esa-sceva/satcom-chunk-collection huggingface dataset:
# SDR-Based 28 GHz mmWave Channel Modeling of Railway Marshaling Yard
## 3 Software-Defined Radio-Based Channel Modeling
### Construction of Measurement Environment  
The transmitting end of the measurement system was placed on the bridge over the railway tracks; the height of the bridge from the rail surface is 14 m and the transmitting antenna fixed on a tripod is 1.75 m high from the bridge, as shown in Figure 10.  
The antenna of the receiving end is fixed on a tripod, which is placed at the middle of the selected track, and the receiving antenna is 1.6 m high from the rail surface, as shown in Figure 11.  
In order to meet the data collection requirements described in Section 2, two tracks are selected for testing, as shown in Figure 12; the track marked with the red circle is selected for the first and second scenarios, and is denoted as \(T_{162}\). The track marked with the orange rectangle is selected for the third scenario, and is denoted as \(T_{3}\). The data collection campaign was conduct on the corresponding track, with an interval of 5 m, for further analysis. On \(T_{162}\), data collection points with or without Catenary between the TX/RXs were separated into two categories for performance comparison.  
Figure 10: Construction of the transmitting end of the measurement system. (**a**) Bridge over the tracks. (**b**) Transmitting end on the bridge.  
Figure 9: Self-developed software based on LabVIEW.
## 3 Results and Discussion
### Softening Point  
The outcome of the softening point test, presented in Figure 5, reveals the temperature at which asphalt binders transition from semi-solid to soft states. Higher softening points are preferable for greater resilience in warm conditions. CR-modified asphalt binders exhibited higher penetration values, aligning with previous research [42]. Among WMA additives, Cecabase demonstrated the lowest softening point (47.5 \({}^{\circ}\)C), while Sasobit exhibited the highest (50.5 \({}^{\circ}\)C). The CECR asphalt binder displayed a relatively low softening point, whereas the SECR binder exhibited the highest value (54 \({}^{\circ}\)C), attributed to the combined influence of crumb rubber and Sasobit, resulting in higher melting temperatures. [PERSON] et al. [43] obtained similar results, concluding that Sasobit enhanced softening points. In a preceding study undertaken by [PERSON] [44], it was determined that the incorporation of Sasobit additive in combination with CR leads to an elevation in the value of softening points. This concurs with the findings observed in the current research.  
Figure 4: The penetration of the asphalt binders.
# The Use of Autogenous Teeth for Alveolar Ridge Preservation: A Literature Review
## 6 Undemineralized Dentin Matrix (UDD)
### Preclinical Studies  
The protocol used for the preparation of this biomaterial was very similar in all of the preclinical studies. The crown portion of the tooth was removed, as well as pulp tissues and periodontal ligament still attached [52; 53; 68]. This was made by using curettes, ultrasonicdevices, hand instruments and specific burs [52; 53; 68]. In all studies, the preparation was rinsed with a saline solution along with a basic alcohol cleanser [52; 53; 68]. Finally, either using the Smart Dentin Grinder(tm) (Kometa Bio ltd., Holon, Israel) [52; 53] or a specific grinder [68], the teeth were grinded into particles with diameters over 300 \(\upmu\)m and less than 1200 \(\upmu\)m [52; 53], or between 350 and 500 \(\upmu\)m [68].  
Histologic and Histomorphic Outcomes  
After assessing the viability of UDD under radiologic, histologic and histomorphometry analysis, some preclinical studies reached opposite conclusions. In 2015, one study, after histologic and histomorphometry analysis, reported that the use of UDD, after 8 weeks did not offered any improvement in bone regeneration, showing in terms of ratio of bone to total area of each probe 170 \(\pm\) 16 \(\upmu\)m\({}^{3}\) for the control group (no bone graft material used) and 71 \(\pm\) 14 \(\upmu\)m\({}^{3}\) for the UDD group, with a significant difference (\(p<0.05\)) [68].  
The opposite was concluded in two other preclinical studies [52; 53]. When compared to a healing without any bone graft material, after a 90 days observation period, the added benefit of the UDD was proven [52; 53]. In one study, the percentage of newly bone formation was 91.32 \(\pm\) 0.8% in the UDD group and 65.89 \(\pm\) 0.6% in the control group (\(p<0.05\)) [52]. In the other study, the percentage of immature bone was 14.2 \(\pm\) 0.66% in the UDD group and 35.17 \(\pm\) 0.74% in the control group (\(p<0.05\)) [53].
## 4 Analysis of Test Results  
### Load-Displacement Curves  
The results of the axial compression tests on all specimens were analyzed. The variation curves between the load and the longitudinal displacement of the specimens under the axial longitudinal load are presented. The main test results are shown in Table 6. Figure 6 displays the load-displacement curves for all specimens.  
In Table 6, the ductility coefficients \(\Delta_{\text{u}}\) of the specimen were calculated by the equivalent energy area method. The nominal yield displacement \(\Delta_{y}\) is calculated from the area of the load-displacement curve. Additionally, it was obtained by using the limit displacement \(u_{\Delta}\) unloaded to 85% \(P_{U}\).  
According to Figure 6, the compressive loading of the specimen could be divided into three stages:  
(i) The load-displacement relationship appeared linear at the beginning of loading and belonged to the linear elastic stage. (ii) When the loading exceeded a certain value, the load-displacement relationship began to change in a curve until it reached the peak load, which was a relatively short phase and belonged to the elastic-plastic stage. (iii) After reaching the peak load, the curve began to decrease until the specimen displayed obvious damage characteristics, and this stage was the damage stage.  
Figure 6 shows that specimens C3 to C6 had a more obvious yield plateau when the load-displacement curve entered the falling section from the rising section. In contrast, specimens C1 and C2 had almost no transition platform. Therefore, strengthening concrete columns with all-light ceramics concrete could enhance the plastic deformation capacity of the strengthened columns and improve the strengthened columns' safety performance.
# Adaptive Bat Algorithm Optimization Strategy for Observation Matrix
## 4 Experimental Simulation
### Analysis and Selection of Transformation Basis
#### 4.3.4 Analysis of the Influence of Transformation Basis on ABA  
The correlation between the transformation base and the observation matrix directly affects the reconstruction effect. Gaussian matrix is not related to most transform bases [70]. Therefore, our experiments employ Gaussian matrix as the observation matrix to prove the robustness of the ABA algorithm to optimize the observation matrix with different transform bases.  
In the signal reconstruction experiment, to test the effectiveness of different observation matrices with different transform bases, we take the \(S_{1}\) and \(S_{3}\) signals and tests the effects of different observation matrices under transform bases DHT and FFT. According to the proposed evaluation index reconstruction error (RE) and performance improvement rate (PI) values, the optimization effect of the observation matrix is reflected, and the smaller mean and reconstruction error are selected as the optimal values, and then the conversion comparison is performed. The selection and reconstruction  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Signal** & **Rate** & **Data** & **DHT** & **FFT** \\ \hline \multirow{4}{*}{\(S_{1}\)} & 0.30 & _mean_ & \(3.6916\times 10^{-15}\) & \(\mathbf{3.1462\times 10^{-15}}\) \\ \cline{2-5}  & 0.35 & _mean_ & \(3.4521\times 10^{-15}\) & \(\mathbf{3.1030\times 10^{-15}}\) \\ \cline{2-5}  & 0.40 & _mean_ & \(3.4700\times 10^{-15}\) & \(\mathbf{3.1139\times 10^{-15}}\) \\ \cline{2-5}  & 0.45 & _mean_ & \(3.3844\times 10^{-15}\) & \(\mathbf{3.0513\times 10^{-15}}\) \\ \cline{2-5}  & 0.50 & _mean_ & \(3.3120\times 10^{-15}\) & \(\mathbf{3.0155\times 10^{-15}}\) \\ \hline \multirow{4}{*}{\(S_{3}\)} & 0.30 & _mean_ & \(5.0519\times 10^{-14}\) & \(\mathbf{4.6387\times 10^{-14}}\) \\ \cline{2-5}  & 0.35 & _mean_ & \(4.9979\times 10^{-14}\) & \(\mathbf{4.4886\times 10^{-14}}\) \\ \cline{1-1} \cline{2-5}  & 0.40 & _mean_ & \(4.8152\times 10^{-14}\) & \(\mathbf{4.3008\times 10^{-14}}\) \\ \cline{1-1} \cline{2-5}  & 0.45 & _mean_ & \(4.7886\times 10^{-14}\) & \(\mathbf{4.2642\times 10^{-14}}\) \\ \cline{1-1} \cline{2-5}  & 0.50 & _mean_ & \(4.7102\times 10^{-14}\) & \(\mathbf{4.1672\times 10^{-14}}\) \\ \hline \hline \end{tabular}  
* The bold indicates that signals can be reconstructed with better results under different observation matrices and transform bases.  
\end{table}
Table 3: Reconstruction effect of different transform based on signal.  
effects of different transform bases based on adaptive bat algorithm are compared, and the robustness of the algorithm is fully proved. The results of the experiment are shown in Table 4.  
It can be seen from Table 4 that the reconstruction effect of the ABA optimization observation matrix is optimal for different transform bases. Moreover, the reconstructed error values are equivalent by comparing the two transform bases, which proves the feasibility of the ABA on different transform bases in the signal reconstruction experiment.
## 3 Methodology
### The Self-Supervised Learning in Our Framework  
As illustrated in Figure 7, pretext tasks are employed for self-supervised learning by combining contrastive learning with an exponentially weighted moving average (EWMA) technique. This method helps in learning strong feature representations from the input data. Once the pretext training is finished, the obtained weights are utilized to initialize the encoder, and the entire model undergoes fine-tuning via supervised learning.  
Contrastive learning boosts representation robustness by distinguishing similar (positive) from dissimilar (negative) pairs or by improving the alignment within positive pairs. Given an input batch \(x\), we apply random image augmentations \(\mathcal{T}\), generating two augmented views: \(x_{1}=t(x)\) and \(x_{2}=t^{\prime}(x)\), where \(t\) and \(t^{\prime}\) are drawn from \(\mathcal{T}\). These augmented versions are passed through both the base and momentum networks, each consisting of a Swin-Transformer encoder and a two-layer MLP projector. The base model \(f_{\theta}\) is  
Figure 7: The diagram of our pretraining method is shown. A random transformation is applied to the input patch \(x\), generating two different views, \(x_{1}\) and \(x_{2}\), which are subsequently input into the framework for the contrastive learning task.  
trained with backpropagation, while the momentum network \(f_{\zeta}\) updates its parameters using an exponentially weighted moving average from the base model:  
\[\zeta\leftarrow\phi\zeta+(1-\phi)\theta \tag{1}\]  
where \(\phi\in[0,1]\) is the decay rate. A one-layer MLP predictor is added to the base network to prevent representational collapse. The similarity between representations is computed via the dot product, and the contrastive loss for a pair \(z_{i}\) and \(z_{j}\) is given by:  
\[\mathcal{L}_{\text{con}}=-\log\frac{\exp((z_{i}\cdot z_{j})/\tau)}{\sum_{m=1}^ {2N}\mathbb{I}[m\
eq i]\exp((z_{i}\cdot z_{m})/\tau)} \tag{2}\]  
In this equation, \(\mathbb{I}_{[m\
eq i]}\in\{0,1\}\) serves as an indicator function that equals 1 when \(m\
eq i\), and \(\tau\) denotes the temperature parameter. As a pretext task, contrastive learning draws intra-class pairs closer together while separating inter-class pairs, thus capturing essential semantic features.
## 3 Extended System Architecture and Parameters  
The proposed cross-seeding based DWDM-PON system is presented in details in Figure 2. The general stages, which are presented in Figure 1, are re-introduced with details in Figure 2.  
It is noteworthy to mention that the main system's stage specifications and their corresponding values are mentioned in the body of Section 2 and Table 1. In the following,  
Figure 2: Detailed design for the proposed DWDM-PON system with enhanced cross seeding mechanism. MZM: Mach Zehnder modulator; EDFA: Erbium-Doped Fiber Amplifier; NRZ: Non-Return to Zero; IM: Intensity Modulator; LPF: Low Pass Filter.  
components' specifications will be addressed. The first launched wavelength is located at 193.025 THz with 0 dB launched power using a CW light source. By utilizing other seven CW light sources and the DSB-OCS technique, the system capacity is enlarged to 16 subcarriers (i.e., channels) by decreasing the channel spacing to 12.5 GHz. A 6.25 GHz sine generator that feeds a standard MZM with a 30 dB extinction ratio and an EDFA that has a 20 dB gain and a 4 dB noise figure are used in stage 2.  
In stage 3, a DeMUX with 10 GHz 3 dB bandwidth and 0 dB insertion loss is utilized. Here, channels that reached the Tx in stage 4 achieve DS modulation via an externally modulated 10 Gb/s Non-Return-to-Zero (NRZ) pseudorandom binary sequence using the previous MZM specifications. As said before, the Rx in stage 4 will be used for demodulating the returned US signal with PIN and an LPF. The PIN specifications are 10 nA dark current and 1 A/W responsivity. A 4 th order LPF with 0 dB insertion loss, 100 dB depth, and a cut off frequency of 1.875 GHz is used. As said before, the same Tx and Rx will be used again in Stage 7, which will provide simplicity and harmony to the proposed design. The MUX/DeMUX used in stage 4 and 6 for the US and DS channel selections have 10 GHz 3 dB bandwidth, 0 dB insertion loss, and 100 dB depth. The specifications of the bidirectional fibers used in Stage 5 are listed in Table 1.  
Points (a-g), indicated in Figure 2, are used to trace, test, and evaluate the proposed design, as will be provided in Section 4.
# Current Progress in Femtosecond Laser Ablation/Ionisation Time-of-Flight Mass Spectrometry
## 3 LIMS System: fs-Laser Ablation Ion Source and Time-of-Flight Mass Analyser
### Selected Femtosecond-LIMS Systems
#### 3.3.2 Miniature Femtosecond-LIMS: LMS
Implementation of double pulse laser ablation ion source increased ion yield and also proved to be a powerful method in the suppression of isobaric interferences, improvement of depth profiling accuracy and quantitative performance to chemical composition [82; 88].
## 5 Measurement of Shape Deviation
### Discussion on the Sample Measurement Results  
Based on the results of the shape deviation measurements, we can conclude that the sample with square-shaped holes has the least accurate shape of openings after 3D printing.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Hole Mark** & **2A** & **2B** & **2C** & **2D** & **2E** & **2F** & **2G** & **2H** \\ \hline Dimension of the hole [mm] & 3 & 4 & 5 & 6 & 7 & 8 & 9 & 10 \\ \hline Dimensional deviation [mm] & +0.568 & +0.735 & +0.789 & +0.641 & +0.941 & +1.190 & +0.938 & +0.978 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Dimensional deviations for holes with square cross-section.  
Figure 10: Color map of measured values for square holes.  
Figure 11: Color map of measured values for rhombic holes.  
As an example, a hole with a shape deviation of 0.568 mm is already present at an opening of 3 \(\times\) 3 mm. Other openings ranging in size from 4 to 10 mm showed a deviation of 0.7 to 1.0 mm. Ultimately, a square-shaped hole printed without support structures is unsuitable for designing an air cooler because significant deformation of the hole can occur during 3D printing on the top surface of the square aperture. Also, poorly scanned burnt layers can damage the 3D printer's wiper during 3D printing.  
Ultimately, it is up to the designer to decide how much precision they wish to consider when designing a particular part. The overall results of the experimental investigation indicate that the rhombic shape of the hole is the most suitable for the design of the air cooler cavities. The circular shape is between 3 and 5 mm, with shape deviations of 0.23 mm.  
The most suitable shape of the hole for air cooler cavities is based on rhombic and circular shapes, with shape deviations ranging from 0.1 to 0.2 mm for all hole diameters. The sample with circular holes with dimensions of 6 to 10 mm showed a deviation of 0.25 to 0.4 mm.  
The 3D printed part with several different hole shapes shown in this manuscript was compared and validated by the author's benchmark tests, which studied the effect of the printing parameters on the critical edge. During the experimental measurement, it was found that the ideal angle of the hole area for a rhombic-shaped specimen is 45 degrees. When designing holes with a larger angle (e.g., a rhombic-shaped hole), the probability of surface accuracy is higher. Figure 12 shows the print quality results and the final geometry of the five benchmark degree samples printed during 3D printing (45, 40, 35, 30, and 25 degrees).  
In this case, the most appropriate angle of inclination from the horizontal plane for the reference sample is 45 degrees. The study [42] reported that surface imperfections decreased with increasing the design angle from 15 to 75 degrees. The authors of [43] also report that the effect of inclined geometry step size significantly affects the mechanical and electrochemical properties of AlSi10 Mg samples.
# Adaptive and Blind Audio Watermarking Algorithm Based on Chaotic Encryption in Hybrid Domain
## 2 Watermarking Algorithm in Hybrid Domain
### Principle of Watermark Embedding  
The frequency band of audio signals that can be caught by human ears is mainly within the range of 300-3400 Hz. The low-frequency signal below 300 Hz and the high-frequency signal beyond 3400 Hz can be barely caught by human ears. The watermark information may be concealed into the audio signal because of the insensitivity of the human auditory system to the small changes of the high-frequency component.  
It is assumed that \(A\) represents the original audio signal, and it can be defined as:  
\[A=\{a(k),\,1\leq k\leq K\} \tag{8}\]  
where \(a(k)\) is the value of the \(k\)th sample point, and \(K\) is the length of this audio signal. Divide \(A\) into \(M\) audio segments \(A_{l}\) (\(1\leq l\leq M\)), and each segment contains \(N\) sampling points. The \(r\)-level DWT is performed on \(A_{l}\) to get the \(r\) th level wavlet coefficients \(De(r,n)\) (\(1\leq n\leq N/2^{r}\)). Divide \(De(r,n)\) into a former packet and a latter packet, namely \(De_{1}(r,j)\)\(De_{2}(r,j)\), and the two packets are shown in Formulas (9) and (10) in accordance with literature [4].  
\[D_{el}(r,j)=D_{e}(r,j),\,j=1,2,\ldots,N/2^{r+1} \tag{9}\]  
\[D_{el2}(r,j)=D_{e}(r,\frac{N}{2^{r+1}}+j),\,j=1,2,\ldots,N/2^{r+1} \tag{10}\]  
Two groups of DCT coefficients \(C_{1}(r,j)\) and \(C_{2}(r,j)\) are obtained from \(De_{1}(r,j)\) and \(De_{2}(r,j)\) by DCT, and then connect them to form \(C(r,n)\) with \(N/2^{r}\). Formulas (11)-(13) can be used to calculate the average amplitudes of \(|C(r,n)|\), \(|C_{1}(r,j)|\) and \(|C_{2}(r,j)|\) to get \(M_{l}\), \(M_{c1}\) and \(M_{c2}\).  
\[M_{l}=\frac{2^{r}}{N}\sum_{n=1}^{N/2^{r}}|C(r,n)| \tag{11}\]  
\[M_{c1}=\frac{2^{r+1}}{N}\sum_{j=1}^{N/2^{r+1}}|C_{1}(r,j)| \tag{12}\]  
\[M_{c2}=\frac{2^{r+1}}{N}\sum_{j=1}^{N/2^{r+1}}|C_{2}(r,j)| \tag{13}\]  
In order to embed the watermark, \(C_{1}(r,j)\) and \(C_{2}(r,j)\) can be modified according to the following embedding rules:  
\[C_{1}^{\prime}(r,j)=C_{1}(r,j)\times\frac{(1+\lambda w(q))M_{l}}{M_{c1}} \tag{14}\]  
\[C_{2}^{\prime}(r,j)=C_{2}(r,j)\times\frac{(1-\lambda w(q))M_{l}}{M_{c2}} \tag{15}\]  
where \(\lambda\) is the embedding depth and its span is within the interval of [0,1]. \(C_{1}^{\prime}(r,j)\) and \(C_{2}^{\prime}(r,j)\) are the watermarked DCT coefficients. Perform the inverse discrete cosine transform (IDCT) on \(C_{1}^{\prime}(r,j)\) and \(C_{2}^{\prime}(r,j)\) to get the watermarked coefficients \(D\epsilon_{1}^{\prime}(r,j)\) and \(De_{2}^{\prime}(r,j)\), and then perform the inverse discrete wavelet transform (IDWT) to reconstruct the watermarked audio segment \(A_{l}^{\prime}\).
# WordMap: Text Mining Application of Enhanced Corpus Segmentation and Semantic Topic Recognition
## 3 Research Design and Assessment Model
### Module Implementation
#### 3.2.1 Corpus Segmentation  
Corpus segmentation is essential for multilingual contexts involving Chinese and English. Chinese segmentation is challenging due to its semantic complexity. Traditional approaches are divided into three categories: rule-based, statistical, and machine learning-based methods [19]. Rule-based word segmentation methods rely on predefined linguistic rules and knowledge bases to match text [20]. This method has difficulty in accurately segmenting multiple-meaning and out-of-dictionary (OOD) words [21]. Statistical models [22], on the other hand, employ probabilistic techniques, such as Hidden Markov Models (HMMs) [23], Maximum Entropy Models (MEMs) [24], and Conditional Random Fields (CRFs) [25]. HMMs regard word segmentation as a sequence labeling task [26], but they are heavily reliant on training data. MEMs are based on the principle of information entropy and have high requirements for feature engineering [27]. CRFs have high computational complexity and inefficiency during the process of large-scale texts [28]. Therefore, these  
Figure 3: User interface flowchart.  
word segmentation methods all have obvious limitations. With the advancement of neural networks such as LSTM [29], machine learning-based methods have demonstrated superior effectiveness in sequence labeling tasks [30]. Therefore, this system employs PKUSEG [31] for Chinese text segmentation. For English, NLTK [32] is used to implement morphological normalization and stop-word filtering. This bilingual pipeline ensures high-quality segmentation as a foundation for subsequent analytical tasks.
## 3 Materials and Methods
### Annotation Procedure and Contextual Grounding in Dialogue
The process is divided into three main stages: before, during, and after annotation. Before annotation, all annotators were provided with comprehensive guidelines, including clear definitions and practical examples for each bias category (racism and sexism), ensuring a shared understanding of the phenomena. During annotation, annotators read each entire dialogue, assessed whether the target comment fitted the label definition, and annotated it within its conversational context, following a structured, step-by-step protocol. After annotation, we computed inter-annotator agreement using Cohen's Kappa, obtaining \(\kappa=0.55\) for sexism (moderate agreement) and \(\kappa=0.79\) for racism (substantial agreement), in line with accepted standards for subjectivity-prone linguistic annotation. Divergent cases were resolved via a manual adjudication protocol involving both annotatorsand a supervising linguist, thereby producing a fully curated and consensus-driven dataset suitable for training and evaluation. The inclusion of dialogical context and the use of interactive, task-specific annotation tooling distinguish this work from many prior Spanish-language toxicity corpora, which have primarily focused on single-turn Twitter posts or decontextualized comment fragments. In contrast, our annotation captures the dynamic unfolding of prejudice in conversation, enabling future models to leverage discourse-level signals that are critical for the robust detection of subtle, pragmatic, and evolving forms of online bias.