esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Neurotoxicity is an obvious adverse effect in Patients encountering a complete course of chemotherapy. The present work is conducted to evaluate the neuroprotective effect of _Ginkgo biloba_ (_Ginkgo_) against the neurotoxicity induced by Cisplatin (Cis) in rats. Forty male Wistar albino rats were arranged into four groups: (1) Control group, rats were given saline; (2) Cis group, rats were injected by Cis 2 mg/kg body weight i.p., twice a week starting on the fifth day for thirty days; (3) Ginkgo group, rats were administered _Ginkgo_ (50 mg/kg orally) daily for thirty days; and (4) Ginkgo+Cis group, rats received _Ginkgo_ (50 mg/kg orally) daily and on the fifth day, rats were injected with Cis (2 mg/Kg body weight i.p.) twice a week for thirty days. Cis significantly increased Gamma glutamyltransferase (GGT) and Acetylcholinesterase (CHE) as compared to the control group and also disturbed cerebral oxidative/antioxidant redox. Co-administration of _Ginkgo_ and Cis reversed the adverse effect of Cis on the brain tissue. Moreover, co-administration of _Ginkgo_ and Cis ameliorated Cis induced brain damage by reducing Amyloid precursor protein (APP), amyloid \(\beta\) (A\(\beta\)), P2Y12R and P2X7R mRNA expressions and proteins. Furthermore, _Ginkgo_ regulated XIAP/BDNF expressions with a consequent decrease of caspase-3 and DNA fragmentation%. The current results concluded that concurrent treatment with _Ginkgo_ can mitigate neurotoxicity mediated by Cis in experimental animals through exhibiting antioxidant effect by restoring cerebral oxidative/antioxidant redox and anti-apoptotic effect via regulating cerebral APP/A\(\beta\)/P2Y12R/P2X7R and XIAP/BDNF signaling pathways.  
cisplatin; neuro toxicity; _Ginkgo biloba_; XIAP; APP; BDNF; purinergic receptors; caspase-3
## 3 Results  
### Magnolol Loading Capacity  
Adsorption capacity (the maximum adsorbed amount of analyte on 1.0 g sorbent) is an important factor in the evaluation of a sorbent. The final concentration of MG in the post-loading solution was 0.725 mg/mL for sample C-MG and 0.375 mg/mL sample Cf-MG. Subsequently, the calculated adsorption efficiency values were 55 mg/g of for the un-functionalized silica and 125 mg/g for the APTES-modified silica nanoparticles, respectively. The results show that by functionalization the capacity of mesoporous SBA-15 silica to load MG increased by 227%.
## 2 The Spectrum Signature of SMPSs
### Spectral Characteristics of Single SMPS
These differences makes the shape of the ripple spectrum of different SMPS units slightly different under the same working conditions [17]. The frequency spectrum near 200 KHz switching signal frequency point of three power supplies of the same type (Phoenix QUINT-PS-100-240 AC/DC/10) is shown in Figure 3, under the same input conditions (220 V, 50 Hz), the same output (24.1 V), the same load (15 \(\Omega\) cement resistance). It can be seen that the overall shape of the spectrum of three power sources is the same, but in the red box  
Figure 2: The relationship between switching frequency and output power. with the amplitude close to 0, the sector of the No. 2 power source is obviously smaller than that of No. 1 and No. 3, while No. 3 has the largest sector. This subtle difference allows the ripple spectrum to be used for branch power identification in redundant systems. However, in practical application scenarios, the output power of SMPSs changes frequently and dynamically, and the switching of the spectrum also changes with the change of the output power. These changes make it difficult for traditional waveform recognition and image recognition methods to complete the above tasks. Therefore, this paper introduces LSTM neural networks and uses the spectrum as a time-series signal to identify the waveform of the spectrum to solve this problem.
# TD-DNN: A Time Decay-Based Deep Neural Network for Recommendation System
## 4 Proposed Methodology
### Data Preprocessing Phase  
Most recommendation algorithms calculate inaccurate similarity when noisy ratings are present in the dataset. In addition, many CF-based measures do not adopt the time information while computing similarity. As a result, the recommendation of low quality might be generated. To avoid this, data preprocessing is applied in the proposed model in two steps. In step 1, the noisy ratings (i.e., inconsistent data) are identified and corrected. A time decay function is applied in the following steps to give weightage to the most recent ratings. It may improve the performance because the user preferences change over time. As the time stamp decreases, the weight decreases. Figure 1 explains the flow of the data preprocessing step. A detailed description of each step is given below:
## 4 Experiment
### Experimental Results and Analysis
#### 4.4.1 Result on the THUCNews Dataset  
This work conducts experiments on the THUCNews dataset using a comparative approach, and the experimental results are shown in Table 3. The results demonstrate that RQ-OSPTrans achieves a performance improvement of 0.1% on the test set and 0.4% on the validation set compared with the baseline pre-trained model with the best performance on the test set. Further performance improvement becomes significantly challenging due to the dataset already achieving high accuracy. In comparison with the BERT word vector loader used in this work, the performance improvement on the test set is 0.1% and 1.1% on the validation set, indicating that the method proposed in this work optimizes the output of BERT, achieving superior performance in Chinese tasks. In contrast to the BERT word embedding learning module, RoBERTa, MacBERT, and RBT6 achieve better generalization performance on the validation set, demonstrating that the mask mechanism adapted to input significantly improves performance. Meanwhile, the BiLSTM structure of the hierarchical attention network does not exhibit superior performance to the Transformer encoder on this task, falling short of the bidirectional attention sequence learning structure in BERT, proving that stacked Transformer encoder modules have significant advantages in long-text learning. RQ-OSPTrans achieves a generalization accuracy of 98.5% compared to previous models on this dataset, indicating that the residual-connected Transformer encoder structure can fully learn the parts of the text that make significant contributions to classifying topics, capturing long-distance semantic correlations.
## 6 On Control Messages Protection
### FLOOD: The Routing Protocol for UAN  
FLOOD is a routing protocol for UAN. It is composed of three phases: the _network discovery_ phase, the _data routing_ phase and the _network reconfiguration_ phase. In the _network discovery_ phase, nodes discover one another in order to build the routing tables. In practice, each node reports to the GW all the neighbors it has discovered and their relative information (e.g., link quality, water conditions). Once received all the information, the GW builds the routing tables, one for each node, by applying the Dijkstra algorithm [32] and distributes them to the corresponding nodes. At this point the network discovery phase ends and the _data routing_ phase begins. In this phase, each node routes messages according to the routing tables received from the GW. Upon a node's leaving or joining the network, a _network reconfiguration_ phase is performed.  
As to the security of the control messages and thus the integrity of the network infrastructure, the network discovery phase and the network reconfiguration phase are particularly relevant. For this reason, we briefly review them in Sections 6.1.1 and 6.1.2 as far as their salient features related to security are concerned. For a detailed description of FLOOD, readers may refer to [31].
## 3 Results and Discussion
### Parametric Analysis  
#### 3.2.1 Scenario A--Reference  
Figure 10 displays the temperature behavior of the reference model on an annual basis. The temperature variation is depicted in detail for each thermal zone of the building. From the plot, TZ01 and TZ02 display a similar behavior over the year, reaching the highest temperatures between the period ranging from the beginning of June until the end of September. In turn, the lowest temperatures are observed during the months of December, January and February. Although the global behavior of the TZ03 is similar when compared with TZ01 and TZ02, throughout the year, the daily swing of temperature is considerably lower in this thermal zone.  
Figure 8: Mean air temperature monitoring results.  
The indoor thermal comfort was evaluated according to the adaptive comfort method with the accepted deviation of the indoor operative temperature defined by EN 16798-1 [40] category II with normal level of expectation from the users. Thus, the recommended standard indoor operative temperatures are presented for buildings without mechanical cooling systems, according to the temperature limits depicted in Figure 11. For the heating season, the temperature limits were 20-25 \({}^{\circ}\)C according to the standard. Moreover, the thermal discomfort during the winter (underheating) is represented by the total number of hours (expressed in an annual percentage) correspondent to a temperature in the thermal zone below 20 \({}^{\circ}\)C. The thermal discomfort by overheating was evaluated with equal methodology, using the upper limit of the EN 16798-1 [40]. By analyzing Figure 11, it is possible to observe that indoor temperature goes under the lower limit comfort threshold (underheating) and exceeds the upper limits (overheating). This result allows it to be concluded that the reference construction conditions are not sufficient to guarantee adequate levels of thermal comfort on their own and need to be improved (e.g., with more insulation) or with the help of mechanical air-conditioning systems, especially during the heating season.  
Figure 10: Annual temperature of the reference building (indoor and outdoor air dry-bulb temperature).  
Figure 9: Correlation factor regarding simulated and monitored data.  
During the winter, the thermal discomfort caused by underheating is considerable in all thermal zones (Table 6). These results reflect, on the one hand, the poor orientation of the building, which does not allow us to take advantage of solar gains in the winter, and, on the other hand, the inadequacy of construction solutions. Thus, it can be concluded that to maintain this building within acceptable comfort ranges throughout the year, a heating system is necessary. Also, in the summer, overheating situations are detected in TZ01 and TZ03. The results in TZ01 are worsened by the existence of the sunroom, which creates an exaggerated heating effect. In TZ02, the effects are negligible.
## 2 Related Work
An anchor selection algorithm that considered neighbor distribution and residual energy was proposed to collect zonal data [24]. [PERSON] [25] pursued a novel optimization mode by considering the moving path, traffic routing, and charging time. However, data upload modes in the algorithms above were relatively single, and all nodes directly communicated with collectors on mobile vehicles. The energy consumptions of nodes are different because of the variety of upload distances to collectors. Hence, the energy balance problem in mobile data collection should also be taken seriously. Combined with our adaptive dual-mode routing mechanism, each node can choose direct upload or multi-hop upload modes according to the relative energy consumption level. The energy consumption can be balanced when the MCGV gathers data, and the network lifetime can be effectively prolonged.
# A Strategy-Driven Semantic Framework for Precision Decision Support in Targeted Medical Fields
## 5 The Emergency Strategy Case Study: Needs Analysis and Formalization
### Formalizing the Emergency Strategy
#### 5.3.1 Knowledge Graph and Pre-Processing Formalizing  
Refining the KG includes two main steps, as explained earlier. Both steps are implemented in the framework's pre-processing phase, as they do not involve the patient and remain consistent across patients.  
**A:** Identify the diseases with high risk and add a Boolean attribute that recognizes them in the graph:  
Let D be the set of nodes representing the diseases in the KG. For every disease \(d\in D\), add a Boolean attribute named risk? with the default value _false_.  
Let \(D_{risk}\subseteq D\) Be the set of diseases with high risk. For each disease \(d\in D_{risk}\), set risk? to _true_.  
**B:** Incorporate the indicators of age, gender, and pre-existing conditions into the KG: This step translates a set of rules \(R\) into conditional edges \(E_{C}\) in the processing step. Each rule \(r\in R\) represented by a tuple \(\big{\langle}s,d,f_{s,d}\big{(}i_{age},i_{g},i_{pre}\big{)}\big{\rangle}\), where \(s\) is a symptom, \(d\) is a disease, and \(f_{s,d}\) is a Boolean function that receives three personal indicators and returns _true_ if \(s\) indicates \(d\) according to the patient indicators. The set of conditional edges \(E_{C}\) are defined as follows:  
\(E_{C}=\{(s,d)|f_{s,d}\big{(}i_{age},i_{g},i_{pre}\big{)}=true\}\). These edges will be evaluated during the processing step when a patient arrives.
## 3 System Model
### Energy Dissipation Model  
Figure 2 illustrates the radio energy dissipation model in our proposal, which contains the transmitter component with the tasks such as digital coding, modulation, filtering, or spreading of the signal. In particular, the amplifying signal for transmission over long distances consumes more energy than the receiver, similar to the one discussed in [37].  
According to this, whenever a sensing node transmits a packet with \(u\) bits data on the distance \(d(x,y)\) from the sender \(x\) to the receiver \(y\), either the free space propagation (\(d^{2}\) energy loss) or two-ray ground reflection (multipath fading \(d^{4}\) energy loss) model is used depending upon the distance \(d(x,y)\). If the \(d(x,y)\) value is greater or equal to the distance threshold \(d_{cro}\), then the two-ray ground model is employed. Otherwise, the free space model will be employed, and that is given in Equation (8) [38].  
\[E_{Tran}(u,d)=\begin{cases}u(E_{circ}+E_{fsp}d^{2})&,\,\text{if}\,d<d_{co}\\ u(E_{circ}+E_{trg}d^{4})&,\,\text{if}\,d\geq d_{co}\end{cases} \tag{8}\]  
where the fixed energy dissipation of the electronic transmitter or receiver circuits is \(E_{circ}\) per bit, \(E_{fsp}\) and \(E_{trg}\) are the amplifier energy portion needed for a distance less than \(d_{cro}\)  
Figure 2: Radio energy dissipation model.  
and distance greater or equal \(d_{cro}\), respectively. The distance threshold \(d_{cro}\) is calculated in our simulation scenarios as Equation (9) [38].  
\[d_{cro}=\sqrt{\frac{(4\pi)^{2}ll_{t}^{2}h_{r}^{2}}{\lambda^{2}}}=\sqrt{\frac{E_{ fsp}}{E_{trg}}} \tag{9}\]  
Moreover, the energy needed to receive the \(u\) bits of a packet data by the radio model can be indicated in Equation (10).  
\[E_{Re}(u)=uE_{circ} \tag{10}\]
# A Network-Based Method to Analyze EMI Events of On-Board Signaling System in Railway
###### Abstract  
The On-board Train Control System (OTCS) plays an important role in the real-time operation of the electric multiple units (EMU) in high-speed railway. The EMU is a complex system made up of various electrical and electronic equipment, so the interactions of the electromagnetic (EM) environment and OTCS are difficult to study, which leads to more challenges to analyze EM interference (EMI) events at the system level. To overcome this difficulty, this paper proposes the thought of a graph model to solve the problem. First, a framework is proposed to more clearly reflect the relationship between the EMC (Electromagnetic Compatibility) problem and network through a comparison with them. Second, a network theory-based model is presented to express the EMC elements for the OTCS in EMU. It decomposes the OTCS and EMU with EMC elements into edges and nodes of the network, which parameters are defined corresponding to EM sources, sensitive equipment, and coupling paths. Thus, each part could be modeled separately or together by calculation, simulation, or measurement, respectively, and the EMC problem could be represented by the paths from origin to destination in the network. Moreover, the modeling process was elucidated by the specific cases in OTCS and the validity of the proposed approach was verified by calculation and measurement results in the case study.  
EMC; system-level analysis; on-board train control system; EM disturbance; network model; EM interactions; measurement and simulation verification +
Footnote †: journal: Applied Sciences  
Article
## 5 Discussion
### Decision Maker's Preference  
Table 9 indicates three results of OMSS for three different \(\rho\). The three results of OMSS are different in both maintenance time sequence and bridges to be maintained. Compared with \(\rho~{}=~{}0.5\), for \(\rho~{}=~{}0.3\), the OMSS generates more traffic delays, while for \(\rho~{}=~{}0.7\), fewer bridges are involved with maintenance activities. Hence, the OMSS is a tradeoff between the traffic delays and the number of bridges to be maintained based on the decision maker's preference.
# Machine-Learning-Based Optimization for Multiple-IRS-Aided Communication System
## 3 Proposed Algorithm for Static UE Scenario
### The Proposed New GRNN-Based Algorithm  
We design a new GRNN-based algorithm for the joint optimization of active and passive beamforming. The model structure is based on a new type of radial basis network (RBN) called new GRNN [33] that can effectively handle our non-linear regression problem with its continuous optimization variables. A key feature of the network is its ability to design and learn quickly. Moreover, the model's performance is further enhanced by collecting large data sets and fine-tuning its free parameters in order to converge to an optimal regression surface and gain superior training efficiency. This proposed algorithm mainly contains three parts, namely data preprocessing, data processing, and data representation. The details of each phase are described below.
# Analysis of Video Transmission Capabilities in a Simulated OFDM-Based Supplementary BPL-PLC System
###### Abstract  
The design and maintenance of a reliable communication system, especially in harsh working conditions for the oil and mining industry, brings many challenges. With the use of a video transmission system, one can monitor the crew and their working environment. Broadband over power line-power line communication (BPL-PLC) seems an ideal medium for such a service, since it enables the use of the existing wired infrastructure for supplementary applications. In this paper, we perform a set of simulations for a dedicated wired medium as well as analyses of a visual data transmission system, designed to deliver video content with quadrature amplitude modulation (QAM) and orthogonal frequency division multiplexing (OFDM). We investigate a set of video sequences at 480 \(\times\) 270 resolution under varying network conditions, including signal-to-noise ratio (SNR) and bit error rate (BER). We perform a subjective evaluation study of video content transmitted over our simulated communication link. The results of this study may aid parties involved in designing additional services for portable devices and user terminals, including reliable means of contact, surveillance and monitoring. The obtained results may be of particular interest to researchers and professionals related to the Industry 4.0 and Internet of things (IoT) concepts.  
digital systems; electrical engineering; ICT; Industry 4.0; IoT; power cable; quality evaluation; reliability; video coding +
Footnote †: journal: Accepted: 11 May 2022
# Routing Path Assignment for Joint Load Balancing and Fast Failure Recovery in IP Network
## 4 Numerical Results and Discussions
### Network Survivability
#### 4.2.1 Node Protection  
Figure 12 depicts a network with twelve nodes. The source node is node 1, while the destination node is node 12. The working traffic towards the destination node is shown by the red links. Our optimization model \"LBO\" provides us this solution (red links).  
We solve the LBO model for node protection and determine whether or not the flow is survivable. We get the routing for each OD pair after solving this model. For example, when node 6 fails, node 3 has no LFA. It means node 3 is not survivable. When node 9 fails, node 6 has no LFA. When node 11 fails, node 10 has no LFA. We analyze this for each OD pair. When node 8 fails, traffic to destination node 12 can be recovered.  
We define survivability as:  
\[\begin{split}&\text{\emph{No. of cases that the failure results in}}\\ &\text{\emph{at least one node has no LFA}}\\ &\text{\emph{Suravability}}=\frac{\text{\emph{No. of cases that the failure results in}}}{\text{\emph{at least one node has no LFA}}}\\ &\text{\emph{at least one node has no LFA}}\\ &\text{\emph{Suravability}}=\frac{\text{\emph{No. of cases that the failure results in}}}{\text{\emph{at least one node has no LFA}}}\\ &\text{\emph{Suravability}}=\frac{\text{\emph{No.  
We find the survivability ratios when the LBO problem encounters a node failure. When a node fails in the network, as illustrated in Table 4, the network is not 100% survivable. The network's survivability ratio increases as the network's size grows. The trends in survival ratio are shown in Figure 13. The survivability ratio of a network grows as the degree and number of nodes increase. As a result, when a node fails, the LBO model is not 100% survivable. As a result, our LB&NFP problem guarantees network survival. The LB&NFP problem's 14 th constraint provides 100 percent survivability.
# Reliability Modelling and Evaluation for LTD System Based on Load-Sharing Model
## 4 Numerical Simulation and Analysis  
In this section, several simulation experiments are performed to evaluate the impact of the load-sharing mechanism on the module, level and system, respectively. Besides, we also consider the module failure threshold in level and the reliability of the trigger generator unit in the system respectively. Without loss of generality, we refer to the self-breakdown experiment of the switches in Ref. [34] and assume the parameter \(\delta=1\). The experimental data are shown in Table 2.  
As described in Equation (1), the experimental data above should be subject to a 3-parameter Weibull distribution. We can get the location parameter \(\gamma=0.35\), the scale parameter \(\eta=0.6815\), the shape parameter \(\beta=17.83\). Based on parameters determined, Figure 3 shows the relationship between working coefficient and failure probability of the switch.  
Furthermore, considering the randomness of the module failure sequences, Monte Carlo method is adopted to simulate the statistic effects in some parts of analysis.
## 3 Experimental Results
### Analysis of Strain Response  
Strain monitoring is a useful and reliable method to record the state of rock, and it has been used in the experiments of [PERSON] et al. [39] and [PERSON] et al. [44]. In the experiments, the strain gages measured circumferential strain, and the strain was negative when the side surface was subjected to tensile stress in a circumferential direction.