects on self-sufficiency and on the local use of solar electricity. As a result of the evaluation, diverse yearly and weekly indicators have been determined, such as electricity purchase and delivery, solar production, flexible generation, and load. Depending on the household configuration, up to 40% of self-sufficiency is achieved on an annual average basis, and 14.4% of the total consumption were flexible. In general, we can conclude that micro-CHP contributed to keep purchase from the grid relatively constant throughout the seasons. Adding to that, smart appliances significantly contributed to load shifting in peak times. It is recommended that similar evaluations will be conducted in other smart grid pilots to statistically enhance insights in the functioning of residential smart grids.  
smart grids; renewable energy; flexibility; demand shifting; photovoltaic systems; smart appliances +
Footnote †: journal: Applied Sciences
# Development of Knowledge Graph for Data Management Related to Flooding Disasters Using Open Data
## 4 Experimental Results and Discussion
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Query Type (Questions)**} & \multicolumn{2}{c}{**Results**} \\ \cline{2-4}  & & **Open Datasets** & **Wikidata** & **Flooding KG** \\ \hline \multirow{8}{*}{**Concept**} & \multirow{8}{*}{Q1: Search for flooding disaster data} & wdt: P279 (subclass of) wd: & wdt: P279 (subclass of) wd: \\  & & & Q8065 (natural disaster) & Q8065 (natural disaster) \\  & & Namdong-gu, & wdt: P279 (subclass of) wd: & wdt: P279 (subclass of) wd: \\  & & Incheon\_Status of flood & Q30017 (high tide) & Q30017 (high tide) \\  & & risk areas & wdt: P349 (National Diet & wdt: P349 (National Diet \\  & & Korea National Territory & Library ID 00566917 & Library ID 00566917 \\  & & disaster data & Information Corporation & wdt: P373 (commons \\  & & Flooding trace information & category) Floods & category) Floods \\  & & flooding level line (annual) & wdt: P910 (topic’s main & wdt: P910 (topic’s main \\  & & (21 datasets) & category) wd: Q6457081 & category) wd: Q6457081 \\  & & & (Category: Flood) & (Category: Flood) \\  & & & (338 triples) & 360 triples \\ \hline \multirow{8}{*}{**Relation**} & \multirow{8}{*}{Q2: What is the cause of flooding? (has cause)} & \multirow{8}{*}{None} & wd:Q7925 (rain) & \\  & & & wd:Q8070 (tsunami) & \\ \cline{1-1}  & & & wd:Q7925 (rain) & wd:Q1550470 (siphoning) & \\ \cline{1-1}  & & & wd:Q8070 (tsunami) & wd:Q2369493 (river \\ \cline{1-1}  & & & wd:Q1550470 (siphoning) & overflow) \\ \cline{1-1}  & & & wd:Q2369493 (river & wd:Q18447212 (heavy rain) \\ \cline{1-1}  & & & wd:Q18447212 (heavy rain) & (direct) wave \\ \cline{1-1}  & & & wd:Q18447212 (heavy rain) & (inter) river water level, \\ \cline{1-1}  & & & levee, drainage pump, & sewer network \\ \hline \multirow{8}{*}{Q3: What is the effect of flooding? (has effect)} & \multirow{8}{*}{None} & wd:Q4 (death) & \\ \cline{1-1}  & & & wd:Q554774 (property \\ \cline{1-1} \cline{2-4}  & & & damage) & \\ \cline{1-1} \cline{2-4}  & & & wd:Q1033074 (dam failure) & \\ \cline{1-1} \cline{2-4}  & & & wd:Q1033074 (dam failure) & (direct) \# of casualties \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Descriptions of the principal datasets selected from the open dataset related to flooding disasters. this study. We will certainly address how to utilize such valuable data for our knowledge graphs in the future to give users of our knowledge graph more benefits.
# A Review of Air Pollution Monitoring Technology for Ports
## 2 Detection Methods for Air Pollutants in Ports
### Air Pollutant Sample Collection
The more applicable enrichment sampling method is divided into active sampling method and passive sampling method; active sampling method is characterized by the need for external power to sample the air through the absorbent medium. The method is more applicable in the port such as the ability to provide electricity and a higher tolerance for noise environment. The diffusion-type passive sampler has the characteristics of small size, no interference from atmospheric particles and aerosols, etc., and can measure a variety of atmospheric pollutants at the same time, which is suitable for the environment with various types of atmospheric pollutants in ports and is also suitable for the situation where space is scarce on ships.
## 2 Methodology
### Orbital Parameters  
Classical Orbital elements are a set of parameters that are used to describe the shape, size, and orientation of a satellite's orbit. These variables are crucial for satellite operation and design as they offer vital details of a satellite's orbit. A total of six Degrees of Freedom (DoF) is typically required for any physical object to define its location in three-dimensional space including three translational motions along X, Y, and Z and three rotational motions around them, commonly named roll, pitch, and yaw. Therefore, [PERSON] [27] defined a set of six classical orbital elements, sometimes called the Keplerian elements, to define an orbit. These six independent constants include the satellite's semi-major axis, eccentricity, inclination, right ascension of ascending node, argument of perigee, and true anomaly.
# Non-Mechanical Beam Steering with Polarization Gratings: A Review
## 2 Non-Mechanical Beam Steering with PGs
### Optical Properties of PG-Based Non-Mechanical Steering Systems
#### 2.2.2 Steering Efficiency  
Although individual PGs have peak diffraction efficiency approaching 100%, the total optical efficiency of the layered beam steering architecture also depends on the transmission efficiency and retardance accuracy of the handedness switches, scattering and absorption losses of the PGs, Fresnel reflections at interfaces, and the angular sensitivity of both handedness switches and PGs.  
In the visible to SWIR spectrum, electronically controlled birefringence (ECB) variable retarder cells can provide transmission > 97% and enjoy a wide manufacturing base. Likewise, there exists a wide selection of inexpensive display glass and index-matching bonding agents suitable for assembling efficient PG-based steering systems over these spectral bands. Figure 3a shows a typical steering stage providing \(\pm\)3.5\({}^{\circ}\) steering at 1550 nm with 96% total optical efficiency into the steered orders. For a full steering assembly with multiple stages, the angle-dependency of PG diffraction efficiency can become dominant and total steering efficiency decreases beyond ~96% per stage. For example, in Figure 3b, efficiency near the center is primarily a product of individual stage insertion loss for the five stages (i.e., 0.96\({}^{\circ}\) = 81%), but decreases with increasing steering angle due to AOI effects. Ultimately, all real diffractive beam steering systems will have a finite aperture that provides a corresponding envelope to the diffraction efficiency, resulting in decreased efficiency at higher angles. For active sensing this affect can be accounted for during processing of the return signal.
## 2 Results and Discussion
### Purification of the Antioxidant Peptides from SHCH
#### 2.2.3 Gel Filtration Chromatography  
Molecular size is an important determinant of the bioactivity of a specific peptide [8]. Therefore, gel filtration chromatography is an important method to purify peptides. Fr.4 was loaded onto a Sephadex G-15 column and separated into two fractions of Fr.4-1 and Fr.4-2 (Figure 3A). Each fraction was collected, lyophilized, and evaluated for HO\(\bullet\) scavenging activity. As shown in Figure 3B, the HO\(\bullet\) scavenging rate of Fr.4-1 reached 87.80% \(\pm\) 2.24% at 5 mg protein/mL and was higher than those of Fr.4 (72.03% \(\pm\) 2.64%) and Fr.4-2 (52.38% \(\pm\) 1.62%). Therefore, Fr.4-1 was selected for further purification by RP-HPLC.  
Figure 1: HO\(\bullet\) scavenging activities of trypsin hydrolysate (SHCH) and its three fractions at 15 mg protein/mL. All data are presented as the mean \(\pm\) standard deviation (SD) of triplicate results. \(a\)\(\sim\) Values with same letters indicate no significant difference for each group of samples at the same concentration (\(p>0.05\)).  
Figure 2: Elution profile of SHCH-I in DEAE-52 cellulose chromatography (**A**); and the HO\(\bullet\) scavenging rate (%) of different fractions of SHCH-I at 10 mg protein/mL (**B**). All data are presented as the mean \(\pm\) standard deviation (SD) of triplicate results. \(a\)\(\sim\)\(d\) Values with same letters indicate no significant difference for each group of samples at the same concentration (\(p>0.05\)). Fr: separated fractions.  
2.4. Isolation of Peptides from Fr.4-1 by Reversed-Phase High Performance Liquid Chromatography (RP-HPLC)  
The hydrophobic and hydrophilic properties of peptides play a key role in their retention time (RT) on an RP-HPLC column, and the RT can be adjusted by changing the ratio of polar (water) and nonpolar (methanol, acetonitrile) solvents [11]. Using an ultrafiltration membrane system, anion-exchange chromatography and gel filtration chromatography, Fr.4-1, which had the highest HO\(\bullet\) scavenging activity among all fractions, was separated using RP-HPLC on a Zorbax C-18 column, and the eluted fractions were collected separately according to the chromatographic peaks (Figure 4). Three fractions, referred to as enzymolysis peptide of scalloped hammerhead cartilage A (SCPE-A), SCPE-B and SCPE-C, with RTs of 10.642, 13.605, and 17.979 min, respectively, showed high antioxidant activities, and their HO\(\bullet\) scavenging rates reached 80.7% \(\pm\) 1.22%, 75.4% \(\pm\) 2.33%, and 92.2% \(\pm\) 3.44%, respectively, at 3.0 mg/mL. Therefore, SCPE-A, SCPE-B and SCPE-C were collected for further research.
## 4 Experimental Case Analysis and Discussion
### Excription of Induction Motor Dataset  
The data source of this research is a laboratory-made defect model of a small induction motor, and we obtained the operating status data through a data measurement platform. The sampling frequency was set to 20 kHz, and the sampling time was 5 s.  
In addition, there are five active state types in the self-made induction motor defect model of this research, including healthy status (Healthy, H), bearing outer ring damage (Bearing, B), broken rotor bars (Rotor, R), short circuit between stator turns (Stator, S), and centering failure (Misaligned, M). Various operating conditions were operated under full load and half load to facilitate the acquisition of operating data under different loads. Figure 8a,b shows the distribution of vibration acceleration signals in the time domain and frequency spectrum in various operating conditions under other loads.  
Figure 7: CNN architecture diagram.  
Finally, in this research, as shown in Table 3, the captured initial vibration acceleration signal was converted through different signal processing and used as the input source of the diagnostic model. As shown in Table 4, we designed the corresponding dataset according to the needs of the case analysis. In terms of actual balanced data, they were mainly used as a control group for data imbalance cases, as shown in Table 5. In terms of  
Figure 8: Five operating states in the time domain and frequency domain diagrams: (**a**) Half load; (**b**) Full load.  
using the low load side as the training set. Finally, the overall average accuracy is more observable. The accuracy of the signal after STFT and WT can reach up to 97%, which is significantly higher than the other two. Time-frequency analysis is effective in fault diagnosis, and frequency-domain signals are better than time-domain signals to handle problems with different load conditions.
# Development and Testing of a Railway Bridge Weigh-in-Motion System
## 3 RB-WIM Installation and Testing
A desirable characteristic of a B-WIM installation is that any intervention on the track side is avoided, an important advantage from a safety and maintenance perspective. Therefore, it was envisaged that the sensors on the stringer beams, right under the sleepers, would be used for axle detection. However, signals from the passing trains revealed that the axle loads distributed over the entire rail-sleeper-bridge system did not provide sharp peaks to identify individual axles in a bogie (double or triple axles, Figure 8). Thus, the sensors were moved from their initial locations to the bottom flange of the rail between two sleepers. Field testing was performed between 20 th May and 25 th May 2013. Over the first two days, the sensors and the system were installed. On 22 nd May, the first of four calibration/test trains, which were weighed off-site, passed over the bridge. Signals from three other pre-weighed trains were also captured; two on 24 th May and one on 25 th May. The four calibration/test trains were weighed on a low-speed weigh-in-motion scale in a railway yard in Warsaw that operates at speeds of up to 5 km/h. All of these trains consisted of a 6-axle locomotive and 25 to 38 carriages of different length, axle configuration, and loading. Due to the limitations of  
Figure 8: Signals collected from the stringers beams and ADMP. Figure 7: Sensor locations (plan view of bridge at track level). the low-speed device, only gross weights of carriages, without individual axle loads, were available for comparison with the B-WIM results.