esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Photocatalytic Antibacterial Activity  
To demonstrate the visible light efficiency of the doped Cu samples, photocatalytic antibacterial experiments were carried out under dark and visible light irradiation, respectively. The following data was also compared with the pristine TiO\({}_{2}\) in anatase and rutile phases (Figure 9). The samples were evaluated in relation to two different bacterial strains (_E. coli_ and _S. aureus_). In the absence of  
Figure 8: (**a**) UV–Vis absorption spectra and (**b**) Tauc plots of anatase (control), rutile (control) and 0.5% Cu-TiO\({}_{2}\) 650 \({}^{\circ}\)C.  
photocatalyst, the growth of both the strains persisted under light and dark conditions. On the other hand, on exposing the bacterial cells with catalyst (1 g/L) under visible light and dark conditions yielded different results.  
In dark atmosphere, no significant change in bacterial growth was observed even after 90 min. While the bacterial growth significantly decreased within 30 min of visible light irradiation (5-Log reduction shown in Figure 10); 99.9999% bacterial reduction was achieved in the presence of Cu-doped samples in a time span as short as 30 min (Figure 11). Moreover, the anatase and rutile TiO\({}_{2}\) samples showed complete disinfection within 60 and 90 min of irradiation respectively. This indicates the stability of the cell structure and the importance of light in the inactivation process.  
However, the inactivation observed is a synergetic effect of TiO\({}_{2}\), as well as Cu ions in the presence of visible light. None of the test samples in a dark atmosphere exhibited any bactericidal property. Therefore, the cease in cell proliferation is a photo-induced inactivation rather than chemo-toxic killing  
Figure 10: Photocatalytic antibacterial disinfection efficiency of anatase, rutile and 0.5% Cu-TiO\({}_{2}\) samples with _E. coli_ and _S. aureus_.  
Figure 9: Photocatalytic inactivation of **(a)**_E. coli_ and **(b)**_S. aureus_ with anatase, rutile and 0.5% Cu-TiO\({}_{2}\) 650 \({}^{\circ}\)C samples.  
of bacterial cells. The introduction of Cu significantly improved the visible absorption efficiency which effectively contributed to the enhanced bacterial inactivation kinetics as observed compared to the control samples.
## 4 Self-Selective Routing (SSR)
### Self-Healing Routing (SHR)  
SHR [24,53] employs the concept of a prioritized time-slotted transmission back-off delay and route repair routine. The first improvement on SHR is as follows: when a given node receives a packet, the node employs Equation (2) instead of Equation (1) to compute the delay before transmitting the packet:  
\[Trans\_Back\_off\_Delay=\left\{\begin{array}{l}\beta*\left(\alpha-\ \alpha_{expected}+ Random\ (0,1)\right)\ \mbox{if}\ \alpha\ >\ \alpha_{expected}\\ \beta\\ \hline\ a_{expected}^{-}\alpha+1\end{array}*Random(0,1)\ \mbox{if}\ \alpha\ \leq\ \alpha_{expected}\right. \tag{2}\]  
From Equation (2), we can determine that the computed delay is such that the nodes closest to the destination forward their packets, rather than those faraway. Furthermore, Equation (2) assumes that there are delays, and thus ensures the response of those nodes far away from the destination than the sender, according to their distance from the destination. In doing so, no packets travel further than the necessary routes, even if there are no nodes closer to the destination: Another addition to SHR is the transmission back-off delay in the slotted time, in contrast to the continuous time used in SSR [40,41,51,52]. The transmission back-off delay is given by Equation (3) as [24,53]:  
\[Trans\_Back\_off\_Delay_{SLOTTED}=\ \left\lfloor\frac{Trans\_Back\_off\_ Delay}{Wds}\right\rfloor*Wds \tag{3}\]  
where Trans\(\_\)Back\(off\_\)Delay is as given in Equation (2), and _Wds_ is the width of the used slot.
## 3 Results and Discussion
### Desorption Process  
Table 1 summarizes the results derived from the different desorption experiments. It is shown that an increase in the volume of the eluent solution did not influence the percentage of Cr(VI) desorption, reaching values of around 50%; however, the chromium concentration in the resulted solution increased as the volume of solution/carbon weight relationship decreased. The reduction of Cr(VI) to Cr(III) responded to the next reaction:  
\[4\mathrm{HCrO}_{4}^{-}+10\mathrm{H}^{+}+3\mathrm{N}_{2}\mathrm{H}_{4}\mathrm{ \cdot H}_{2}\mathrm{SO}_{4}\to 4\mathrm{Cr}^{3+}+16\mathrm{H}_{2} \mathrm{O}+3\mathrm{N}_{2}+3\mathrm{SO}_{4}^{2-} \tag{16}\]  
In the present case, Cr(VI) was reduced to Cr(III), which is described as having low toxicity [3; 5]. In addition, as a result of the low solubility of the Cr(III), it can be precipitated, leading to Cr\({}_{2}\)O\({}_{3}\) and/or Cr(OH)\({}_{3}\), which could be used as a pigment [42; 43; 44].
# Acoustic Emission-Based Method for IFSS Characterization in Single-Fiber Fragmentation Tests
## 2 Materials and Methods
### AE Measurement  
The AE measurement is performed using a Kistler 8152C (Kistler, Winterthur, Switzerland) broadband piezoelectric sensor, mounted directly in the center of the sample (see Figure 2). The sensor features a frequency range from 50 kHz to 900 kHz. A silicone grease couplant is used to facilitate the propagation of AE from the sample to the sensor. A Kistler 5125C (Kistler, Winterthur, Switzerland) pre-amplifier with an amplification factor of 10\(\times\) is used. The amplified signal is acquired by a NI 9223 DAQ (National Instruments, Austin, TX, USA) at a sampling rate of 1 MHz. LabVIEW is used to control data acquisition and store the AE signals. Throughout the test, the raw AE signal is continuously measured for all samples.  
Figure 1: (**a**) Sample geometry, (**b**) Stages of the preparation (**b1**) sample out of mold (**b2**) sample post of CNC process (**b3**) final sample post polished (**b4**) sample post test. In ((**b**) right) show the final sample after of polished process.  
Figure 2: Experimental setup for the tests. The AE sensor is placed on the sample with mounting support, with tightness sufficient to prevent relative movement that could distort the measured signal.
# A Bibliometric Analysis and Visualization of In-Vehicle Communication Protocols
## 3 Results
### Co-Authorship Analysis
#### 3.3.1 Country Co-Authorship Network  
The bibliometric analysis of publications by countries highlights the differences in research activity and its impact on productivity. Figure 7, generated using VOSviewer, shows the country's co-authorship network. Nevertheless, its mean citations per publication (12.39) and a normalized citation rate (score) of 1.25 indicate that China does produce a great deal of work; however, the impact on individual papers is not very high relative to other countries. On the other hand, Australia and Canada tend to strike an equilibrium between volume and impact. Australia quenches its publication thirst with 70 publications but receives a remarkable 2153 citations, averaging over 30 citations per article and a normalized score of 2.22, the highest in the dataset. Similarly, Canada records 97 publications with 2082 citations, bolstering the average to 2.24, a high citation rate, emphasizing their sustained research impact. Numerous European and other developing nations meaningfully impact the research scope as well. Germany and France have solid outputs of 157 and 77 documents, though their average citation impacts of 8.21 and 10.24 still lag behind the global leaders. At the same time, however, Brazil, Egypt, and Denmark boast a substantial citation impact for their modest publication numbers, which imply focused, high-caliber work. Overall, countries that manage a balance of high output and high average citation scores tend to have the most substantial impact on the evolution of the discipline [22].
## 4 Results and Discussion
### Threat Level Assessment (End-to-End System)  
To evaluate the proposed system performance, the threat level metric provided for each frame of a given scene is evaluated across multiple frames. The successful output of this value is evaluated by the full system for both datasets UTI (Figures 10-12) and Oxford (Figure 13). It should be noted that it is not the absolute values of the threat level that are significant but the increments or decrements between the frames.  
Considering Figures 10-12, it can be observed that the threat level increases from top to bottom frames as 14.7, 16.9 and 20.0. From the first frame to the second frame (Figure 10 to Figure 11), we can see the distance activity matrix brightening in the right top and left bottom edges. This is due to the close proximity of persons ID 1 and 4. This leads to an increase in the threat level of the frame by \(16.9-14.7=2.2\). Similarly, when looking at the first and third frames (Figures 10-12), this time, the interaction activity matrix brightens up in the third frame due to the handshake interaction in this frame. This also leads to an increase in threat level, which is by \(20.0-14.7=5.3\). It is also clearly observed in the threat activity matrix for the third frame in Figure 12, where the center squares brighten up to show a significant threat between persons 2 and 3. This increment (of 5.3) in the threat level is higher than the previous comparison (of 2.2) in Figure 10, and Figure 11 since the handshake interaction poses a higher threat than proximity alone. The same can be observed by comparing the second and third frames.  
A simpler situation is analyzed in Figure 13. Here, there are only two people belonging to the same group, and they are present in the video throughout the time. However, there are no physical interactions such as shaking hands. Therefore, the only parameter that dictates the threat level is the number of people and their interpersonal distances in each frame. When analyzing Figure 13, the people in the first frame are moving away from each other until the second frame. This is why the threat level goes down from 95.0 to 46.0 from the first frame to the second. In the third frame, new people come into the frame, and they move closer to each other. Therefore, an increase in the threat level of 105.3 is observed. However, this dataset does not contain a rich set of scenes to evaluate all components of the proposed system.  
Figure 11: Full system result of UTI interaction dataset at \(t_{2}\).  
Figure 12: Full system result of UTI interaction dataset at \(t_{3}\).  
Figure 13: Full system result of oxford dataset.
# Route Risk Index for Autonomous Trucks
## 4 Results
### State Risk Distribution  
Figure 10 is a grouped bar chart displaying the average components of the risk index for each state. The states are along the x-axis, ordered by the average risk index from highest to lowest. The y-axis represents the average value of each risk component. Notably, the variation across states is less than the variation across regions, as shown previously in Figure 9. The chart's color-coding identifies four different risk components, as indicated in the legend. The segmentation of each state's bar into these four colors shows the contribution of each risk component to that state's average risk index. This visualization allows for cross-state comparisons of how different risk factors contribute to the overall risk index. For instance, the chart shows that in the state with the highest average risk index, precipitation is the largest contributor, followed by IRI, AADT, and then fatalities. The pattern appears consistent in several states with the highest risk indices, indicating that precipitation and road conditions are significant contributors to the risk index across various states.  
Figure 9: Ranked risk indices by route and region.  
Figure 11 is a choropleth map of the United States, where the color code for each state is the deviation of its average risk index from the mean, measured in standard deviations. This type of map visually conveys the geographic distribution of derived risk indices across states. States colored in blue and red have risk indices that are more than one standard deviation below and above the mean, respectively. The other colors show states with intermediate risk indices, as indicated in the legend. States with higher risk levels are mostly in the Southeast and Mideast. In contrast, the Rocky Mountain and Plains parts of the country exhibit lower-than-average risk levels.  
The bar chart and accompanying map serve as tools for stakeholders assessing the variation in risk profiles across states, informing federal and state-level decision making on resource allocation for road safety improvements crucial to AT deployment. They can highlight where investment in infrastructure is most needed, particularly in areas where precipitation and road quality are predominant risk factors, thereby enhancing the safety and efficiency of autonomous trucking operations. Moreover, these visualizations aid in understanding regional disparities in traffic volume, road conditions, weather patterns,  
Figure 11: Risk index by state.  
Figure 10: Ranked risk indices by state.  
and safety statistics, which collectively shape the risk landscape. The insights derived from these resources highlight the need for tailored interventions or technology in autonomous trucking routes to address specific regional risk profiles, ensuring safe and efficient transport across the national network.
## 5 Results and Discussion
### Comparison of Rock-Breaking Mechanism of 3-RDE Cutter and Conventional Cutter
When the cutter moves forward continuously, the cutting surface squeezes the frontal rock, and finally the rock loses the coupling force and breaks. Figure 11 shows the maximum principal stress nephogram of the rock in rock breaking with the conventional cutter and 3-RDE cutter. From Figure 11, one can notice that there exists a large tensile stress region on the rock surface under the action of 3-RDE cutter, especially the frontal region of the convex ridge, where the tensile stress is the largest. Despite the tensile stress also occurs under the action of conventional cutter, both the amplitude and distribution region are much smaller than that in the 3-RDE cutting process. Generally, the tensile strength of rock is much less than the shear strength,  
Figure 8: Contact stress in rock-cutting process: (**a**) Conventional cutter; (**b**) 3-RDE cutter. Figure 9: Plastic strain in rock-cutting process: (**a**) Conventional cutter; (**b**) 3-RDE cutter. so the increase of the tensile stress region will be beneficial to the rock breaking, which undoubtedly results in a higher rock breaking efficiency. In summary, due to the different shape, the rock breaking mechanism of the conventional cutter and the 3-RDE cutter is different: (1) The conventional cutter breaks rock mainly by shearing. While the 3-RDE cutter not only shears the rock in the same way as the conventional cutter, but also delivers a crushing action similar to a roller cone insert. (2) The cutting edge and working face of the conventional cutter synchronously interact with the rock, while the 3-RDE's convex edge and working surfaces asynchronously break the rock. The forces required by the 3-RDE cutter are smaller, and the cutting process is more stable and efficient.
###### Abstract  
Introduction: Pain management of patients with chronic pelvic pain syndrome (CPPS) is challenging, because pain is often refractory to conventional treatments. Botulinum toxin A (BTX-A) may represent a promising therapeutic strategy for these patients. The aim of this systematic review was to investigate the role of BTX-A in CPPS treatment. Methods: We reviewed the literature for prospective studies evaluating the use of BTX-A in the treatment of CPPS. A comprehensive search in the PubMed, Scopus, Web of Science, and Cochrane Central Register of Controlled Trials databases was performed from English language articles published between January 2000 and October 2021. The primary outcome was to evaluate pain improvement in CPPS after BTX-A treatment. Pooled meta-analysis of the included studies, considering the effect of BTX-A on pain evaluated at last available follow-up compared to baseline values, was performed together with meta-regression analysis. Results: After screening 1001 records, 18 full-text manuscripts were selected, comprising 13 randomized clinical trials and five comparative studies. They covered overall 896 patients of both sexes and several subtype of CPPS (interstitialystitis/bladder pain syndrome, chronic prostatitis/prostate pain syndrome, chronic scrotal pain, gynecological pelvic pain, myofasical pelvic pain). The clinical and methodological heterogeneity of studies included makes it difficult to do an overall estimation of the real effect of BTX-A on pain and other functional outcomes of various CPPS subtypes. However, considering pooled meta-analysis results, a benefit in pain relief was showed for BTX-A-treated patients both in the overall studies populations and in the overall cohorts of patients with CPP due to bladder, prostate, and gynecological origin. Conclusions: BTX-A could be an efficacious treatment for some specific CPPS subtypes. Higher level studies are needed to assess the efficacy and safety of BTX-A and provide objective indications for its use in CPPS management.  
chronic pelvic pain; botulinum toxin A; bladder pain syndrome; prostate pain syndrome; scrotal pain; myofasical pain; gynecological pelvic pain  
Updated systematic review investigating the efficacy of Botulinum Toxin type A in the treatment of Chronic Pelvic Pain Syndrome.
## 1 Introduction
At the theoretical level, LSTM is more suitable for real-time time series analysis and prediction; however, the experimental result shows that CNN achieved higher accuracy in prediction before the betting game. [PERSON] et al. [27] proposed a new Temporal Graph Convolution (TGC) concept. Given the sequence features of N stocks and the multi-dimensional binary relationship, TGC can relearn and obtain new sequence features. This research combines LSTM and TGC to perform correlation calculation and ranking to weighted stocks and uses this correlation to predict the return on investment of the market. Its research has obtained a very high return on investment in the backtest of NASDAQ and NYSE. [PERSON] et al. [28] proposed a Deep Q network (DQN) combined with a convolutional neural network. The model takes stock trading volume and closing price graphs as input and is trained on data from different countries. The results show that the proposed model has the characteristic of transfer learning and can be trained on relatively large data and tested on a small market. In terms of return on investment and model adaptability, [PERSON] et al. and [PERSON] et al. are the current state-of-the-art models. Subsequently, more in-depth network models were applied to different trading markets and financial commodity price predictions [29; 30; 31; 32; 33; 34]. In our previous research [35], we explored the effectiveness and practicality of various financial analysis technical indicators in the time series deep learning network. This research uses well-known Moving Average (MA) technical indicators to design the feature selection algorithm, and uses the four-layer LSTM to conduct the actual measurement. The experimental target is TMSE 0050 ETF, and the results confirm that the technical indicators can effectively improve the model prediction accuracy in the time series deep network. Based on the above literature discussion, it can be found that most of the current research on the application of deep network to financial products is mainly based on price forecasting. Most of the models do not use financial technical indicators, nor do they focus on trading strategies. This study introduces technical indicators into the deep neural network, and studies whether the model output is effective as a trading strategy reference directly. This research combines Back-propagation Neural Network, GRU and Attention Mechanism to design a deep learning-based stock trading strategy model, and combines technical indicators for empirical analysis. The remaining chapters of this study are as follows: The second section discusses time series DNN models, the third section introduces the proposed framework, the fourth section is the experimental results and analysis, and the final section gives the conclusion and future works.
## 2 Related Works  
Addressing smart energy management also implies to focus on how energy is supplied and also on how it is used.  
There are many solutions to monitor and manage energy systems also based on the use of WSN. For example, in [8] a WSN was proposed as a solution for energy evaluation and planning in an industry plant, after considering the disadvantages of the currently wired infrastructures used for data communications, such as high installation costs. WSNs are ideal for the implementation of low-cost solutions for energy evaluation in industrial plants.  
In [9], another application based on WSN was proposed and in particular a scheme for energy usage evaluation and condition monitoring for electric machines was considered. The importance of this scheme lies in its non-intrusive, intelligent, and low-cost nature of the adopted solution.  
The smart home energy management system (SHEMS) presented in [4] is a solution to address efficiency in energy consumption at home level, known as _green home_. The solution integrates diversified physical sensing information and control of various consumer home devices, through active sensor networks having both sensor and actuator components.  
In this paper, we propose our solution that is a real time tool able to analyze power consumption of an industrial site based on a three layers architecture. The main objectives are (1) a low-cost solution, (2) plug and play installation and (3) easy maintenance. Also we describe the features of the adopted WSN solution, the practical problems, the constraints and design decision tuned on the real objectives. In particular, we adopted a multi-hop routing protocol for WSN able to self-organize the network, to assure flexibility and scalability and finally to assure reliability. Another interesting component of the system is the adopted middleware able to solve the technological integration problems and to manage the big amount data acquired from the sensors.  
With respect to the architecture proposed in [8,9] our proposal gives a complete integrated hardware and software architecture to monitor energy consumption of a wide industrial plant with a lot of multifunction network analyzers to check. In order to accomplish our mission, we paid particular attention to reliability of the data communication by using a swarm intelligence based routing algorithm; the advantages we obtained have been twofold: (1) a very reliable communication; and (2) a self-organizing network with plug and play capabilities.  
The basic idea implemented in our application is similar to that presented in [4] for the communication technology used. In that case, the authors create a very advanced tool with context aware capabilities thanks to the different kinds of information to manage and the possibility to drive devices according to the environmental events; to this aim they also implemented an ad hoc routing protocol to efficiently manage data exchange. Instead, our application does not support any context aware behavior due to the homogeneity of managed information and the well known location of monitored devices.