esa-sceva/satcom-chunk-collection huggingface dataset:
# Study on the Ablation Behavior of High-Intensity Lasers in Vacuum
## 4 Conclusions  
The researchers conducted repeated ablation experiments using high-intensity lasers in a vacuum environment for high-efficiency laser drilling. The variation in ablation depth and volume with power density, pulse number, and defocus was investigated, and the average single-pulse ablation depth and average volume ablation efficiency were calculated. The results indicate that, in a vacuum environment, the absence of convective heat dissipation significantly reduces heat loss at the bottom of the ablation hole. When continuous ablation is performed at a repetition rate of 10 Hz, the titanium alloy exhibits an enhanced ablation mechanism, similar to the effect observed in pulsed laser drilling. The high repetition rate generates a continuous liquid surface at the bottom of the ablation hole, which is ejected under the scouring action of the plasma. This mechanism increases the average single-pulse ablation depth of the nanosecond laser by more than four times. The measured average single-pulse ablation volume reached 0.97 \(\upmu\)m\({}^{3}\)/\(\upmu\)J. The combination of the vacuum environment and multi-pulse nanosecond laser significantly improves both ablation efficiency and hole quality.  
The study of the interaction between high-intensity lasers and matter holds significant application value in the fields of materials science, plasma physics, and laser processing technology. However, current research on the ablation behavior of high-intensity lasers in a vacuum environment remains limited. Delving deeper into this field not only aids in understanding the fundamental mechanisms of laser-matter interactions but also propels the advancement of related technologies. For instance, grasping the ablation characteristics of high-intensity lasers in vacuum conditions will significantly propel technological progress in areas such as spacecraft surface treatment, precision manufacturing, and the development of novel materials. Therefore, conducting a systematic study on this topic not only bears great theoretical significance but also exerts a profound influence on practical engineering applications.  
All authors contributed to the study conception and design. Conceptualization, Y.W. and J.Y.; methodology, J.Y. and H.G.; formal analysis, Y.W. and H.G.; investigation, Y.W., H.G., B.D., S.L., Q.C., S.W. and T.Z.; resources, Q.C. and D.W.; data curation, H.G.; writing--original draft preparation, H.G.; writing--review and editing, H.G., B.D. and Y.W.; supervision, S.L. and D.W.; project administration, S.L. and Q.C.; funding acquisition, Y.W. and J.Y. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the State Key Laboratory of Laser Propulsion & Application Independent Research Project (SKLLPA-202206).  
Not applicable.  
Data Availability Statement: Data is contained within the article.  
The authors declare no conflicts of interest.
###### Abstract  
This research presents a new method based on a combined temporal convolutional neural network and long-short term memory neural network for the real-time assessment of short-term voltage stability to keep the electric grid in a secure state. The assessment includes both the voltage instability (stable state or unstable state) and the fault-induced delayed voltage recovery phenomenon subjected to disturbance. The trained model uses the time series post-disturbance bus voltage trajectories as the input in order to predict the stability state of the power system in a computationally efficient manner. The proposed method also utilizes a transfer learning approach that acclimates to the pre-trained model using only a few labeled samples, which assesses voltage instability under unseen network topology change conditions. Finally, the performance evaluated on the IEEE 9 Bus and New England 39 Bus test systems shows that the proposed method gives superior accuracy with higher efficacy and thus is suitable for online application.  
fault-induced delayed voltage recovery; long short-term memory; observation time window; short-term voltage stability; temporal convolutional neural network; transfer learning Article
## 4 Optimization Problem Modeling
### System Cache Queue Modeling
#### 4.1.2 Relay Satellite Queues  
Define \(Q_{k,t}\) as the queue size for the relay satellite \(RS_{k}\) at time \(t\). The update equation for the queue at the satellite \(RS_{k}\) is given by the following:  
\[\begin{split} Q_{k,t+1}=\min\left\{& Q_{k}^{\max},Q_{k,t }+\sum_{n=1}^{N}\gamma_{n,k,t}^{n}D_{n}^{o}-\sum_{k^{\prime}\
eq k}^{K}\gamma_{ n,k^{\prime},t}^{r}D_{n}^{o}\\ &-\sum_{n=1}^{N}\gamma_{n,k,t}^{g}D_{n}^{o}+\sum_{m=1}^{M}\sum_{t ^{\prime}=1}^{t-1}\lambda_{m,t^{\prime},t}^{g}D_{m,t^{\prime},t}^{g}\\ &-\sum_{m=1}^{M}\sum_{t^{\prime}=1}^{t-1}y_{m,t^{\prime},k,t}\min \left(\frac{\varphi_{k}\tau}{D_{m,t^{\prime}}F_{m,t^{\prime}}},D_{m,t^{\prime },k,t}\right)\right\}\end{split} \tag{6}\]  
Here, \(Q_{k}^{\max}\) denotes the upper limit of the queue for \(RS_{k}\); \(y_{m,t^{\prime},k,t}\in\{0,1\}\) is a binary task indicator for the satellite's task execution. A value of 1 signals that \(RS_{k}\) processes the task \(\theta_{m,t^{\prime}}\) at time \(t\). The symbol \(\varphi_{k}\) stands for the computational capacity of \(RS_{k}\), and \(D_{m,t^{\prime},k,t}\) indicates the workload associated with task \(\theta_{m,t^{\prime}}\) for \(RS_{k}\) at \(t\). The workload update is defined as follows:  
\[D_{m,t^{\prime},k,t+1}=D_{m,t^{\prime},k,t}-\min\left(\frac{\varphi_{k}\tau}{D_ {m,t}F_{m,t}},D_{m,t^{\prime},k,t}\right) \tag{7}\]  
Initially, \(D_{m,t^{\prime},k,t}\) for satellite \(RS_{k}\) is determined by the following:  
\[D_{m,t^{\prime},k,t}=\begin{cases}0,&t<t^{\prime},\;and\;z_{m,t^{\prime},k,t^{ \prime}}=1\\ D_{m,t^{\prime}},&z_{m,t^{\prime},k,t^{\prime}}=1\;and\;\sum_{t=t^{\prime}}^{ t}y_{m,t^{\prime},k,t},\;t^{\prime}\leq t\leq\bar{t}.\end{cases} \tag{8}\]  
Equation (8) implies that the task is considered empty when there are no incoming data from the IoT devices, and it reflects the total task size once task execution begins.
# Performance of Different SLAM Algorithms for Indoor and Outdoor Mapping Applications
## 2 Materials and Methods
### Interactive SLAM  
The interactive SLAM algorithm uses odometry and point cloud data to create'vertex' at certain intervals and calculate the RMS value by creating an edge connection between the vertexes. The positions of the vertexes can be changed or corrected according to these edge connections. The point cloud matching algorithms such as ICP, GICP, NDT, NDT OMP are used to connect the edges of vertexes [17].  
Two loop closure methods are used in the interactive SLAM algorithm. The first method is the manual loop closure. In this method, vertexes are selected manually, and their positions are corrected using point cloud matching algorithms. Unlike the manual loop closure method, the automatic loop closure method automatically creates edges between vertexes at specified distances. Threshold values and parameters selected in automatic loop closure algorithms are critical. If the threshold value is selected as high, each vertex will match each other at the specified distance limit, and errors will accumulate, causing unwanted drifts. If the value selected is too low, the paired vertexes will be very few, and no correction will be made [17].  
With the plane detection method in the interactive SLAM algorithm, conditions such as perpendicularity and parallelism can be detected, and point clouds can be corrected. The map can be updated by determining the minimum elevation using the planes defined as the ground. Likewise, using the surfaces defined as ceilings, the maximum elevation can be determined, and the map can be updated by filtering the values greater than this elevation.
# Wireless Remote Control for Underwater Vehicles
## 5 Simulation Results
### Joystick and Way-Point Position Control  
Joystick (JOY_TX) and way-point (WP_TX) packets are sent from the control station to the HROV to control the HROV trajectory. For these applications, packet loss caused by a combination of channel error, deafness, and interference may result in some error on the trajectory performed by the HROV:  
Figure 9: Average throughput of all data traffics when varying the distance between HROV and control station (CTR): the average throughput was computed for each distance window observed in the step plot.  
while this is not the case for the JOY_TX packets sent in short range with a broadband optical link that has a negligible packet error rate (PER) when in range, this effect can be observed in the transmission of the WP_TX packets sent through the acoustic network, as their PER is 5.5%. In order to measure the trajectory error, we computed the RMSE of the path followed by the HROV with respect to the original path. Specifically, in Figure 10, we report the value of the RMSE for different positions of the HROV, observing that the RMSE linearly increases with distance as long as the distance between the HROV and the control station is less than 2.5 km, for which the RMSE is 3.5 m. Surprisingly, for longer distances, the trend changes drastically, as the RMSE no longer increases with distance. Specifically, in correspondence to the position of the first acoustic relay, we observe a peak of the RMSE that increases up to 14 m: from there onward, the RMSE oscillates between a value of 2 m and a value of 7.5 m when the HROV is 10 km from the control station. The reason for this behavior is the fact that, with the MAC layer being used for WP_TX contention, we cannot predict when a packet is lost due to deafness or collisions. Still, from our simulations, it results that, even in the worst case, the RMSE is always smaller than 20 m for all trajectories. Moreover, for distances up to 500 m where the HROV path is quite detailed (Figure 6b), the RMSE is always less than 1 m, while in the area controlled through joystick (up to a distance of 30 m from the control station), the RMSE is always less than 0.2 m. These results have been obtained by transmitting subsequent way-points, sending a new position as soon as the HROV should have reached the previous way-point in case of no packet loss without employing any additional guard time between the transmission of subsequent way-points. Nevertheless, the RMSE is always less than 0.6% of the distance between the HROV and the control station, that is in the order of magnitude of the existing tracking system accuracy. Still, if a smaller RMSE is required, a guard time between subsequent way-points can be employed to allow packets retransmissions [118], at the price of a longer mission duration.
# 3D Avatar Approach for Continuous Sign Movement Using Speech/Text
## 4 Results
### Results of Translation Process  
This section illustrates the translation process of the proposed model. The translation process includes English to ISL sentence conversion and ISL sentence-to-sign representation. The evaluation of the proposed system was performed by dividing the generated sentences into a 80:20 ratio between the training and testing sets, respectively, and the Word Error Rate (WER) of the input word was recorded. The result of the text processing system is presented in Table 7, where the WER metric is derived from Levenshtein distance (edit distance function). Here we compare the word from the reference sentence and the output sentence. The distance calculates the number of edits/changes (insertion/deletion and modifications) required to convert the input text to the correct reference text. In Table 7, Ins, Del, and Sub refer to the number of insertion, deletion, and modification/substitution operations for converting source text to the proper target text, respectively.  
For evaluating the performance of the translation system, some metrics have been considered: SER, BLEU, and NIST. SER computes the sign error rate during the generation of each sign from the ISL sentence. In this work, we recorded SERs of 10.50 on the test data. This error occurred due to WER happening during text entry input, which resulted into wrong sign generation by the avatar. BLEU and NIST are used for evaluating the quality of text during the translation from English (source language) to ISL (target language). The translation is done based on the multiple reference text (used from the vocabulary), and it calculates the precision score based on the unigram, bigram,..., \(n\)-gram model where \(n\) is the number of words in the reference text.  
BLEU assigns equal weights to all \(n\)-grams, whereas NIST gives more importance to the rare words and small weights to the frequently used words in the sentence, so the overall score of NIST is better than BLEU. The result of the rule-based translation process is presented in Table 8. From Table 8, it can be concluded that the NIST score outperforms the BLEU score.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Speech Type** & **Speech Signal** & **Output Text (Using IBM-Watson Service)** \\ \hline Discrete & & Hello \\ \hline Discrete & & Come \\ \hline Continuous & & Do you like it? \\ \hline Continuous & & Thank you \\ \hline \hline \end{tabular}
\end{table}
Table 6: Mapping of the speech signal to text for different types of speech.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**WER (\%)** & **Ins (\%)** & **Del (\%)** & **Sub (\%)** \\ \hline
25.2 & 3.3 & 7.1 & 14.8 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Text processing results based on Word Error Rate (WER).
## 3 Results
### Emerging Opportunities and Limitations/Barriers
 \\ \hline
**-** & \ \begin{tabular}{c} SASRs can improve well-being by addressing \\ key issues like agitation, loneliness, and stress, \\ and offering emotional support to older adults. \\ Their use could also reduce dependency on \\ medication and improve overall life quality for \\ older individuals. \\ \end{tabular}  & - & \begin{tabular}{c} Many studies suffer from high risks of bias, \\ including issues with randomization and \\ blinding. The need for better methodological \\ designs and larger sample sizes is evident to \\ draw firmer conclusions about the effectiveness \\ of SASRs. \\ \end{tabular}  \\ \hline
**-** & \ \begin{tabular}{c} SASRs can help mitigate stress and emotional \\ distress for children in hospital settings, \\ providing a calming presence and an engaging \\ distraction during treatment. This could \\ improve overall hospital experiences for \\ children and support their emotional needs \\ during stressful situations. \\ \end{tabular}  & - & \begin{tabular}{c} Although results are promising, there is still \\ limited data on long-term impacts. Further \\ research is necessary to evaluate how robots \\ can be effectively integrated into hospital \\ routines and ensure they align with healthcare \\ staff workflows. \\ \end{tabular}  \\ \hline
###### Abstract  
The upward trend of adopting Distributed Energy Resources (DER) reshapes the energy landscape and supports the transition towards a sustainable, carbon-free electricity system. The integration of Internet of Things (IoT) in Demand Response (DR) enables the transformation of energy flexibility, originated by electricity consumers/prosumers, into a valuable DER asset, thus placing them at the center of the electricity market. In this paper, it is shown how Local Energy Markets (LEM) act as a catalyst by providing a digital platform where the prosumers' energy needs and offerings can be efficiently settled locally while minimizing the grid interaction. This paper showcases that the IoT technology, which enables control and coordination of numerous devices, further unleashes the flexibility potential of the distribution grid, offered as an energy service both to the LEM participants as well as the external grid. This is achieved by orchestrating the IoT devices through a Consumer Digital Twin (CDT), which facilitates the optimal adjustment of this flexibility according to the consumers' thermal comfort level constraints and preferences. An integrated LEM-CDT platform is introduced, which comprises an optimal energy scheduler, accounts for the Renewable Energy System (RES) uncertainty, errors in load forecasting, Day-Ahead Market (DAM) feed in/out the tariff, and a fair price settling mechanism while considering user preferences. The results prove that IoT-enabled consumers' participation in the energy markets through LEM is flexible, cost-efficient, and adaptive to the consumers' comfort level while promoting both energy transition goals and social welfare. In particular, the paper showcases that the proposed algorithm increases the profits of LEM participants, lowers the corresponding operating costs, addresses efficiently the stochasticity of both energy demand and generation, and requires minimal computational resources.  
local energy markets; consumer digital twin; transactive energy; thermal comfort; DER  
2023
## 2 Fuzzy Fault Level Constrained Optimal Power Flow (FFLC-OPF)  
### Fuzzy Fault Level Constraints  
Fault current levels in the system should be within the breaking duties of circuit breakers (CBs). Several parameters in terms of fault current security are needed to be checked, but this paper focuses on initial symmetrical fault currents which can be used in the stage of operational planning. To obtain initial symmetrical fault currents for a 3-phase short circuit fault at a faulted bus \(i\), the following formulation is usually used:  
\[I_{f,i}=\left|V_{i}/Z_{ii}\right| \tag{1}\]  
where \(V_{i}\) is the pre-fault voltage at the faulted location. In Equation (1), \(Z_{ii}\) represents the diagonal components of the bus impedance matrix or the driving point impedance. When the fault current level exceeds the corresponding CB's rating, the faulted location cannot be isolated from the unaffected system and hence the fault can spread throughout the system and the affected region can be enlarged. Thus it is really important to maintain the fault current level within the CB's rating. This can be explained with a constraint, which is called a fault level constraint (FLC), as follows:  
\[I_{f,i}\leq I_{CB,i} \tag{2}\]  
where \(I_{CB,i}\) is for the CB's breaking duty. In other words, the CB's breaking duty is the FLC in that specific bus.  
Assuming that FLCs are included in an optimization formulation and available control measures to reduce fault currents at critical locations, some of the FLCs might be violated and this can cause infeasibility of the optimization problem. For that reason, this paper comes up with the application of fuzzy concept to FLCs. For the purposes of this study, a triangular fuzzy membership function is introduced to define the fuzzy membership set as seen in Figure 1. This technique is called fuzzy enforcement, and this application can adequately deal with variables that slightly exceed their limits. In this paper, the technique of fuzzy enforcement is applied to FLCs, so that even when the initial condition of the system violates FLCs, the optimizer can provide the optimal solution.  
The triangular membership function in Figure 1 can be expressed mathematically as follows:  
\[\mu_{i}\Big{(}I_{f,i}\Big{)}=\left\{\begin{array}{ccc}1&,&I_{f,i}\leq I_{CB,i} \\ \frac{I_{CB,i}+i_{i}-I_{f,i}}{\varepsilon_{i}}&,&I_{CB,i}\leq I_{f,i}\leq I_{CB,i }+\varepsilon_{i}\\ 0&,&I_{f,i}\geq I_{CB,i}+\varepsilon_{i}\end{array}\right. \tag{3}\]  
where \(\varepsilon_{i}\) stands for the acceptable limit of fault current violation for bus \(i\) during the solution process. The triangular fuzzy membership function represents a degree of certainty for each member.
## 2 MANTiS-32 Wireless Measurement System Design
### MANTIS-32 Wireless Networking Architecture
#### 2.3.1 Wireless Communication Architecture of the MANFIS-32 Sensing System with MANTiS-6050 Modules  
The MANTiS-32 wireless sensing system adopts a hybrid tree-bus topology to achieve enhanced scalability, flexibility, and communication reliability across the sensor network. By synchronizing precise timestamps with each MANTiS-6050 sensor, the system enables orderly, sequential data transmission from each channel. This ensures deterministic communication over the RS485 bus, eliminating data collisions and maintaining high temporal fidelity. As shown in Figure 4, the MANTiS-32 hub is architected to poll data from eight MANTiS-6050 sensor modules at fixed intervals of 0.52 ms. Each sensor's response is acknowledged before the hub proceeds to the next node, and the aggregated data are subsequently transmitted to the upper-tier PBM system via Wi-Fi. This tightly coordinated, multi-node communication scheme enables highly stable, high-resolution structural monitoring. With a complete sensor sweep across all eight channels achievable in under 4 ms, the system is particularly well-suited for high-frequency vibration measurement and real-time modal analysis.  
Each MANTiS-6050 sensor is connected to the MANTiS-32 hub via an RS485 bus, as depicted in Figure 4. Sensor 1 initiates data transmission immediately without delay. Each packet includes a metadata-enriched header comprising the Packet Number (P), Sensor Number (S), and Channel Number (C), followed by sensor measurements encoded in ASCII-format hexadecimal values. Upon completion of transmission, the appearance of a semicolon (\(j\)) on the RS485 line is interpreted as the end-of-packet delimiter. This triggers a fixed latency of 0.52 ms before the next sensor node initiates transmission. This time-slotted, collision-free communication strategy ensures orderly access to the shared RS485 bus, facilitating precise time alignment and efficient high-throughput data acquisition across the sensor array.  
Figure 3: Design and integration of the MANTiS-32 wireless communication module.
# A Tweak-Cube Color Image Encryption Scheme Jointly Manipulated by Chaos and Hyper-Chaos
## 5 Performance Analysis
### Histogram Analysis  
The histogram is an important statistical feature of the image. An outstanding cryptosystem has the capacity to evenly distribute the histogram of the cipher image. In this case, the attacker cannot extract statistical features of the plain image from its cipher. Figure 8 presents the histograms of the plain and cipher images in Figure 7, which demonstrates that encryption makes the histogram information safely obscured.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Modified Key**} & \multicolumn{3}{c}{**Difference Rate (\%)**} \\ \cline{2-4}  & **Couple** & **House** & **Tree** \\ \hline \(\mathrm{x}_{10}+10^{-14}\) & 99.6155 & 99.5880 & 99.5931 \\ \(\mathrm{c}_{1}+10^{-14}\) & 99.6094 & 99.6190 & 99.6089 \\ \(\mathrm{c}_{2}+10^{-14}\) & 99.6043 & 99.6002 & 99.6007 \\ \(\mathrm{x}_{0}+10^{-14}\) & 99.6190 & 99.6237 & 99.6038 \\ \(\mathrm{y}_{0}+10^{-14}\) & 99.6038 & 99.6165 & 99.6185 \\ \(\mathrm{z}_{0}+10^{-14}\) & 99.5956 & 99.6185 & 99.6048 \\ \(\mathrm{w}_{0}+10^{-14}\) & 99.6140 & 99.5946 & 99.6063 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Difference between cipher images with modified keys.  
Figure 7: (**a**) The cipher images with a small change to initial value \(\mathrm{x}_{10}\); (**b**) the pixel-by-pixel differences between (**a**) and Figure 6b.  
In order to further verify the encryption effect, we used the statistical magnitude \(\chi^{2}\) to measure the pixel values distribution of the plain and cipher images, which can be defined as follows:  
\[\chi^{2}=\sum_{\mathrm{i=0}}^{255}\frac{\left(\mathrm{f_{i}-g_{i}}\right)^{2}}{ \mathrm{g_{i}}} \tag{20}\]  
where \(\mathrm{f_{i}}\) denotes occurrence frequency of pixel value \(\mathrm{i}\), and \(\mathrm{g_{i}}\) represents the theoretical frequency of pixel value \(\mathrm{i}\). As displayed in Table 5, the chi-square test was performed on six images at a significant level of 0.05. Since the values are not only much smaller than the chi-square values of the plain images, but they are also lower than the critical value \(\chi^{2}_{0.05}(255)=293.24783\), they all pass the test. That is, our cryptosystem can effectively resist statistical attack.