esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
Bangla is a language with complex morphology, similar to Urdu. This paper presented a new, bigger Bangla dataset and an encoder-decoder-based sequence-to-sequence framework for lemmatization. After adjusting the hyper-parameters, the testing split of the dataset showed 95.75% character accuracy and 91.81% exact match accuracy. The research in [20] presented a hybrid lemmatizer and POS-tagger for Akkadian, an ancient language spoken from 2350 BCE to 100 CE. The authors used TurkuNLP to initially POS-tag and lemmatize the text and then improved the lemmatization quality by post-correcting with dictionary-based methods. The post-correction also assigned confidence scores to flag the most suspicious lemmatizations for manual validation. The results showed that the tool achieved a Lemma+POS labeling accuracy of 94% and a lemmatization accuracy of 95% in a held-out test set. For the Urdu language, there have been very limited efforts. In [21], the authors proposed a rule-based lemmatizer for the Urdu language. The lemmatizer works by adding or removing suffixes from the end of the words. The evaluation was carried out on only 1000 words. Further, the evaluation corpus for their approach is not publicly available. They reported an accuracy of 86.5% for their lemmatizer. Another paper, [22], presented a lemmatization algorithm based on recurrent neural network models for the Urdu language to complete normalization and morphological processes effectively. The proposed model was trained and tested on two datasets, the Urdu Monolingual Corpus (UMC) and the Universal Dependencies Corpus of Urdu (UDU), and outperformed existing models in accuracy, precision, and recall, achieving F-scores of 0.96, 0.95, 0.95, and 0.95, respectively. After a detailed analysis of related work, some significant challenges in the lemmatization process were identified. The rule-based approach is not appropriate for morphologically rich languages. Rules can also generate lemma of non-words and does not always provide an appropriate lemma. The results for most rule-based approaches are low as compared to dictionary-based approaches. The only available Urdu lemmatizers are rule-based and are limited to only 1000 words, which may not be accurate, given the morphological structure of the Urdu language. Information about part of speech tags is also missing in existing lemmatizers. A strong relationship exists between the PoS tag and the lemma of a word, so we need a PoS tag for the correct lemma. No large dictionaries have yet been built for the Urdu lemmatization system. To foster NLP research in resource-poor languages and to overcome the limitations of the existing Urdu lemmatizer, we propose a state-of-the-art and open-sourced Urdu lemmatizer.
## 5 Performance Evaluation
### Scenario I: System Reconfiguration  
In the first set of simulations, the workload variations induced by dynamic reconfiguration of the system, in particular, the addition and removal of control loops, are studied. The simulation pattern is as follows. At time t = 0, two control loops, say loops 1 and 2, are active. Loops 3 and 4 are activated at t = 6s and deactivated at t = 12s, simultaneously.  
The control performance of the four control loops is shown in Figure 4. Before loops 3 and 4 are activated, i.e., during time interval t = 0-6s, both control loops 1 and 2 achieve good performance with conventional time-triggered sampling, as can be seen from Figure 4(a). However, all control loops become unstable after the number of active control loops increases from 2 to 4 at time t = 6s, which causes the available bandwidth to be insufficient. In contrast, when the proposed flexible time-triggered sampling scheme is used, all control loops in the system remain stable and perform satisfactorily all the time, as shown in Figure 4(b).  
The sampling periods used in four smart sensors are depicted in Figure 5. It is apparent that the proposed flexible time-triggered sampling scheme dynamically adjusts the sampling period of each sensor at runtime, which is in contrast to the time-triggered sampling scheme that uses fixed periods.  
Figure 4: System output under system reconfiguration.  
The deadline miss ratios in control loops explain the difference in their control performance under different sampling schemes. As shown in Figure 6, all control loops suffer much severe deadline miss under time-triggered sampling than under flexible time-triggered sampling almost all the time. In particular, with time-triggered sampling, (almost) all control commands in the four control loops miss their deadlines during the time interval t = 6-12s, which yields system instability as shown in Figure 4(a). Under flexible time-triggered sampling, the deadline miss ratios in all control loops are well  
Figure 5: Sampling period under system reconfiguration.  
Figure 6: Deadline miss ratio under system reconfiguration.  
controlled and keep around the desired level most of the time. The variations in workload only incur some transient processes.
## 7 Low-Carbon Green Development of Asphalt Mixtures
### Recycled Material
#### 7.1.3 Biochar  
In recent years, there has been an increasing focus within the pavement industry on reducing carbon footprints and providing new strategies to address climate change. Biochar, as a renewable resource, has garnered significant research interest due to its potential in soil enhancement and carbon sequestration. Biochar is a carbon-rich material produced through the pyrolysis of biomass, primarily derived from agricultural residues (such as rice husks and corn stalks), forestry waste (such as branches and bark), and food processing by-products (such as fruit pits and vegetable scraps) [88; 89; 90]. Researchers have explored the use of biochar as an additive in asphalt mixtures to reduce overall carbon emissions. [PERSON] et al. [91] found that the energy consumption of biochar-modified bioashpalt (BMBA) is only 25% of that of conventional asphalt, with energy savings of 25% and 38% during the production and preparation stages, respectively. The greenhouse gas (GHG) emissions reported in this study range from 0.16 to 0.19 kg CO\({}_{2}\) equivalent per kg of BMBA. By partially substituting traditional mineral fillers with biochar, it is possible to effectively reduce the carbon intensity of materials while enhancing the performance and durability of asphalt. However, the production process of asphalt pavements inevitably releases volatile organic compounds (VOCs), posing health risks to construction workers [92]. To address this issue, some researchers have begun to investigate the application of biochar for the removal of VOCs from asphalt. Studies have shown that biochar can reduce VOC emissions by approximately 50%, with its adsorption efficacy closely related to the type of biochar used [93; 94]. For instance, biochar derived from straw or wood primarily interacts with VOCs through chemical adsorption, whereas biochar derived from pig manure relies more on physical adsorption [95]. By increasing the specific surface area, pore volume, and number of surface chemical functional groups of biochar while simultaneously reducing pore size, the adsorption capacity of biochar in asphalt can be significantly enhanced [96]. Despite the promising potential of biochar in VOC removal, research on the synergistic effects between biochar's efficiency in VOC elimination and the enhancement of asphalt performance remains relatively scarce. Furthermore, the annual production of 373 million tons of biochar from agricultural waste has the potential to sequester approximately 500 million tons of carbon dioxide each year, equivalent to 1.5% of the global annual CO\({}_{2}\) emissions [97; 98]. These findings suggest that biochar, as an alternative material, can provide effective solutions for the sustainable development of the pavement industry while simultaneously achieving the dual benefits of environmental and economic improvements.
# Semi-Supervised Time Series Anomaly Detection Based on Statistics and Deep Learning
## 3 Proposed Anomaly Detection Framework
### Predecessors of Tri-CAD  
As mentioned earlier, the proposed Tri-CAD framework is similar to the framework proposed in [45]. However, the two frameworks are different. Specifically, Tri-CAD first checks whether a time series is periodic. It then checks whether the time series is stationary or non-stationary. On the contrary, the framework proposed in [45] first checks if a time series is stationary. It then checks whether or not the time series is periodic. Checking that a time series is periodic can be done more definitely and clearly than checking that a time series is stationary. Hence, Tri-CAD can achieve more definite classification of time series than the framework proposed in [45].  
As also mentioned earlier, Tri-CAD integrates an anomaly detection mechanism, WAAD, proposed in [46]. WAAD is used for detecting anomalies in non-periodic and non-stationary time series. It first applies the DWT to the time series of a sliding time window to obtain wavelet transform coefficients. It then uses an AE to encode and decode (reconstruct) these coefficients. WAAD detects anomalies by monitoring the reconstruction error between the original and reconstructed coefficients for every time window. An anomaly is assumed to occur if the error meets specific conditions. In Tri-CAD, WAAD is improved by replacing the reconstruction error with the standard anomaly score. If the standard anomaly score is increasing and exceeds a pre-specified threshold, then an anomaly is assumed to occur. In practice, the improved WAAD has better performance than the original WAAD. It also has the merits that the standard anomaly score threshold value can be set intuitively.
# Fault Diagnosis System of Power Grid Based on Multi-Data Sources
## 5 Example Analysis
### Description of Example and Running Process of Fault Diagnosis System
According to Algorithm 4, unprocessed protection set (_P_3_ = {RBP_L2_B1}) and the set of suspected faulty elements (_V_e5_ = {L2, B3, L3}) are obtained. After obtaining \(P\)1_ and _CB_2_, corresponding substations start their quasi-real-time systems. Data mining is carried out for warning-level information related to maloperation protection and refuse-operation circuit breakers. Then fault reasons are tracked. The tracking process of refuse-operation circuit breaker CB15 is taken as an example. The relevant data of CB15 is shown in Table 10.  
According to the process of tracking fault reasons in Section 4.1.2, the suspected degree of each fault reason for CB15 refuse-operation is obtained, as shown in Table 11.  
Therefore, CB15 is overhauled according to the sequence of _r4_, _r3_, _r2_ and _r1_. The incorrect action reasons for CB58 and NBP_L8_B7 can be tracked in the same way. After getting the diagnosis result of the real-time processing system, the dispatching center starts a batch processing system. The fault recording documents of related circuit breakers of elements in _V_e5_ (CB4, CB5, CB6 and CB7) are called through the fault recording information net. Their waveform diagrams are shown in Figure 11. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Project** & **Data** & **Normal Value** \\ \hline Current RMS of tripping/closing coil & 1.2 A & 2.2–2.4 A \\ Current time of tripping/closing coil & 37 ms & 56–60 ms \\ Voltage at two ends of tripping/closing coil & 220 V & 210–230 V \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Relevant data of CB15. Figure 11: Fault recording waveform diagrams of CB4, CB5, CB6 and CB7. According to the mutation monitoring algorithm [10], the minimum fault time is 0.3002 s. The positive-sequence fault component of each current and voltage and the zero-sequence fault component of current is calculated by the symmetrical component method [10]. According to the fault direction discrimination method [10], the \(I1\) fault direction is negative, the \(I2\) fault direction is positive, the \(I3\) fault direction is positive and the \(I4\) fault direction is negative, so L3 is the faulty element. The zero-sequence fault components of \(I1\) and \(I2\) are large, and the difference between the current phasor of phase A of \(I1\) and the current phasor of phase BC is large, and the difference between the current phasor of two phases BC of \(I1\) is almost zero. Thus A-phase grounding short circuit fault occurs in L3. According to the single-end fault location method [13], the fault location of L3 is 50.2 km away from CB6 and 149.8 km away from CB7. Fault details of L6 and B15 can be obtained in the same way. Due to space limitations, this article does not repeat.
## 3 Results  
### Validation  
The validation of the developed model was carried out by replicating the cycle presented as reference cycle in this study by using the same operating conditions provided in the reference paper [22]. These parameters are summarized in Table 5. The coefficient of performance (COP) and the total refrigerant mass flow rate (flow rate through the condenser) were calculated and compared with the results reported in the reference study. The results obtained were compared in Figure 5. COP values showed an average deviation of approximately 0.17%, while the total refrigerant mass flow rates through the condenser differed by about 0.46% between the reference and model results. It was observed that the results were highly similar, and the model is suitable for use in the present analysis.
# Hybrid ARQ Scheme with Autonomous Retransmission for Multicasting in Wireless Sensor Networks
## 3 Proposed HARQ Algorithm  
The MCS of multicast packets is determined by the user with the worst channel quality [16; 17]. When HARQ is used, the MCS level is usually determined such that the worst user can achieve the target packet error rate (PER) within the maximum number of HARQ transmissions [18]. Under this scheme, the receiver achieves improved diversity gain, but the ACK feedback overhead increases as the number of retransmissions increase, as mentioned in Section 2.  
When the target number of HARQ transmissions is large--the target number of HARQ transmissions is determined by taking into account the packet transmission delay, the average throughput, and the buffer size at both transmitter and receiver--the resulting probability that a HARQ packet will be successfully decoded in the first or second transmission is row. Based on this observation, we propose a new HARQ scheme that automatically retransmits a data packet a predetermined number of times, \(N\), without receiving ACK information. This process is referred to as 'autonomous retransmission' for the rest of this paper. Starting from the \((N+1)\)-th transmission, the proposed algorithm operates in the same manner as the conventional HARQ scheme, where packet retransmission is determined based on ACK information received through the feedback channel. Figure 2 illustrates the proposed HARQ method when three HARQ processes are used to transmit to two multicast users, where \(N=2\). The users do not send ACK/NACK messages for the first and second packet transmissions, and start to send ACK information from the third transmission onwards. Consequently, ACK feedback overhead is reduced. Since a multicast transmission requires multiple ACK channels in the uplink for multicast users, the feedback overhead increases with an increase in the number of users. Using the proposed HARQ scheme, we can reduce the feedback overhead for multicast transmission, thereby increasing the uplink throughput. In Figure 2, users do not send ACK  
Figure 1: Conventional HARQ scheme. Subpkt \(x\)-\(y\) (\(m=n\)) means the \(n\)-th transmission of the \(y\)-th HARQ packet by the \(x\)-th HARQ process.  
messages from TTI = 1 to TTI = 6. Consequently, other users can exploit these unused resources to transmit control signals and uplink data packets.  
Let us define \(M_{\max}\) as the maximum number of HARQ transmissions including initial transmission and retransmissions. The proposed scheme is identical to the conventional HARQ method when \(N\) = 0, but the packet is transmitted without HARQ when \(N=M_{\max}-1\). Since the parameter \(N\) provides the tradeoff between the HARQ gain in the downlink and the feedback overhead in the uplink, it is important to determine the parameter \(N\) in order to optimize the overall efficiency of a communication link.
# Vibration Fatigue Life Reliability Cable Trough Assessment by Using Weibull Distribution
## 5 Conclusions  
1. The method proposed in this paper allowed us to estimate and predict the fatigue life of cable trough structures based on the stresses fatigue-vibration-Weibull analysis.
2. The proposed method lets us determine the Weibull parameters directly from the principal vibration stress values obtained after the product's application, mass, and resonance frequencies were considered in the analysis.
3. The proposed method by using the Weibull distribution lets us determine the product reliability indices as shown in Tables 4 and 5.
4. The efficiency of the proposed method depends on the accuracy on which the acceleration response and the dynamic factor are determined. This fact is because they determine the principal vibration stress values on which the Weibull parameters are determined.
5. We highlight that although it seems to be possible to include in the proposed method, the component's material deformation, because it is based on material properties such as elasticity and ductility, then due to, those material properties affect in the vibration analysis, the acceleration response and dynamic factor, then to generalize the proposed method to the deformation more research must be undertaken.  
Conceptualization, [PERSON]-C., M.R.P.-M. and R.C.T.-V.; methodology, [PERSON]-C. and M.R.P.-M.; data analysis, [PERSON]-C. and R.C.T.-V.; writing--original draft preparation, [PERSON]-C. and M.R.P.-M.; writing--review and editing, [PERSON]-C., M.R.P.-M. and R.C.T.-V.; supervision, [PERSON]-M.; funding acquisition, [PERSON]-C. and R.C.T.-V. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# Deep Learning-Based Human Action Recognition with Key-Frames Sampling Using Ranking Methods
## 4 Experimental Results
### Datasets
#### 4.1.2 MSR-Action3D Dataset  
The MSR-Action3D dataset had been devised by [PERSON] and Communication and Collaboration Systems Group at Microsoft Research Red-Mond. A total of 10 subjects repeatedly performed 20 different actions to generate a total of 567 sequences. The action classes are defined as follows: '_Band_', '_DrawCircle_', '_DrawCross_', '_DrawTick_', '_ForwardKick_', '_ForwardPunch_', '_GolfSwing_', '_Hammer_', '_HandCatch_', '_HandClap_', '_HighArmWave_', '_HighThrow_' '_HoizontalArmWave_', '_Jogging_', '_PickUplandThrow_', '_SideBoxing_', '_SideKick_', '_TennisSerue_', '_TennisSewing_', '_TwoHandWave_'. Figure 6(b) shows an example of '_TwoHandWave_' action frames in the MSR-Action3D dataset.  
Figure 6: Example of actions in three datasets; (**a**) _OneHandWave_ in the DHA dataset, (**b**) _TwoHandWave_ in the MSR-Action3D dataset, and (**c**) _BaseballSwing_ in the UTD-MHAD dataset.
## 2 Instrument Developments
### PAU--One Receiver and griPAU  
In order to develop the science behind the GNSS-R observables and their relationship with the brightness temperatures, two simplified instruments have been developed, which consist of just one LHCP down-looking antenna, a seven LHCP path hexagonal array, and a RHCP up-looking antenna.  
Two of these instruments have been built, one for ground based operations (PAU-One Receiver-rev 1-[18] and griPAU-rev 2-[19]), and another one for airborne operations (PAU-One Receiver Airborne, see Section 2.4).  
Figure 9 shows the griPAU instrument deployed during the Advanced L-BAnd emissiviTy and Reflectivity Observations of the Sea Surface (ALBATROSS) 2009 field experiment in the Canary Islands. In this particular implementation of the PAU concept, two 7-patch hexagonal arrays are used: one for a dual polarization radiometer (vertical and horizontal polarizations) at 1.400-1.427 MHz  
Figure 10: _griPAU_ instrument block diagram showing: commercial GPS receiver to provide Doppler estimates, up-looking receiving element to provide delay estimates (every 5 ms), and down-looking receiving element (with 7 LHCP hexagonal patch array) to collect the reflected signal. The whole system is embedded in a Xilinx Virtex-4 FPGA and has serial USB connectivity.  
Figure 9: _griPAU_ instrument mounted on a 6 m tower with automatic antenna positioner, during the ALBATROSS 2009 field experiment in Gran Canaria (Canary Islands, Spain).  
(instead of f\({}_{0}\) = 1575 MHz, B = 2.2 MHz), and the second one for PAU/GNSS-R. The smaller up-looking patch antenna in the center is used to track the delay of the direct signal, and fed it to the reflectometer. Figure 10 shows the griPAU (PAU/GNSS-R) block diagram. This instrument includes an automatic tracking of the specular reflection point of the pre-selected GPS satellite to simplify instrument's operation, while at the same time ensures observations collocated in time and space, exactly in the 1.400-1.427 MHz band used for passive observations.
# Dolphin Sounds-Inspired Covert Underwater Acoustic Communication and Micro-Modem
## 3 Modern Design  
Our modem is designed specifically for divers to communicate with their partners or UUVs securely as shown in Figure 1. On one hand, the communication algorithm needs to feature low probability of detection and interception. On the other hand, the modem hardware should be portable and consume low power. In this section, we describe the modem hardware design. The hardware of the modem is composed of two parts including electronic and transducer elements connected by a sealed tube. The transducer is used to transmit and receive communication signals. The electronic element has a signal processing board and power amplifier covered by a waterproof housing. Since the maximum working depth for the diver is less than 100 m, we use PVC-based housing, which is cheap and lightweight.