esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Verification and Validation of the Mathematical Model  
The mathematical model as mentioned above was solved using the finite volume method; thus, it was necessary to develop a computer simulation tool that could follow the solution methodology described in Section 4. The numerical code was developed in the Fortran programming language. First, the mathematical model was used to solve a reference exercise available in the literature to verify that it was able to provide reliable results. It was verified by comparing its results against the analytical solution for a composite solid reported by [PERSON] and [PERSON] [21]. Then, the mathematical model was subjected to another test known as validation, where the results of the model were compared with data from temperature measurements from a roof under outdoor ambient conditions. To validate the numerical model that solved the heat conduction equation, experimental data from a previous publication of the author were used [22]. The model was validated for two cases: a conventional gray roof and a white reflective roof. In the experiment performed by [PERSON] et al. [22] with two test cells, the exterior surface temperature of the roofs was measured every 10 min for five days. Figure 3 shows the temperatures obtained by solving the model and the experimental temperatures. This figure shows that the model satisfactorily reproduced the behavior of the gray roof and the white reflective roof. The maximum deviations of the temperatures obtained for the gray and white roofs were 5.5% and 4.6%, respectively. Therefore, this model can be used to study the thermal performance of concrete roof slabs in different weather conditions. One advantage that the current model brought is that most of the validated tools available were validated for a single day [17; 23]. In contrast, the model presented by the author was validated by using data from an experimental test performed for five consecutive days. In other words, this tool was validated by comparing its results with 720 temperature values for each type of roof.
## 4 Microanalysis  
### X-Ray Diffraction Quantitative Analysis  
Figure 11 displays XRD patterns and component ratios of red clay and lime-ash-stabilized soil at different curing durations. As depicted in Figure 8c, the predominant constituents of red clay encompass minerals like quartz, kaolinite, and illite. Following the introduction of lime and fly ash, a notable alteration in its mineral composition is discernible. The emergence of calcium carbonate and calcium hydroxide signifies that the incorporation of lime-ash has induced structural modifications in the red clay. Figure 8a reveals that there are negligible alterations in XRD diffraction angles and components for both lime-ash-stabilized soils at different curing periods. This implies that the integration of phosphogypsum into the clay has engendered a relatively stable soil structure. Upon introducing phosphogypsum into the lime-ash-stabilized soil, the formation of gypsum dihydrate and the reduction in the proportion of calcium hydroxide indicate additional reactions between phosphogypsum, lime, and fly ash, culminating in the generation of a small quantity of ettringite. Nonetheless, XRD patterns failed to manifest characteristic peaks of ettringite after 7 days of curing due to the brief reaction duration and the minimal amount produced. After 28 days of curing, the proportion of ettringite increased, and the volumetric expansion resulting from ettringite formation further occupied some pores, thereby diminishing soil porosity, bolstering pore structures, and significantly augmenting the strength of the stabilized soil. As curing time progresses, reactions between internal materials in the samples become more extensive, and the proportion of ettringite gradually escalates. Nevertheless, the rise in ettringite content between the 60- and 90-day curing durations is insignificant. To summarize, the generation of ettringite consequent to the judicious  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Phosphogypsum** & **Initial Moisture** & **Linear Shrinkage/\%** & **Shrinkage Limit/\%** & **Shrinkage Coefficient** \\
**Content/\%** & **Content/\%** & **Linear Shrinkage/\%** & **Shrinkage Limit/\%** & **Shrinkage Coefficient** \\ \hline
0 & 31.9 & 2.63 & 20.55 & 0.154 \\
4 & 34.3 & 5.50 & 20.06 & 0.368 \\
8 & 35.1 & 6.30 & 18.83 & 0.421 \\
16 & 36.0 & 6.70 & 18.79 & 0.486 \\
24 & 36.6 & 7.08 & 18.33 & 0.487 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Shrinkage index of improved soil.  
Figure 10: Improve the swelling and shrinking characteristics of soil: (**a**) The relationship between phosphogypsum content and shrinkage of soil; (**b**) The relationship between phosphogypsum content and swelling of soil.  
addition of phosphogypsum to lime-ash-stabilized soil constitutes the primary impetus for enhancing soil strength, and protracted curing periods yield heightened strength.
# Day-Ahead PV Generation Scheduling in Incentive Program for Accurate Renewable Forecasting
## 2 PV Generation Scheduling Problem in the Incentive Program  
### Renewable Energy Incentive Program  
In the day-ahead market of Korea, PV sites forecast their PV generation and schedule their net output for market offerings. In order to reduce energy deviations from PV generation, incentives are provided when the difference between actual output and the generation schedule is lower than a certain level [19].  
For a given time step \(t\), the energy deviation can be calculated as follows:  
\[e_{t}=\frac{|output_{t}-a_{t}|}{CAP}\times 100\% \tag{1}\]  
where \(e_{t}\) is the energy deviation at time \(t\), \(output_{t}\) is the actual net PV generation, \(a_{t}\) is the scheduled net PV generation at time \(t\), and \(CAP\) is the capacity of the PV generator. At each time step, the actual output is compared to the generation schedule to calculate the energy deviation and determine the incentive unit price. The unit price of the incentive is determined on an hourly basis, which means that if the energy deviation at any given time is greater than 8%, no incentives will be available at that time. In addition, if the daily average energy deviation is greater than 8%, that PV site will not qualify for the incentive.  
PV generation can be variable depending on weather conditions, but it is possible to adjust net PV generation by curtailing PV output or using ancillary resources, including energy storage systems. Therefore, the net PV generation can be calculated as follows:  
\[output_{t}=PV_{t}-curt_{t}-ess_{chg,t}+ess_{dis,t} \tag{2}\]  
where \(PV_{t}\) is the PV generation at time \(t\), \(curt_{t}\) is the PV curtailment at time \(t\), and \(ess_{chg,t}\) and \(ess_{dis,t}\) are ESS charge and discharge amount at time \(t\), respectively.  
The net PV generation is scheduled and offered to the energy market one day in advance. Depending on the energy deviation, the incentives are provided as follows:  
\[I_{d}=\sum_{t=1}^{24}output_{t}\times i_{t} \tag{3}\]  
where \(I_{d}\) is the daily incentives, and \(i_{t}\) is an incentive unit price at time \(t\). Figure 1 shows the incentive unit price that is changed depending on the energy deviation.  
In order to maximize the revenue of the PV site in the incentive program, optimized net generation scheduling is required, considering the ESS operation and curtailment based on the given scheduled PV output.
# Optimal Path Routing Protocol for Warning Messages Dissemination for Highway VANET
## 5 Performance Evaluation
### Packet Loss Rate  
This parameter is the ratio of dropped packets, which may be calculated as follows:  
\[P_{r}=\frac{\Sigma_{i=1}^{p_{i}}P_{li}}{P_{t}}, \tag{9}\]  
where \(Pr\) denotes the packet loss rate, \(Pli\) is a dropped packet, and \(Pt\) is the total number of packets delivered across the network.  
Due to the repeated network changes, the path choice during WM transmission remains crucial. In VANETs, topological changes are frequently caused by fast nodes traveling in various directions. The nodes continuously move, enter, and exit each other's communication ranges. As a result, paths are frequently dropped and new ones generated, potentially leading to network partitions. As a greater number of nodes enhances network connectivity and lowers packet drops, the probability of such network partitioning seems higher in scatter networks than in dense networks. The packet loss ratio decreases as the density increases because nodes are closer. Hence, the proposed protocol with clustering and the compared protocols without clustering becomes more intimate in the results. The three protocols' results illustrated in Figure 5a-e reveal such behavior.
## 3 Results and Discussion
### Comparison with Modern Animals' Hair and Reference Intervals  
Unfortunately, we cannot compare Hg levels in modern animals to historical levels in the same animal species, because there are no modern animals identical to the mammoth mammals. Elephants are closest to mammoths genetically, but they have a different body and habitat. We compared the Hg concentration in hair of mammoth and yak, as they have similar characteristics (nutrition, long hair, etc.). A yak (_Bos mutus_) from the Barnaul Zoo has a low Hg level (\(0.006\pm 0.001\) ug/g) (Table 4). There are no data about Hg concentration in the hair of yak from other regions of the world, but concentrations of other elements were found to be comparable between Altai and Asian yak, indicating that the differences in their exposure to metals are insignificant for the vast territories of their habitats [56; 57]. Hg concentrations in all mammoth mammals' hair samples were significantly higher than Hg concentration in unpolluted hair samples of modern animals such as cattle (0.0066 \(\pm\) 0.0002 ug/g) (Table 4). Methods for determining reference ranges in hair by using results from a large human population are described in detail elsewhere [11]. The reference intervals and 90% confidence intervals for the lower and upper limits were calculated for hair trace-element content in cattle (_Bos taurus_) per the recommendations of the American Society for Veterinary Clinical Pathology Quality Assurance and Laboratory Standard Guidelines [48; 58]. Concentrations of Hg in mammoth fauna mammals' hair mostly lie within the optimal reference range for cattle (Table 4), excluding two highest concentrations,which apparently reflect high environmental exposure to mercury in these mammals during the last periods of their lives. For plant-eating animals, vegetation is one of the main factors characterizing the living conditions [20], although Hg accumulation by animals depends both on their diet and habitat.
# Dynamic Response of Drill Floor Considering Propagation of Blast Pressure Subsequent to Blowout
## 5 Structural Analysis  
### Implementation to Finite Element Analysis  
#### 5.1.1 Finite Element Model  
The numerical analysis was performed using the explicit formulation available in the Abaqus finite element software package, and the equations of motion were solved using the central difference time integration rule [24]. The program is a special-purpose analysis tool that uses explicit dynamic finite element formulation. It is proven to be suitable for modeling brief, transient dynamic events such as blast and impact problems, and has been used in previous research [25; 26; 27]. The total number of elements and nodes are 84,788 and 81,133, respectively. The mesh size is about \(50\times 50\) mm\({}^{2}\) in order to reflect the details of the drill floor. Element types S4R and S3R were selected for the analysis. S4R stands for 4-node general-purpose shell, reduced integration with hourglass control, finite membrane strains and S3R stands for 3-node triangular general-purpose shell, finite membrane  
Figure 14: Blast pressure application and pressure configuration in time domain for (**a**) overall peak, (**b**) overall average, and (**c**) group peak.  
Figure 15: Proposed methodology for blast pressure application: group peak and propagation, (**a**) blast pressure application and (**b**) pressure configuration in time domain.  
strains. These shell element types in Abaqus use reduced (lower order) integration to form the element stiffness; however, the mass matrix and distributed loadings are still integrated exactly. Reduced integration usually provides more accurate results (provided the elements are not distorted or loaded in in-plane bending) [28].  
Figure 16 presents the configuration of the geometry and the FE model used in this study. The finite element model for the structural analysis of the drill floor shown in Figure 16b simplifies the geometry model to save analysis time. However, the details of the primary and secondary structures are well represented.
# Entity Relationship Extraction Based on a Multi-Neural Network Cooperation Model
## 2 Related Work
### Entity Relationship Extraction Based on Deep Learning  
With the rise of deep learning technology, more and more deep learning models, such as CNN (Convolutional Neural Network), RNN (Recurrent Neural Network), LSTM, and GRU, are used in entity relationship extraction tasks [18; 19; 20; 21].  
In 2013, [PERSON] et al. first proposed using the CNN method for relationship classification and encoding the input words through the dictionary of synonyms. The model mainly included a convolution layer, full connection layer, and softmax classification [22], which inspired the subsequent deep learning model for relationship extraction. In 2014, [PERSON] et al. proposed using word vectors and position vectors as the input of a convolution neural network, which could better improve the effect of relationship extraction [19]. In 2015, [PERSON] et al. [23] proposed using RNN for relationship extraction, without using any lexical features; the effect was similar to that of CNN combined with lexical features. In 2016, [PERSON] et al. [24] proposed LSTM (long short-term memory) instead of the RNN model, added an attention mechanism, proposed BiLSTM attention model architecture, and used word vectors and position vectors as feature representations of the model input layer, which improved the effect of relationship recognition.  
Most of the above studies used the traditional pre-training method [25] to vectorize the text. Among them, the Word2 vec text feature extraction model [26] released by Google in 2013 had a good effect in the traditional method, but Word2 vec is a static method and cannot be dynamically optimized for specific tasks.  
In 2018, Google released [PERSON]'s pre-training model [27], which could obtain the dependence of dynamically encoded word vectors to capture longer distances by fully extracting words and sentences [28; 29]. At the end of October 2018, it announced [PERSON]'s performance in 11 NLP (Natural Language Processing) tasks, and [PERSON] achieved good results [30]. [PERSON] et al. proposed the Roberta model in 2021 [31]. The Roberta model added batch size and training data (more than 100 GB) based on the [PERSON] model and used double-byte coding in language representation [32], which improved the accuracy of vocabulary representation and task execution efficiency.  
Therefore, based on the above comparative analysis, deep learning models perform well in relation extraction tasks, and we propose a new method of multi-neural network cooperation for relation extraction based on deep learning. The proposed Roberta-BiGRU-FC multi-neural network cooperation model uses [PERSON]'s pre-training model as an encoder and BiGRU and FC model as a decoder, and the model was optimized to better extract entity relations under the condition of a small, balanced dataset.
# Graphene-to-Phoneme Conversion with Convolutional Neural Networks
## 1 Introduction  
The process of grapheme-to-phoneme (G2P) conversion generates a phonetic transcription from the written form of words. The spelling of a word is called a grapheme sequence (or graphemes), the phonetic form is called a phoneme sequence (or phonemes). It is essential to develop a phonemic lexicon in text-to-speech (TTS) and automatic speech recognition (ASR) systems. For this purpose, G2P techniques are used, and getting state-of-the-art performance in these systems depends on the accuracy of G2P conversion. For instance, in ASR acoustic models, the pronunciation lexicons and language models are critical components. Acoustic and language models are built automatically from large corpora. Pronunciation lexicons are the middle layer between acoustic and language models. For a new speech recognition task, the performance of the overall system depends on the quality of the pronunciation component. In other words, the system's performance depends on G2P accuracy. For example, the G2P conversion of word'speaker' is 'S P IY K ER'. In TTS systems, a high-quality G2P model is also an essential part and has a great influence on the overall quality. Inaccurate G2P conversion results in unnatural pronunciation or even incomprehensible synthetic speech.
# Comparison of Key Nutrient Content of Commercial Puppy Foods with Canine Dietary Requirements
## 3 Results
### Fatty Acids
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Nutrients**} & \multirow{2}{*}{**Unit**} & \multicolumn{6}{c}{**Content**} & \multicolumn{6}{c}{**Recommendations *} \\ \cline{3-10}  & & **DEPF\_1** & **DEPF\_2** & **DEPF\_3** & **DEPF\_4** & **DEPF\_5** & **Early Growth** & **Late Growth** & **Adult** \\ \hline \multicolumn{10}{c}{\(n-6\):} \\ \hline LA & g & 2.10 & 0.55 & 1.18 & 2.85 & 3.37 & 1.30 & 1.30 & 1.32 \\ \hline AA & g & 0.01 & 0.06 & 0.05 & 0.04 & 0.03 & 0.03 & 0.03 & NR * \\ \hline \multicolumn{10}{c}{\(n-3\):} \\ \hline ALA & g & 0.58 & 0.03 & 0.23 & 0.74 & 0.38 & 0.08 & 0.08 & NR \\ \hline EPA & g & 0.03 & 0.01 & 0.02 & 0.05 & 0.07 & NR & NR & NR \\ \hline DHA & g & 0.03 & 0.02 & 0.04 & 0.05 & 0.09 & NR & NR & NR \\ \hline EPA + DHA & g & 0.06 & 0.03 & 0.06 & 0.10 & 0.16 & 0.05 ** & 0.05 ** & NR \\ \hline label EPA + DHA & g & 0.1 & 0.24 & 1.82 & 0.22 & 0.94 & – & – & – \\ \hline \multicolumn{10}{c}{\({}^{*}\) Ref. [7], NR = No Recommendation, ** recommendation for both EPA and DHA as a total. LA–Linolenic Acid; AA–Arachidonic Acid; ALA––Alnolenic Acid; EPA–Eicosapentaenoic Acid; DHA–Docosahexaenoicenoic Acid.} \\ \end{tabular}
\end{table} 
Table 6: Fatty acids (per 100 g DM) content in analyzed dry extruded puppy foods compared to the FEDIAF recommendations for fatty acids for growth of puppies and adult dogs. lower compared to DEPF_2 by 59.14% (DEPF_1), 46.24% (DEPF_3), 49.47% (DEPF_4) and 51.62% (DEPF_5).
## 3 Experimental Results
### System Performance  
In Figure 5, we investigate the system performance for three modulation formats and two different types of equalization in terms of max. ODN loss as a function of ER set at DML side. Note the DML bias current \(i_{b}=60\) mA to keep the DML operating on the safe side as well as at the optimum performance. As shown in Figure 5, the optimum ER is about 4 dB for all modulation formats. The performance in terms of max. ODN loss with 20 km fiber is very similar to that with 25 km fiber. From Section 3.2, the bandwidth of SMF (including DML chirp effects) based on IM-DD systems is about 18 GHz with 25 km fiber, and from Equation (2) we can observe that this bandwidth limitation is irrelevant if compared with DML and APD bandwidths. Therefore, the bandwidth of this DML based IM-DD link is more limited by the bandwidth of DML (from Figure 2: 12 GHz at \(i_{b}=60\) mA) and APD bandwidth. The 3-dB bandwidth of our APD is around 7 GHz [36]; the overall system is therefore mainly limited by the APD. Comparing the performance of FFE and FFE + DFE, for EDB and PAM-4, the improvement is very limited by applying FFE + DFE. PAM-2 is the only one among these three modulation formats that has a relatively large enhancement (in terms of the max. ODN loss, an enhancement of about 2 dB) when the FFE + DFE is applied. EDB and PAM-2 have the same baud rate, but the spectrum bandwidth of EDB is only the half of that of PAM-2. The spectrum bandwidths of PAM-4 and EDB are narrower than that of PAM-2. As shown in Figure 3a, in the frequency response of a 25 km SFM in a DML based IM-DD system, an overshooting can be observed from around 6 GHz to 16 GHz. This can help to compensate partially the low-pass attenuation when DML and APD are applied alone (i.e., the back-to-back case), and the overall BW of the system is increased as a result. PAM-2 outperformed EDB when the bandwidth limitation was relaxed. Therefore, in Figure 5, PAM-2 shows the best performance among these three modulation formats. On the other hand, the overshoot is not observed in O-band which has much lower CD. We have experimentally demonstrated in [10] that EDB is the best among these three modulation formats in O-band 25G-PON because of the more limited bandwidth. The performance of PAM-2 + FFE is very similar to that of EDB + FFE + DFE, and FFE is less complex than FFE + DFE. Moreover, in terms of max. ODN loss, PAM-2 + FFE + DFE is the only scenario where it can reach 29 dB, which is the typical required power budget for 25G-PON [5]. Therefore, PAM-2 represents a good choice for DML based 25G-PON in C-band.  
Figure 5: _Cont._
###### Abstract  
One of the most common mitigations against network-borne security threats is the deployment of firewalls, i.e., systems that can observe traffic and apply rules to let it through if it is benign or drop packets that are recognized as malicious. Cheap and open-source (a feature that is greatly appreciated in the security world) software solutions are available but may be too slow for high-rate channels. Hardware appliances are efficient but opaque and they are often very expensive. In this paper, an open-hardware approach is proposed for the design of a firewall, implemented on off-the-shelf components such as an FPGA (the Xilinx KC705 development board), and it is tested using controlled Ethernet traffic created with a packet generator as well as with real internet traffic. The proposed system can filter packets based on a set of rules that can use the whitelist or blacklist approach. It generates a set of statistics, such as the number of received/transmitted packets and the amount of received/transmitted data, which can be used to detect potential anomalies in the network traffic. The firewall has been experimentally validated in the case of a network data throughput of 1 Gb/s, and preliminary simulations have shown that the system can be upgraded with minor modifications to work at 10 Gb/s. Test results have shown that the proposed firewall features a latency of 627 ns and a maximum data throughput of 0.982 Gb/s.  
network security; firewall; FPGA; Ethernet; packet classification; embedded systems Article