esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 The SNR of MFIR Filters
### The Scaling Factors  
The equations derived in Section 3 adapted to MFIR structures are given in Table 1.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Forward ordering** & **Reverse ordering** \\ \hline Real pole linear phase & \(0\to p_{i}=2^{i+2}-2\) \\ Complex-conjugate pole pair & \(0\to p_{i}=2^{i+2}-2\) \\ Complex-conjugate pole pair linear phase & \(0\to p_{i}=2^{i+3}-4\) \\ \hline
**Reverse ordering** & \(f_{i}\left(n\right)\) or \(\overline{f_{i}\left(n\right)}\) \\ \hline Real pole & \(0\to p_{i}=2^{P}-2^{i}\) \\ Real pole linear phase & \(0\to p_{i}=2^{P+1}-2^{i+1}\) \\ Complex-conjugate pole pair & \(0\to p_{i}=2^{P+1}-2^{i+1}\) \\ Complex-conjugate pole pair linear phase & \(0\to p_{i}=2^{P+2}-2^{i+2}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Length of the impulse responses of the partial MFIR transfer functions.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Forward ordering** & **Reverse ordering** \\ \hline L\({}_{2}\) bound & \(\prod\limits_{m=0}^{i}S_{m}=\left(\left\|F_{i}\left(e^{j\alpha_{i}}\right)\right\| _{2}\right)^{-1}\) & \(\prod\limits_{m=i}^{P-1}S_{m}=\left(\left\|F_{i}\left(e^{j\alpha_{i}}\right) \right\|_{2}\right)^{-1}\) \\ Infinity bound & \(\prod\limits_{m=0}^{i}S_{m}=\left(\left(\sup\limits_{-\pi<\alpha_{m}<\pi} \left(\left|F_{i}\left(e^{j\alpha_{i}}\right)\right|\right)\right)^{-1}\) & \(\prod\limits_{m=i}^{P-1}S_{m}=\left(\sup\limits_{-\pi<\alpha_{m}<\pi<\pi} \left(\left|F_{i}\left(e^{j\alpha_{i}}\right)\right|\right)\right)^{-1}\) \\ Absolute bound & \(\prod\limits_{m=0}^{i}S_{m}=\left(\sum\limits_{m=0}^{\pi_{i}}\left|f_{i}\left(n \right)\right|\right)^{-1}\) & \(\prod\limits_{m=i}^{P-1}S_{m}=\left(\sum\limits_{m=0}^{\pi_{i}}\left|f_{i}\left(n \right)\right|\right)^{-1}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Scaling factors for Multiplicative Finite Impulse Response (MFIR) structures.  
In case of absolute bound scaling, the length \(p_{i}\) of the impulse response \(f_{i}(n)\) must be known. As this length is filter dependent, Table 2 gives an overview of the impulse response lengths for forward and reverse ordering of MFIR stages [2,19].  
It is shown in [19] that in case of a real pole approximation, the scaling factors only depend on the stage on which they are applied, _i.e._, not on any of the previous or next stages.
## 3 Results and Discussion
### Electrochemical Properties of the Polymers  
To investigate the electrochemical properties of the polymers, cyclic voltammetry was used. Figure 2 shows the cyclic voltammograms of PDTSDTBTDI-DMO and PDTSDTBTDI-8 taken at 100 mV/s on a Pt electrode in Bu\({}_{4}\)NCIO\({}_{4}\)/CH\({}_{3}\)CN. The polymers' LUMO and HOMO values were determined from the onsets of reduction and oxidation potentials, respectively. The calculated values are summarized in Table 3. The onsets of cyclic voltammograms were determined on drop-cast polymer films on the platinum electrode as the working electrode of the reference electrode Bu\({}_{4}\)NCIO\({}_{4}\)/CH\({}_{3}\)CN (0.1 M) versus Ag/Ag\({}^{+}\). The HOMO values for both PDTSDTBTDI-DMO and PDTSDTBTDI-8 polymers are equivalent although the former polymer's LUMO energy level is lower than that of the latter. These findings show that adding different side chains onto BTDI units had an insignificant effect on the polymers' degree of HOMO. However, around 0.1 eV, the LUMO levels of the polymers showed an influence. The donor unit's nature dominated the HOMO energy level, and the DTS donor unit was the same for both polymers. The polymers had a slightly deeper HOMO level than did PDTSDTBTz, which was found to be \(-5.18\) eV. The HOMO energy levels of both polymers were observed to be more profound in comparison to those found for PDTSBT, PDTS\({}_{\text{EH}}\)DTBT, PDTS\({}_{12}\)DTBT and PDTSDTTTz-4 analogues (\(-5.05\), \(-4.99\), \(-5.02\), and \(-5.04\) eV, respectively). However, the LUMO energy levels for both polymers were lower than those observed for PDTSBT, PDTS\({}_{\text{EH}}\)DTBT, PDTS\({}_{12}\)DTBT and PDTSDTTTz-4 analogues \(-3.27\), \(-3.17\), \(-3.19\) and \(-3.41\) eV, respectively. In addition to that, the two polymers' LUMO levels were also found to be significantly lower than the PDTSAZ and PDTSDTTAZ values, where \(-2.76\) and \(-2.81\) eV were found, respectively. This could be attributed to the BTDI unit's having a stronger electron-accepting ability than the benzothiadiazole, dithienylbenzothiadiazole, thiazolobtiiazole,triazoleole and dithienyltriazoleole units. Conversely, compared to their PDTSTPD counterpart, the two polymers showed shallower HOMO and LUMO energy levels.
## 5 Conclusions  
Soil slopes in two different conditions such as saturated and unsaturated conditions, are analyzed considering the horizontal seismic coefficients. Two hundred types of material properties were considered, and finally, correlations between shear strength, slope angle and shear stress are developed as Equations (9) and (10).  
* From Equations (9) and (10), it is clear that the higher the value of \(\beta\) and \(\sigma\), the value of \(\tau\) will be lower. The relationship could be checked for any slope stability case. For any other slope stability project, the shear strength can be obtained by simply applying Equations (9) and (10). The factor of safety can also be obtained by dividing shear strength by shear stress. These equations can be used to compute the shear strength of any soil slope in the given material properties range.
* The applicability of Equations (9) and (10) is above 99 percent.
* The same equations can be extended to other material types with the same procedure of analysis. If a material's properties exist between the specified values, then the shear strength can be calculated with interpolation. The same work can be extended to analyze the seismic condition by also considering the vertical seismic coefficient to get a clearer idea of the shear strength variation in complex conditions.
* In future work, the correlation of shear strength, factor of safety and slope angle with the variation of the width of an embankment will be studied.  
Conceptualization, investigation, methodology, writing original draft by M.I.K. and resources, supervision, funding by S.W. All authors have read and agreed to the published version of the manuscript.  
This work is conducted with support from the National Natural Science Foundation of China (Grant Nos. U1602232 and 51474050), Doctoral Scientific Research Foundation of Liaoning Province (Grant No. 20170540304 and 20170520341), China Scholarship Council (Grant No. 201806080103), Key Research and Development Program of Science and Technology in Liaoning Province, China (Grant No. 2019H2/10100035) and the Fundamental Research Funds for the Central Universities (Grant No. N170108029).  
Not applicable.  
The authors would like to thank the Applied Sciences (MDPI) journal for considering this paper for publication. The authors are also grateful to Northeastern University China for supporting this work.  
The authors declare no conflict of interest.
# Research on Climactic Chapter Recognition of a Chinese Long Novel Based on Plot Description
## 3 Proposed Method
### Climactic Chapter Recognition
#### 3.3.1 BiGRU  
As a novel text has significant sequential features, the BiGRU model [19] is adopted as the base model for climactic chapter recognition. The Gated Recurrent Unit (GRU) model is a type of Recurrent Neural Network (RNN) that introduces a simpler structure than LSTM networks. Figure 3 shows the structure of the GRU, which consists of two key gates: the update gate and the reset gate. The update gate controls how much of the previous hidden state is preserved in the current state, while the reset gate controls how much of the previous hidden state should be ignored. These gates enable GRU to manage long-term dependencies in sequences efficiently. The calculation process of the GRU can be described in Equations (17)-(20).  
\[\mathbf{r_{t}} =\sigma(\mathbf{W_{r}x_{t}}+\mathbf{U_{r}h_{t-1}}) \tag{17}\] \[\mathbf{z_{t}} =\sigma(\mathbf{W_{z}x_{t}}+\mathbf{U_{z}h_{t-1}})\] (18) \[\tilde{\mathbf{h_{t}}} =\tanh(\mathbf{W_{h}x_{t}}+\mathbf{U_{h}}(r_{t}\odot\mathbf{h_{t-1}}))\] (19) \[\mathbf{h_{t}} =(1-z_{t})\mathbf{h_{t-1}}+z_{t}\tilde{\mathbf{h_{t}}} \tag{20}\]  
where \(\mathbf{W_{r}}\), \(\mathbf{W_{z}}\), \(\mathbf{W_{h}}\), \(\mathbf{U_{r}}\), \(\mathbf{U_{z}}\), and \(\mathbf{U_{h}}\), respectively, denote the weight matrices of the GRU. The function \(\sigma\) denotes the sigmoid activation, and \(\odot\) denotes the element-wise multiplication operation. \(\mathbf{z_{t}}\) denotes the update gate at time step \(\mathbf{t}\), and \(\mathbf{r_{t}}\) is the reset gate at time step \(\mathbf{t}\). \(\tilde{\mathbf{h_{t}}}\) is the candidate hidden state at time step \(\mathbf{t}\), while \(\mathbf{h_{t}}\) is the hidden state at time step \(\mathbf{t}\).  
Figure 2: Climactic chapter recognition model.  
A unidirectional GRU processes information only in the forward direction, which limits the model's ability to capture the contextual information. To overcome this limitation, the BiGRU model extends the GRU by utilizing both forward and backward GRU layers, allowing it to capture bidirectional dependencies in the text. This bidirectional structure is well illustrated in the fusion layer of Figure 2. As shown in Equations (21)-(23), the BiGRU model makes effective use of the contextual semantic features of a novel text by concatenating the feature vectors of the forward and reverse GRU models.  
\[\overset{\rightarrow}{\mathbf{h}}_{\mathbf{t}} =\text{GRU}(\overset{\rightarrow}{\mathbf{h}}_{t-1},\mathbf{x}_{t}) \tag{21}\] \[\overset{\leftarrow}{\mathbf{h}}_{t} =\text{GRU}(\overset{\leftarrow}{\mathbf{h}}_{t+1},\mathbf{x}_{t})\] (22) \[\mathbf{y}_{t} =[\overset{\rightarrow}{\mathbf{h}}_{t},\overset{\leftarrow}{\mathbf{h} }_{t}] \tag{23}\]  
where \(\mathbf{x}_{t}\) is the input vector at moment \(t\), \(\overset{\rightarrow}{\mathbf{h}}\) and \(\overset{\leftarrow}{\mathbf{h}}\) denote the feature vectors obtained from the forward and backward GRU models, respectively, and \(\mathbf{y}_{t}\) is the feature vector obtained by the BiGRU model at the current moment.
# Password Cracking with Brute Force Algorithm and Dictionary Attack Using Parallel Programming
## 3 Proposed Techniques
### Applying Brute Force and Dictionary Attacks Using Hashcat #4  
In this technique, we used the Hashcat tool version 6.2.6 to launch two types of attacks, namely the brute force and dictionary attacks. The purpose of this investigation is to evaluate the influence of different GPUs on password cracking. Specifically, one of the GPUs used in this study supports CUDA, and we compare the performance of the two GPUs, namely Intel(R) HD Graphics 630 and NVIDIA(R) GeForce(R) GTX 1050 Ti with 4 GB GDDR5 and 768 CUDA cores. In addition, we evaluate the impact of password charset on the speed of password cracking and how password structure affects the process. By employing this approach, we aim to gain insights into the effectiveness of different GPUs and password characteristics in password cracking. The following Table 2 demonstrates the hardware and software specifications.  
A collection of passwords was generated randomly and secured using the MD5 encryption technique. We utilized the same passwords that were in previous techniques. Each password's string and corresponding hash value are illustrated in Table 3. Subsequently, we attempted to crack the passwords by providing only their hash values to the Hashcat tool.  
Hashcat is employed by specifying a script or command line with different parameters based on the experimental objective. The \"-m\" parameter specifies the encryption type, which is 0 (MD5) in this case. The \"-a\" parameter determines the type of attack to be conducted, with 3 for brute force attack and 0 for dictionary attack. The \"d\" parameter selects the device to be utilized for password cracking, where \"3\" is for CPU + GPU 0, and \"1\" is for GPU 1 (CUDA) exclusively. Finally, the character set for the experiment is determined using the \"1\" parameter for lowercase English characters, \"u\" for uppercase English characters, \"d\" for digit numbers, and \"s\" for special characters. We aim to test the impact of including special characters on password-cracking performance. Furthermore, it is our intention to assess the efficacy of various hardware setups for the purpose of executing these attacks. Table 4 shows the scripts used for different cracking scenarios.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & CPU & 7 th Generation Intel(R) Core(TM) i7-7700 HQ \\  & & Quad Core (6 MB Cache, up to 3.8 GHz) \\ \cline{2-3}  & Memory & 16 GB, DDR4, 2400 MHz \\ \cline{2-3} Hardware & Storage & 1 TB 5400 rpm hard drive + 128 GB solid state drive \\ \cline{2-3}  & GPU 0 & Intel(R) HD Graphics 630 \\ \cline{2-3}  & GPU 1 &
\begin{tabular}{c} NVIDIA(R) GeForce(R) GTX 1050 Ti with 4 GB GDDR5 with 768 CUDA cores \\ \end{tabular} \\ \hline \multirow{4}{*}{Software} & Operating System & Windows 11 Home 64-bit (10.0, Build 22,000) \\ \cline{2-3}  & Driver & GeForce Driver 531.68 \\ \cline{2-3}  & Software & Hashcat v6.2.6 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Hardware and Software Configurations.  
Figure 10: A segment of parallel code with 8 cores using dynamic scheduling in four chunks.
## 7 Experimental Results
### Communication Architectures Comparison  
The performance of the proposed communication architectures has been studied. The evaluated scenario reproduces data exchanges between matched peers using request-response at 100% of the communications load, i.e., the transmitted messages have the Maximum TCP Segment Size (1500 bytes). The criteria for performance evaluation are the following:  
* Total number of messages exchange: this refers to the total number of messages that have to be exchanged among all peers both for lookup of the match peers and for sharing information among them.
* End-to-end Latency: it contains the information obtained from the total nodes in the network regarding the lookup latency and the processing latency.  
In both cases, the number of matched nodes has been taken to be equal to 60% of the total nodes in the network, i.e., for 25 peers, the number of matched nodes will be 15. Figure 14 shows that the number of total messages exchanged with the embedded QR approach is lower than with simple QR even if the same locality-routing algorithm is used. It is worthwhile to point out that simple QR needs an additional application layer to recollect data from the match nodes, while embedded QR doesn't need this feature because the nodes respond by sending their operational parameters in the same lookup process.  
Figure 15 shows that end-to-end delay in embedded QR architecture is lower than the query-response approach because the total delay is mainly produced by the processing delay. The lookup delay can be considered insignificant when the application layer is embedded in the P2P routing layer. The total end to end delay for 25 nodes with embedded QR is around 500 ms while for simple QR is around 650 ms because its lookup latency is around 200 ms (see Figure 13). Thus, the average processing delay per node in embedded QR architecture can be estimated around 20 ms. This means that the average latency to process a TCP packet with maximum segment size in an embedded query-response design (round-trip time delay) is around 20 ms, which meet the network microgrid requirements that were outlined in Table 1.  
Figure 14: Comparison of total number of messages exchanged by different communication architectures.
# A New Windage Loss Model for S-CO\({}_{2}\) Turbomachinery Design
## 1 Introduction
Thus, understanding the windage loss mechanism in an S-CO\({}_{2}\) turbomachinery and developing a good model for the windage loss prediction are important. Most of the existing empirical models for windage loss are based on data obtained from air, water, and glycerin-water mixture tests. These models must first be validated regarding whether they can reflect the real gas effects of S-CO\({}_{2}\). Furthermore, the Taylor number or the Reynolds number range of the existing models are too narrow to apply to the typical S-CO\({}_{2}\) power cycle conditions. Therefore, a new windage loss measurement performed under the S-CO\({}_{2}\) condition is necessary to evaluate the validity of the existing empirical models. In this paper, S-CO\({}_{2}\) windage loss experiments conducted by a research team at Korea Advanced Institute of Science and Technology (KAIST) are first presented. The existing empirical windage loss models are then compared to the data obtained from experiments  
Figure 1: Windage loss comparison of CO\({}_{2}\) and air with [PERSON]’s model (for test rig geometry, r = 0.032 m, c = 0.0015 m and l = 0.2 m). performed under S-CO\({}_{2}\) conditions. A method is proposed to improve the existing models by reflecting the real gas effects better. Finally, a new windage loss model which explains the data better is proposed and compared.
# Role of Bimodal Water Retention Curve on the Unsaturated Shear Strength
## 6 Conclusions  
From this research, several conclusions can be deducted as follows:  
* Lower air-entry value and lower inflection point of soil WRC signify larger sizes of dominant macropore and micropore in PSD of soil.
* Dual porosity structure in PSD is more unlikely if there are higher percentages of fine-grained particles inside the soil.
* For matric sutions less than \(\mathrm{AEV}_{1}\), the relationship between shear strength and matric suction is linear and \(\phi^{\mathrm{b}}\) is the same with \(\phi^{\prime}\).
* For matric sutions between \(\mathrm{AEV}_{1}\) and \(\mathrm{AEV}_{2}\), the relationship between shear strength and matric suction is still linear but \(\phi^{\mathrm{b}}\) is less than \(\phi^{\prime}\).
* For matric sutions beyond \(\mathrm{AEV}_{2}\), the relationship between shear strength and matric suction is non-linear and \(\phi^{\mathrm{b}}\) is much smaller than \(\phi^{\prime}\).
* A new mathematical equation has been proposed to estimate the unsaturated shear strength of soil with bimodal water retention curve. The proposed equation has been evaluated and it is in agreement with the experimental data of the unsaturated shear strength carried out in this study.  
[PERSON], [PERSON] and [PERSON] conceptualized the study; [PERSON] and [PERSON] implemented data processing under the supervision of [PERSON] and [PERSON]; the original draft of the manuscript was written by [PERSON] and [PERSON] with editorial contributions from [PERSON] and [PERSON]; the funding acquisition was made by [PERSON]. All authors have read and agreed to the published version of the manuscript.  
The authors appreciate the financial support from the Nazarbayev University, Faculty Development Competitive Research Grant Program (FDCRGP) Grant No 110119 FD4508, and Collaborative Research Project (CRP) Grant No. 11022021 CRP1508. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the Nazarbayev University.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.
## 6 Discussion and Implications
### Practical Implications  
The proposed framework has implications for three primary stakeholder groups:  
1. Competition Organizers: Organizers should proactively assess and mitigate debt types under their responsibility, including infrastructure, configuration, documentation, and accessibility-related aspects. Tools like the stakeholder-specific questionnaire can support pre-competition audits, platform design iterations, and post-competition reflection. As shown in Figure 4, certain debt types (e.g., Data and Infrastructure Debt) have high organizer-side impact, highlighting the importance of early planning, communication, and versioning strategies.
2. Competition Participants: Participants can use the questionnaire to self-assess their submissions and development workflows, focusing on areas like code hygiene, test coverage, and algorithmic transparency. Identifying hidden debt (e.g., SATD, configuration or versioning issues) before submission enhances reproducibility and ethical compliance.
3. Educators and Instructors: Academic institutions can embed the questionnaire in AI engineering curricula to teach responsible development practices. The results from the pilot study (Section 4.6) show that students gained awareness of platform level responsibilities and software-engineering-for-AI challenges, beyond mere model performance optimization.
## 4 Discussion
When inducing 100% downlink traffic transmission, \(E_{avg}\) and \(E_{max}\) were generally in good agreement (Figure 9). However, because the iPerf3 application could not guarantee a constant allocation of 100% to the PDSCH resources (Figure 4), \(E_{max}\) was almost always higher and thus the more conservative metric. Furthermore, a good agreement was found between the theoretical extrapolation of \(E_{RE,SSB}\) to \(E_{max}\) using ratio of the radiation patterns of the PDSCH and SSB beams (Equation (1)) and the experimentally determined \(E_{max}\) based on the direct measurement of \(E_{RE,PDSCH}\) (Figure 6). Therefore, extrapolation to the maximum exposure level can be done without prior knowledge of the radiation patterns--knowledge of \(E_{RE,PDSCH}\), the subcarrier spacing, the channel bandwidth, and the TDD factor are, in fact, sufficient to estimate \(E_{max}\). However, outside the scanning range of the BS radio and/or in non-line-of-sight (NLOS), the following factors need to be considered: First, in a complex environment characterized by multiple reflections, the selected beam that maximizes the field strength at the measurement location might not correspond to the direct beam obtained assuming LOS. Second, multi-layer transmissions (i.e., different PDSCH beams transmitted in different directions, using reflections/diffractions to reach the UE) might result in a different measured \(E_{RE,PDSCH}\) compared to the more invariable \(E_{RE,SSB}\) (since the SSB beams are fixed) than expected from the radiation patterns. And finally, the accuracy of the radiation patterns at angular directions outside the scanning range, characterized by gain values well below the peak, is likely also characterized by a larger uncertainty. Therefore, in locations in NLOS or outside the scanning range of the BS radio, the experimental extrapolation to \(E_{max}\) is likely to provide a more accurate evaluation of the worst-case exposure, as it is based on actual measurements. In addition, the influence of SSBs that were simultaneously transmitted by the other sector antennas, or even from other sites, on the measurement of \(E_{RE,SSB}\) was assumed to be negligible. However, the slightly higher \(E_{max}\) values obtained from the theoretical extrapolation (Figure 6) may be caused by these additional contributions to \(E_{RE,SSB}\). This could be confirmed using a dedicated 5G NR decoder that can measure directly the receiver power per RE of the particular SSB. Finally, when the objective of the assessment is to measure the maximum actual transmit power, the \(E_{max}\) values obtained using the methods described in this paper and in [13] (i.e., the _theoretical_ maximum) shall be scaled with the relevant power reduction factor as described by the working draft of the International Electrotechnical Commission standard IEC 62232 [8,9,15].