esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Key Steps in a FRB Search Pipeline
### Rejection of Radio Frequency Interference
#### 2.1.3 Real-Time Thresholding  
Typical time-domain surveys contain a sequence of time-frequency data recorded with integration times of a few tens to hundreds of microseconds. At these timescales, the RFI environment at a radio telescope changes rapidly, which makes a static channel mask ineffective for real-time searches for FRBs. FRB surveys require such real-time processing as the high data rates preclude storing the full-resolution observations for a meaningful length of time. This means RFI mitigation needs to occur on-the-fly as well. This is a challenge because (1) the tension between hardware memory limitations and the varying but ever-incoming data stream mean the algorithm has only limited access to the data statistics that change over time and (2) the compute bounds and real-time requirement demand a simple algorithm. In practice, for a multi-beam survey in which \(10^{2}\,\mathrm{G}\mathrm{b}\mathrm{p}\mathrm{s}\) of incoming data need to be cleaned of RFI in real-time, as in Figure 1, these requirements translate to computer demands that require \(\sim\)1-5% of the overall execution time [7; 9], operating on the local statistics of buffered data chunks spanning \(\sim\)1 s. The FRB surveys at, e.g., CHIME [10] and Apertif [9] each employed algorithms that identify and remove samples stronger than a defined noise threshold and that adapt dynamically to the noise characteristics of the incoming data. These can act in both time and frequency domain and can be applied iteratively. The RFIm real-time mitigation library [9] that implements this algorithm is open source.
# Jupyter Notebooks in Undergraduate Mobile Robotics Courses: Educational Tool and Case Study
## 3 Purpose of the Research  
Mobile robotics courses encompass a wide range of complex and abstract concepts from different disciplines, which may hamper the learning process. This paper relies on experiential learning to orchestrate such process in undergraduate courses. Specifically, it studies how the pose of contextualized problems to be solved by the completion of worked examples may impact students' performance. For that, it has been devised a novel educational tool consisting of a collection of Jupyter notebooks. Said notebooks aim to empower the students' climbing of the steps in the Bloom's taxonomy towards the achievement of the pursued learning goals. In mobile robotics courses, such goals are the understanding, design, and development/implementation of basic robotics skills such as robot motion, sensing, map building, localization, path planning, etc. Concretely, the learning programme of the courses in the spotlight (see Section 4.1) is targeted at the acquisition of the following knowledge/abilities by students:  
* To know and explain which are the fundamental problems so a robot can be considered autonomous.
* To know the components of a robot (sensors, actuators, software, mechanical components, etc.) and how they operate in isolation and as components of a system.
* To understand how the most used sensors for gathering information from the environment work, as well as their associated probabilistic models.
* To comprehend the localization and map building process for a mobile robot.
* To understand and develop motion planning algorithms.
* To program a mobile robot so it navigates a 2D environment.  
Thereby, the specific research questions addressed in this work are:  
**RQ1.**: Do Jupyter notebooks enhance learning in mobile robotics undergraduate courses?
**RQ2.**: Do they represent an improvement over the traditional problem posing approach?* Are they a suitable framework for designing hands-on sessions from the lecturers point of view?
# A New String Edit Distance and Applications
## 4 Conclusions  
A new string dissimilarity measure is proposed with a flexible and publicly available algorithm for implementing it. It has shown promise in ongoing forensic applications thus far, but generalized Levenshtein with multi-character edits could have uses with in vivo sequences as well. Applications in graph edit distance and consequent potential uses in handwriting recognition, fingerprint recognition, and cheminformatics were also discussed. Allowing the interaction of edit types--such as a motif stuttering forward, then undergoing indels or substitutions within, before, or after that motif-is a novel addition that expands the useful applications of this work, and is accomplished via the dictionary structure in a way that is fast, feasible, and open to further speed optimization. It allows for a more accurate picture of the stutter phenomenon and how similar two STR sequences truly are when any stutter is expected, whether from PCR or in vivo polymerase slippage.  
In practice, this work and the associated code available can benefit those analyzing STR sequencing data, manufacturers and developers providing software tools to accurately recognize and label sequences from these data types, thus, indirectly contributing to reliable reporting of STR sequence data, both in the forensic genetics and the medical diagnosis fields where accuracy in interpretation of STR sequence data is crucial. To count the number of edit operations to get from one sequence to another, set all edit costs to one (this will be symmetric, since every operation will have an inverse of equal cost). This is a useful first pass on sequence similarity. Optimizing the costs to reflect the frequency of the edits in the data requires a kit-specific understanding of artifact frequencies, since those would be used to determine the relative costs of each operation. Applications such as sequence alignment may use costs motivated by traits other than pure artifact frequency, depending on the information desired.  
Further work could be done to penalize the overall number of edit operations in addition to the cost of each individual operation. It would also be a simple addition to consider transpositions as a separate possible edit operation, though for the original application it was unnecessary.  
The restrictions in place because of the stutter dictionary have proven helpful for several reasons, but do limit effectiveness in rare cases. A motif inserting in the middle of another motif, for example, would not be detected by the RFL algorithm, and neither would a motif spread apart by too many single-character intra-motif insertions (len(motif)\(-1\) insertions, in particular). Although this limit is easily changeable in the code, the way the algorithm is written requires a hard cap. For forensic applications, however, when the strings are only a few edits apart and insertions are rare, the restrictions do not limit effectiveness.
## Materials and Methods
### Electrochemical Analysis  
Electrochemical measurements were performed by utilizing an electrochemical workstation (Chen Hua CH604B, Shanghai) with a three-electrode setup: the specimen as working electrode (1 cm\({}^{2}\) area exposed), a platinum sheet as counter-electrode, and a saturated calomel electrode (SCE) as reference electrode. Fifty milliliters of deoxygenated m-SBF incubated in a water bath at 37 \(\pm\) 0.2 \({}^{\circ}\)C served as electrolyte. Potentio-dynamic polarization curves were scanned from \(-\)2 V to \(-\)1 V at a scan rate of 2 mV/s. The natural corrosion current (I\({}_{\text{corr}}\)) and natural corrosion potential (E\({}_{\text{corr}}\)) were determined by Tafel extrapolation.
# Filter Pruning via Attention Consistency on Feature Maps
## 3 Methods
### Mathematical Model of Filter Pruning Framework  
In this paper, the pruning rate of the \(i\)-th layer is denoted as \(P_{i}\) and prune each layer independently. For the \(i\)-th layer, the purpose of filter pruning is to obtain and remove the \(N_{i+1}\times P_{i}\) less important filters from \(\mathbf{W}_{i}\). This can be formulated as the following optimization problem:  
\[\begin{split}&\min_{m_{i}^{j}}\sum_{i=1}^{K}\sum_{j=1}^{N_{i+1}}m_{ i}^{j}\mathcal{L}\Big{(}\mathbf{W}_{i}^{j}\Big{)}\\ &\text{s.t.}\sum_{j=1}^{N_{i+1}}m_{i}^{j}=N_{i+1}\times P_{i} \end{split} \tag{1}\]  
where \(\mathcal{L}\Big{(}\mathbf{W}_{i}^{j}\Big{)}\) is a function to evaluate the importance of a filter \(\mathbf{W}_{i}^{j}\). In this framework, most the existing weight-based techniques directly design \(\mathcal{L}\) on weights. As reported in [PERSON] [31], weight-based pruning methods ignore the distribution of input images. Therefore, such methods might not accurately evaluate the filter importance for every dataset. Feature maps are the product of input images in network transmission, hence their importance of them can be regarded as a filter property as they are generated by matrix operations on weights. Considering the distribution of datasets, to achieve filter pruning, instead of weights, feature map-based pruning defines \(\hat{\mathcal{L}}\) on the feature maps. Therefore, for evaluating the importance of feature maps, Equation (1) can be reformulated as:  
\[\begin{split}&\min_{m_{i}^{j}}\sum_{i=1}^{K}\sum_{j=1}^{N_{i+1}}m_{ i}^{j}\mathbb{E}_{I\sim P(I)}\Big{[}\hat{\mathcal{L}}\Big{(}\mathbf{F}_{i}^{j}(I) \Big{)}\Big{]}\\ &\text{s.t.}\sum_{j=1}^{N_{i+1}}m_{i}^{j}=N_{i+1}\times P_{i} \end{split} \tag{2}\]  
where \(I\) represents an input image and \(P(I)\) represents the distribution of input images. Furthermore, \(\mathbf{F}_{i}^{j}(I)\) is a feature map generated by \(\mathbf{W}_{i}^{j}\). \(\hat{\mathcal{L}}\Big{(}\mathbf{F}_{i}^{j}(I)\Big{)}\) evaluates the importance of a feature map \(\mathbf{F}_{i}^{j}(I)\). The difference between the feature map-based pruning algorithms mainly lies in the design of \(\hat{\mathcal{L}}\). In this paper, an attention consistency discriminant module (ACDM) is designed as the evaluation function \(\hat{\mathcal{L}}\). ACDM evaluates the attention consistency of the feature maps. It shows that the feature maps with higher attention consistency are more important.
# Bridge Crack Detection Based on SSENets
## 3 Experimental Results and Ablation Study
### Hyperparameters  
SSENets uses Stochastic Gradient Descent (SGD) algorithm to train the training set containing 4856 images and labels mentioned in Section 2. We use the learning rate reduction strategy proposed by [PERSON] and [PERSON] et al. [37] for training, in which initial learning rate is 0.001, momentum is 0.9, weight decay is 0.3 and each batch contains 32 samples.
# Enhanced Bug Priority Prediction via Priority-Sensitive Long Short-Term Memory-Attention Mechanism
## 3 Our Approach
### LSTM-Attention Algorithm
The integration of LSTM and Attention effectively addresses key challenges in Seq2 Seq models by combining LSTM's ability to capture both long-term and short-term dependencies in sequential data with Attention's dynamic focus on relevant input elements, thereby enhancing the predictive accuracy and robustness of bug priority predictions in complex or large datasets.
# On Applicability of Network Coding Technique for 6 LoWPAN-based Sensor Networks
## 5 Conclusions and Future Work  
The paper discusses the efficiency of the inter-session network coding technique in the IEEE802.15.4/6 LoWPAN-based wireless sensor networks. The NC6 LoWPAN mechanism was examined both in simple and more complex scenarios. The results confirm that the application of the network coding technique reduces both the overall traffic volume and the packet loss ratio, which leads to the sensor's energy saving, but it may increase the end-to-end packet delay. The following recommendations should be considered for effective application of NC6 LoWPAN in WSNs. Firstly, packets have to be buffered in the NC layer to provide a network coding opportunity, even in the softly loaded network. Secondly, an appropriate value of the buffering time has to be selected, depending on network type, traffic characteristics and user's mobility. Thirdly, only unicast-based user data packets can be coded.  
The future work will be focused on the adaptive technique, allowing an increase in the coding efficiency in cognitive mobile sensor networks. The new solution should actively react on links quality, network structure changes due to nodes mobility, and the dynamic nature of wireless communications. It should also be independent of the broadcasted transmission and data link layer signaling protocols. Finally, it should incorporate some level of intelligence, allowing for us to adjust the network coding processes to a traffic type, routing information, and other possible cross layer information.  
[PERSON] prepared the concept of the paper; [PERSON] developed the algorithms; [PERSON] conceived, designed and performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON] wrote the paper. [PERSON] revised the paper.  
Work done as a part of the CYBERSECIDENT/369195/I/NCBR/2017 project supported by the National Centre of Research and Development in the frame of CyberSecIdent Programme.  
The authors declare no conflict of interest.
## 4 Results and Discussion
### Simulation Results
It is not necessary to implement 512 QAM as well in this scenario. Figure 4: (a) AIR performance at 0.86 NGMI threshold. (b) SE performance at 0.86 NGMI threshold. Figure 3: (a) AIR performance at 0.92 NGMI threshold; (b) SE performance at 0.92 NGMI threshold. For scenario 1-3, we can achieve the optimal SE at a specific SNR from Figures 3-5, respectively. Table 2 summarizes the QAM order exchanging point of scenario 1 to 3. Next, we simulate the performance of the CAP-based VLC system. First, the SNR performance in the CAP-based VLC system is measured. As the training sequence measures the channel SNR, it is crucial to know the relationship between the training sequence measured SNR and the PS signal SNR. We choose uniform 64 QAM as the training sequence and measure the received SNR of the PS signal. Figure 6 shows the relationship between the PS signal SNR (SNR_PS) and the training sequence SNR (SNR_TS). The SE and QAM order of PS are chosen according to Figure 2b. We can see that the SNR of the PS signal matches well with that of the training sequence. The maximum gap between SNR_PS and SNR_TS is only 0.04 dB. Therefore, it is reasonable to set the SE according to the training sequence SNR. The NGMI and AIR performance are shown in Figure 7a,b, respectively. The simulation results illustrate that the NGMI of scenario 1 and 2 fluctuates in a small range around 0.92 and 0.86, respectively, which indicates that the relationship between SE and SNR in Figures 3 and 4 is accurate. The fluctuation range of 0.92 is from 0.916 to 0.921, while that of 0.86 is from 0.855 to 0.861. For scenario 3, the NGMI is shown by green circles. The AIR is shown in Figure 7b, and the optimal AIR performance is achieved \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Exchanging Points**} & \multicolumn{2}{c}{**NGMI\_th = 0.92**} & \multicolumn{2}{c}{**NGMI\_th = 0.86**} & \multicolumn{2}{c}{**HAIR**} \\ \cline{2-7}  & **SNR** & **Shannon** & **SNR** & **Shannon** & **SNR** & **Shannon** \\  & **(dB)** & **Capacity** & **(dB)** & **Capacity** & **(dB)** & **Capacity** \\ \hline \multirow{2}{*}{(1)} & 16.67 & 5.57 & 15.65 & 5.24 & 15.78 & 5.28 \\  & 18.79 & 6.26 & 17.63 & 5.88 & 17.51 & 5.84 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: QAM order exchanging point and corresponding SNR. Figure 5: (a) HAIR performance. (b) Corresponding SE of HAIR. Figure 6: The PS signal SNR (SNR_PS) and training sequence SNR (SNR_TS). by scenario 3. As the code rate of each point is optimized, this result is reasonable. However, the cost is to change the FEC code rate at each operation point, which is almost impossible in a practical system. Therefore, for a functional system, the NGMI threshold and FEC code rate should be fixed. As the NGMI below the threshold cannot permit error-free decoding for the system, we choose to subtract 0.1 bit from the preset SE in the experiment to ensure error-free decoding.
# Caching-Aware Intelligent Handover Strategy for LEO Satellite Networks
## 2 System Model
### Remaining Idle Channels  
Each satellite has a fixed number of channels. Channels that are occupied will not be allocated to other users, while unoccupied channels are idle and waiting to be allocated. The number of remaining idle channels reflects the satellite load. Unbalanced load distribution will lead to a large number of idle channels in the light-load satellite coverage area, and the dropped calls and call blocking rate will increase in the overload areas. This situation will greatly reduce the overall performance of the system. In this paper, the number of idle channels is obtained in real-time through information interaction between satellites and utilized as a factor to make the handover decisions. For example, the handover strategy can dynamically reset the links of connected users to the adjacent satellites with idle channels to reduce the blocking rate of new calls.
## 4 Evaluation Scenario  
### Traffic, Network and Device Parameters  
In this paper, the developed motion capture system [7] is used to provide traffic parameters for performance evaluation of the eLPRT protocol and comparison with the unslotted CSMA/CA and GTS schemes of the IEEE 802.15.4.  
Typical motion capture applications, such as character animation, require a frame rate of 30 fps, which means that the sensors have to be sampled at 30 Hz. If the interval between data packets is set to 100 ms, each data packet will carry three samples from each sensor. A smaller interval could be chosen, but it would tend to decrease the number of supported nodes, because the payload length of the data packet would decrease, increasing the protocol overhead.  
Each sensor node contains six sensors (three accelerometers and three magnetometers) which are sampled with a resolution of 12 bits. To save space, each two 12-bit samples are compressed into 3 bytes. Each data packet also carries a sample of the battery voltage in 2 bytes. Therefore, the payload length required to carry all these samples is 29 bytes.  
The MAC header and trailer fields occupy a total of 11 bytes and the physical layer of the IEEE 802.15.4 requires another 6 bytes. Therefore, the length of data packets, considering the payload, the MAC overhead and the PHY overhead is 46 bytes.  
The evaluation is based on parameter values specified by IEEE 802.15.4 standard for the physical layer operating in the 2.4 GHz frequency band. The standard specifies, for this band, a symbol period (SP) of 16 \(\upmu\)s, bit rate of 250 kbps, a turnaround time of 192 \(\upmu\)s (\(aTurnaroundTime=12\) symbols), minimum superframe period of 15.46 ms and minimum CAP length of 7.04 ms. Other relevant parameters were previously presented in Section 2.  
The parameters of the CC2430 [10], which was the device used in the implementation of the eLPRT protocol, were used to provide the simulation results concerning the energy consumption of the nodes due to the operation of the MAC protocols. The CC2430 specifies a current consumption of 26.7 mA in RX mode, 26.9 mA in TX mode (0 dBm) and 190 \(\upmu\)A in the used sleep mode (power mode 1).
# Exploiting Lunar Navigation Constellation for GNC Enhancement in Landing Missions
## 2 Simulation Environment
### User Navigation Equipment
#### 2.2.3 Altimeter  
In landing applications, altimeters are very relevant sensors since they provide a direct estimate of the vertical position in a user-centred East-North-Up (ENU) reference frame. Additionally for this case, there is the possibility to employ fancy models of the sensor, considering the exploited technology and the considered topography of the Moon. Indeed, regarding this latter, there is a wide range of possibilities, from assuming a perfect sphere or using detailed DEM. In the current work, using the simpler spherical Moon model, the real altitude is obtained as in Equation (15) employing Moon average radius, \(R\)\({}_{\mathbb{D}}\).  
\[\zeta=\|\mathbf{r}\|-R\)\({}_{\mathbb{D}} \tag{15}\]  
The error effects introduced by the sensor are provided by a zero-mean additive white Gaussian noise, which, to reflect the behaviour of laser altimeter technology, considers a standard deviation of 1% of the current real height [30]. The resulting measurement function is thus defined as in Equation (16):  
\[\xi=\zeta+\varepsilon_{\zeta}(\xi) \tag{16}\]  
with \(\varepsilon_{\zeta}(\zeta)=\mathcal{N}(0,1)\zeta/100\) defined as the noisy component.
## 5 Results and Discussions  
### Validation of Results through Simulation of Manufacturing in Industry 4.0  
The simulation may take place during the design or optimization of a flexible manufacturing system for the determination of static load, in the design phase or for the validation of more elaborate solutions based on \"dynamic models\". These techniques are used to substantiate decisions on the optimal operation of flexible production systems. The strategic decision to digitize flexible production systems is currently an important area of research, given the complexity of these systems, which aim to increase production capacity in the context of diversifying production, shortening product life cycles and increasing competitiveness. Figure 10 shows a sequence from the simulation application performed in Technomatix--Plant Simulation to analyze and evaluate the performance of the flexible manufacturing system analyzed in the case study.  
Figure 10: Model sequence simulation of a flexible manufacturing line. Application—2D Model.  
Figure 9: Human operator interaction—HMI. A sequence from the application.  
Following the simulation analysis, by viewing the statistical data for each workstation, it was observed that one of the workstations in the system was experiencing a bottleneck (Figure 11), with a high bottleneck time (81.53%).  
The simulation method was used to set the system performance limits, determine and optimize production times: operating times, weaving times through production, \"tight spots\" in the system, auxiliary waiting times, blocking times, equipment load, etc.  
In the diagram in Figure 11, the operational and auxiliary times per workstation can be evaluated (percentage %). For example, for the workstation \"Station\": grey--waiting (tw = 0%); green--working (operational time top = 13.68%); yellow--blocked (blocking time tb = 81.83% very high, unmeasurable); red--failed (5.03%) (failed); blue--paused (stopped). In order to eliminate asynchronous in the system (i.e., deadlocks), measures were taken to optimize operation, for example by arranging a workstation in parallel (Figure 11), increasing effective working times (to 95%) according to statistical resources in Figure 12.  
Through Plant Simulation, real-time dynamic simulation of the intended manufacturing process was performed:  
* To perform stochastic (partially unpredictable) experiments;
* Modeling and simulation of a manufacturing station/line/system;
* Statistical modelling of flexible manufacturing systems where techno-economic indicators are important, such as production times, machine capacity constraints and auxiliary time constraints (waiting times, blocking times);
* Animation of the movements performed in flexible production systems using simulation software.  
Simulation models were developed to allow the user clearer control over the complex flexible manufacturing system at any level without losing any of the aspects being monitored. This modeling strategy supports graphical representations through model visualization.  
Figure 11: Sequence from the simulation program. Statistical resources—analysis of bottlenecks.