esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Simulation Results and Discussion
### Design and Optimization of Design-2  
Following that, the second configuration of dielectric resonators (DR) was constructed and termed design-2, as illustrated in Figure 2b. This section primarily focuses on the formation of circular polarization and the augmentation of bandwidth, as circular polarization is generated by activating two orthogonal modes within the DRA.s. To that end, design-2 is proposed, in which a novel conformal metal strip is responsible for the excitation of two virtually, orthogonal mode pairs. The antenna array is configured in the H-plane with a corner to corner distance (d) of 18 mm, equating towards o /2 at 3.72 GHz. [18].  
Because of the multiple sweeps, the components of the revolutionary feed strips have been optimized; the optimal dimensions of the individual strips are h2 = 12 mm, h3 = 2 mm, h4 = 10.5 mm, and w2=10 mm. All of the strips have a width of 1 mm. The aspect ratios and permittivity of the DRA also have an effect on S11 bandwidth. Furthermore, the resonance frequency can be impacted by a number of parameters, including the efficiency of field mutual coupling between the strips and the DRA, the capacity, and the strip's location. The design approach for the Novel-shaped conformal metal strip is summarized in Table 3. Incorporating all of the strips at the optimal location and characteristics results in produced an additional resonant frequency at -4.37 GHz, which, in parallel, widened the S11 bandwidth from 7.19% to 32.96%. The S11 band covered by Design-2 ranged from 3.59 GHz to 4.55 GHz; because of this, it may be used for sub-6 GHz 5G applications. Impedance-matching bandwidth of Design-2 is depicted in Figure 6.  
Figure 5: Mutual coupling of Design-1.
## 3 Results and Discussion
### FTIR Results  
In this study, FTIR was used to compare and analyze the functional groups of COBA, as shown in Figure 7.  
Figure 7: FTIR of castor oil-based bio-oil and COBA.  
As shown in Figure 7, no new absorption peaks appeared in asphalt after the addition of bio-oil, but only the intensity of the peaks was different. Therefore, bio-oil was mainly physically dispersed in asphalt. As the thermal oxidation occurred, the area of characteristic peak for COBA at 1705 cm\({}^{-1}\) and 1030 cm\({}^{-1}\) increased gradually. It was found that asphalt is susceptible to thermal oxidation, resulting in the breaking of C=C to form C=O. At the same time, the H atoms are oxidized to hydroxyl groups, and the sulfur-containing functional groups react with oxygen to form the polar functional group of S=O. These increase the number of oxygen-sensitive functional groups inside asphalt. For example, the bio-oil showed a clear absorption peak of C=O at 1705 cm\({}^{-1}\), and the larger the content of bio-oil, the more obvious the absorption peaks. Therefore, in order to quantify the variation of the oxygen-sensitive groups of COBA, \(I_{\text{C=O}}\) and \(I_{\text{S=O}}\) were calculated as shown in Figure 8.  
According to Figure 8, the \(I_{\text{C=O}}\) increased 20, 144, and 340 times compared to that of base asphalt when the bio-oil content was increased from 5% to 15% in the virgin sample. However, the \(I_{\text{C=O}}\) of base asphalt was only 1.7 \(\times\) 10\({}^{-4}\), which was extremely low, indicating that bio-oil brought a large amount of C=O to COBA. On the contrary, \(I_{\text{S=O}}\) decreased with increasing bio-oil, which is due to the very low content of S in bio-oil and the dilution of S content after blending with base asphalt.  
Under thermal oxidation, the growth of \(I_{\text{C=O}}\) in COBA was similar in both short-term aging and long-term aging, while \(I_{\text{S=O}}\) grew rapidly in long-term aging. When the content of bio-oil increased from 5% to 15%, \(I_{\text{C=O}}\) increased by 2.4, 0.32, and 0.19 times compared to the virgin sample, respectively, after short-term aging; and 5.69, 0.85, and 0.31 times, respectively, after long-term aging. \(I_{\text{S=O}}\) increased by 0.03\(\sim\)0.36 and 2.45\(\sim\)3 times, respectively, in short-term and long-term aging, indicating that the production of S=O in COBA occurs mainly in the long-term aging. By considering the change of various groups in COBA and base asphalt before and after thermal oxidation, it can be assumed that the generation of C=O in COBA is mainly contributed by bio-oil, while S=O mainly originates from the base asphalt.
## 5 Experimental Results and Discussion
### Discussion
#### 5.5.1 Error Analysis  
One general problem faced by the model was that it was unable to correctly classify sarcastic or implicit hate speech. Of the tweets containing hate, 15% were misclassified because the language used sarcasm or indirectness. In comparison to having obvious, easily recognizable nude words, implicit hate speech may contain sarcasm, puns or references  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Model** & **Accuracy (\%)** & **F1 Score (\%)** \\ \hline L-HSAB & CNN & 89.5 & 88.3 \\ L-HSAB & AraBERT & 91.8 & 90.7 \\ OSACT4 & CNN & 87.4 & 85.0 \\ OSACT4 & AraBERT & 90.2 & 88.5 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Confidence intervals for accuracy and F1 scores for CNN.  
to a specific culture, without necessarily having to contain Arabic words. For example, a seemingly positive comment, such as \"We understand your concern\", may be a taunt to a target group that is designed to hurt. The second major source of misclassification arose from dialectal differences within the Arabic language. Although Standard Arabic is the basis for the majority of standardized educational representations, the Gulf, Levantine, and Maghrebi varieties that have infiltrated social media do not differ significantly in vocabulary, grammar and usage. The model was not very effective about 12% of the time, mainly due to an inability to understand colloquial or regional expressions. This limitation suggests that there is an opportunity to develop dialect-specific datasets, or at least embeddings, to enhance the study of Arabic hate speech in its multicultural aspects.  
Progression performance was also affected by the class imbalance. The \"religious hate speech\" class had quite high precision and recall values, of 0.91 and 0.89 respectively, for classifying the posts accurately, while the \"political hate speech \"class produced lower values of precision (0.79) and recall (0.74) for classification of the posts. This can primarily be attributed to the limited inclusion of the political hate speech in the dataset, in addition to the generalization capability of the model.  
The majority of misclassifications occurred in samples of hate speech and sarcasm, when no straightforward marker of offensive content was present. It was found that these cases contributed nearly 15% of false negatives. Tweets containing landmarks also posed problems, while the dialect-specific expressions turned out to be perhaps the hardest, as embeddings derived mainly from Modern Standard Arabic did not code for regional differences. Including dialect-specific data or embeddings could help alleviate such errors.
# An Unambiguous Delay-And-Multiply Acquisition Scheme for GPS L1C Signals
## 1 Introduction
We prove that the correlation function of our proposed scheme has a triangular shape, which completely removes the ambiguity problem, and analyze the performance of the proposed DM method using the signal-to-noise ratio (SNR) of the decision variable and the probability of detection. We also add the procedure to find the Doppler frequency for tracking the L1C signal. The rest of the paper is organized as follows: Section 2 defines the GPS L1C signal and its DM signal, while Section 3 provides the result of this paper, such as new DM acquisition schemes for GPS L1C signal and the noise characteristics of the proposed DM method with performance analysis. Section 4 discusses the results of the proposed methods.
## 3 Intermittent Receiver-Driven Data Transmission
### Routing Protocol  
The routing protocol of IRDT is based on the distance vector routing protocol. All nodes have routing tables and a routing function for deciding on the transmission of an SREQ packet.  
A routing table contains hop counts from the node which has created the table to all nodes in the network. In order to create its own routing table, each node must exchange its table with its neighbors. In IRDT, all nodes periodically wake up and wait for ID packets for a short period of time, which, however, is longer than the intermittent interval. When a node receives an ID packet within this period, it registers on its routing table that the hop count to the sender of the ID is one. We refer to this interval as'sampling interval' (denoted by \(T_{si}\)).  
The routing algorithm for IRDT must be based on multi-hop routing, and therefore each node conducts the relay processing of the packet. Although minimum hop routing is preferable for the purpose of minimizing energy consumption, in some situations nodes cannot utilize the optimal routing due to poor radio wave conditions or failure of certain nodes. Therefore, for higher flexibility, the routing algorithm considers alternatives to the minimum hop route. Here, we define forward nodes, sideward nodes, and backward nodes. A node whose hop count from the sink node is \(H\) classifies its adjacent nodes as shown below.  
**Forward nodes:**: Adjacent nodes whose hop count from the sink node is \(H-1\).
**Sideward nodes:**: Adjacent nodes whose hop count from the sink node is \(H\).
**Backward nodes:**: Adjacent nodes whose hop count from the sink node is \(H+1\).  
Figure 4 shows an example of this classification of neighboring nodes.  
The routing function is a logic function that utilizes a routing table. Sender nodes decide whether to return an SREQ packet in accordance to this function, an example of which is shown in Figure 5. The function in Figure 5 assumes the minimum hop routing; however, detours are also used when the condition of sideward relay is satisfied.  
Here, we define communication failure as a situation in which the sender cannot obtain a RACK and a DACK from the receiver. For minimum hop routing with detours, the sender node prefers forward nodes as receivers, and sideward nodes are selected if communication with all forward nodes fails. In order to prevent routing loops, all data packets have a time to live (TTL) field. The TTL is decremented by one only when a receiver node has received a data packet from a sender node. When the TTL becomes zero and the receiver is not the destination of the data packet, the data packet is discarded. No sender node selects a sideward node or a backward node if this results in loss of data packets due to the TTL mechanism.
# Error Resilient Coding Techniques for Video Delivery over Vehicular Networks
## 4 Channel Coding Protection  
The second error resilience approach is based on channel coding techniques which provide data redundancy to correct the bit stream errors found during video delivery. These techniques are complementary to the source coding approaches of the previous section, enabling the recovery of lost packets and, as a consequence, improving the received video quality. In particular, we propose the use of RaptorQ codes, a well known Application Layer Forward Error Correction (AL-FEC) technique. In Section 4.1, we will concisely explain how RaptorQ codes work and will select the most suitable parameters for RaptorQ configuration. These parameters will be selected following two criteria: (1) minimizing the overhead, and (2) setting the maximum delay allowed by the application. Then, in Section 4.2, we will analyze the performance of this FEC technique regarding packet recovery using a simple probabilistic error model. The recovery abilities of the RaptorQ codes will be evaluated in combination with the encoding layouts presented in the previous section, in order to obtain the lowest TLR possible. The results obtained here will determine the benefits of using these techniques in combination with the ones proposed in the previous section.
# Spectrum Slicing for Multiple Access Channels with Heterogeneous Services
## 4 Orthogonal Multiple Access  
We first consider algorithms based on OMA, assuming that \(U_{I}>1\), and, for the sake of simplicity, that all intermittent users have the same slot arrival rate \(\alpha\). In an OMA system,  
Figure 3: Evolution of the AoI and PAoI.  
the broadband user transmits in frames of \(N\) broadband slots, each of which contains an encoded data packet. It is sufficient to decode \(K\) of the \(N\) packets to recover the whole frame. Reserved slots for the intermittent users are interleaved with the ones for the broadband user: there is one intermittent slot every \(T_{\text{int}}\), where in general \(T_{\text{int}}\
eq N\), and in which one or more intermittent users try to access the channel.
# Design of an Underwater Acoustic Waveform and Integrated System for Communication and Detection
## 3 Hydroacoustic Integrated System Architecture
### Communication Transceiver and Symbol Suffix  
The generation of the shared waveform is illustrated in Figure 8. The communication symbols are channel encoded, then combined into signal frames, and subsequently CPM modulated. The CPM modulated signal is then multiplied with an LFM signal to obtain a shared waveform signal. After DA conversion and power amplification, the signal is sent out.  
Figure 8: Signal generation at the transmitter.  
Figure 6: The hydroacoustic integrated system.  
Figure 7: The integrated hydroacoustic system based on constant envelope waveforms.  
At the receiver, communication and detection processing are conducted. For detection, matched filtering can be employed. The focus here is on recovering the data. The received signal is first filtered, amplified, converted from analog to digital, and down-converted to obtain the baseband signal. The baseband signal is then framed and multiplied by the conjugate of the LFM template signal to obtain the CPM signal. After matching filtering, frequency estimation and compensation are performed. Finally, Viterbi decoding and channel decoding are applied to obtain the received information sequence. The signal processing flowchart is shown in Figure 9. Here, we introduce a frame format with a symbol suffix for two-step frequency and phase estimation, enhancing the system's communication performance. The frame format is shown in Figure 10. The shaded part in the figure is elaborated in Figure 11.  
To address the Doppler effect and multipath effect, a frame structure employing symbol suffix (SS) has been designed for intra-frame compensation. The shared waveform signal of the designed symbol suffix frame structure can be simplified as SS-CPM-LFM. The frame structure is shown in Figure 10. The Head is a preamble sequence from a fixed  
Figure 10: The data frame structure.  
Figure 9: Signal processing flowchart at the receiver.  
codebook \(\{1,\,-1,\,1,\,-1\ldots\}\), where the corresponding signal of the preamble sequence is an LFM signal with a fixed phase offset. Leveraging the excellent autocorrelation properties of LFM allows for signal synchronization and initial frequency compensation. The preamble is used for frame detection via correlation to obtain the peak. To reduce the computational complexity, downsampling can be applied.  
To demonstrate the improvement of the SS frame structure on the communication and detection performance of the integrated system, the traditional frame structure without a sign suffix is considered. In this case, a preamble is inserted before the data.  
In the two frame structures, the preamble satisfies \(I_{n}=-I_{n+1}\). Then the preamble consists of LFM with a fixed phase offset, which has favorable autocorrelation characteristics. This is used for synchronization and frequency and phase estimation and compensation.
## 2 Methodology
### IoT Terminal Design
As a result, the signal's range is likewise shortened, and it is more vulnerable to noise and interference. A larger spreading factor, such as SF12, on the other hand, causes a lower data rate, enabling a greater range and better signal quality. The quantity of data that may be delivered in each length of time is nonetheless decreased as a result [38]. The final decision about the spreading factor is made considering the needs and limitations of the communication system, including the intended range, data rate and amount of interference. The potential for interference with other signals running at the same frequency, which might result in subpar performance, is a limitation when utilizing LoRa CSS. When a transmitter and receiver are moving relative to one another, a phenomenon known as doppler shift happens [39]. This may result in a change in the operating frequency  
Figure 5: System architecture for IoT terminal. for satellite communications, which can have an impact on the signal's quality. The terminal can use adaptive frequency hopping and frequency correction techniques to minimize the doppler shift and maintain a steady signal. To adjust for frequency changes brought on by the doppler effect or other variables, communication systems utilize frequency correction algorithms. These algorithms, which may be used in either the transmitter or the receiver, usually include constantly checking the signal strength and modifying the operating frequency to keep the connection steady. Utilizing input from the receiver to change the transmitter's frequency is a typical method of frequency correction. This may be achieved by comparing the received signal to a reference signal and changing the transmitter's frequency in accordance with the variation between the two. Implementing adaptive frequency hopping is another strategy where the transmitter continually modifies its operating frequency in response to various factors, such as the quantity of interference or the caliber of the received signal. This enables the transmitter to maintain a solid connection with the receiver while avoiding frequencies that could be blocked or degraded.
# Study on Dynamic Splitting Properties of S-PP Hybrid Fiber Concrete after High Temperatures
## 2 Test Materials and Methods
### Calculation of Dissipated Energy  
The process of tensile failure of concrete structures affected by high temperature (fire) after dynamic disturbance is also the conversion of energy. Analyzing the failure of concrete materials from the energy perspective can better reflect the damage evolution law of specimens under different working conditions. According to the characteristics of the dynamic splitting test, the dissipated energy is closely related to the tensile damage of materials, which can be used as the toughness evaluation index of concrete materials [23]. Based on the law of energy conservation [24], the dynamic splitting tensile toughness of the material is further analyzed from the perspective of energy dissipation. The energy carried by the incident wave, reflected wave, and transmitted wave in the whole impact process and the energy dissipated by the failure of the specimen can be obtained from Equations (4)-(7), and the dissipated energy density is \(w_{d}\), which is calculated from Equation (8).  
\[W_{i}(t)=C_{0}A_{b}E_{b}\int_{0}^{T}\varepsilon_{i}^{2}(t)dt \tag{4}\]  
Figure 6: Force on flattened Brazilian disc.  
\[W_{r}(t) =C_{0}A_{b}E_{b}{\int_{0}^{T}{\varepsilon_{r}^{2}(t)dt}} \tag{5}\] \[W_{t}(t) =C_{0}A_{b}E_{b}{\int_{0}^{T}{\varepsilon_{t}^{2}(t)dt}}\] (6) \[W_{s}(t) =W_{i}(t)-W_{r}(t)-W_{t}(t)\] (7) \[w_{d} =\frac{W_{s,\max}}{V_{c}} \tag{8}\]  
where \(W_{s}(t)\) represents the energy dissipated by the specimen, \(W_{i}(t)\) represents the incident wave energy, \(W_{r}(t)\) represents the reflected wave energy, \(W_{i}(t)\) represents the transmitted wave energy, \(C_{0}\) represents the wave velocity of bar, \(A_{b}\) and \(E_{b}\), respectively, represent the cross-sectional area and the elastic modulus of the Hopkinson pressure bar, \(T\) represents any time, \(W_{s,\max}\) represents the total dissipated energy, and \(V_{c}\) represents the volume of the concrete specimen.  
It can be seen from Equation (9) that the loading strain rate \(\dot{\epsilon}\) is related to the dynamic splitting strength \(f_{st,d}\) and dynamic elastic modulus \(E_{d}\), while the dynamic elastic modulus of the material is difficult to measure. To avoid the influence of the strain rate error on the analysis process, this paper uses the incident wave energy change rate \(\dot{e}_{i}\) to measure the change law of the dissipated energy of concrete materials, and the formula is shown in Equation (10).  
\[\dot{\epsilon}=\frac{f_{st,d}}{\tau E_{d}} \tag{9}\]  
\[\dot{e}_{i}=\frac{W_{i,\max}}{t_{i}} \tag{10}\]  
where \(\tau\) represents the time interval from zero to peak of transmission strain, \(W_{i,\max}\) represents the value after the incident energy is stabilized, and \(t_{i}\) represents the duration of the incident wave.
## 2 Pattern-Recognition-Based Ensemble Data Imputation Framework
### Module 1. Validation Data Generation  
As shown in Figure 1, the original timeseries data have good values (green square) and missing data (red square). Module 1 creates validation data (yellow square) using pattern recognition technique. Data with good values (e.g., x7 and x8 in Figure 1) and the generated validation data (x', and x',s) are the same in terms of values; their only difference is that the validation data are used to evaluate the imputation method. Pattern recognition is applied to identify some good data points that have similar characteristics with the missing data (red square) and marks them as validation data. The characteristics include time indicators (such as hour of day, day of week, weekday weekend indicator, and month of year) and weather variables (such as outdoor air temperature, humidity, and solar radiation). This means that the pattern recognition technique tries to search among good data and find the ones that have similar happening time and weather condition with the missing data, trying to mimic the missing data mechanism. Similarity metrics such as Euclidean distance are used to quantify how missing data and good data are similar with each other. To be more specific, the problem can be formulated and simplified using the equation representing that the method tries to search among all good data (\(X_{g}\)) to select several good data points (\(x_{g}\), a matrix with time and weather variable) with the highest similarity metrics (defined by \(f_{sim}\)), meaning they are the most similar to the missing data set (\(X_{m}\)). The selected good data points are supposed to be more likely to miss values. Those data sets that are similar to missing data but have a truth value can be further used as validation datasets to test the effectiveness of data imputation methods in the next steps. The pattern recognition techniques can be unsupervised clustering techniques (such as K-means clustering, categorical mixture models, hierarchical clustering, correlation clustering, and kernel principal component analysis), supervised  
Figure 1: Diagram of the developed data imputation framework with two modules.  
classification techniques (such as neural network, support vector machine, decision tree and random forest), and any other statistical methods that can classify and cluster data.  
\[\underset{x_{g}\in X_{g}}{argmax}f_{sim}\Big{(}x_{g},X_{m}\Big{)} \tag{1}\]