esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Literature Review
### Comparison of Raw Bamboo and Bamboo Scrimber  
A short comparative study of two common bamboo materials--raw bamboo and bamboo scrimber--provides an insight into the above, as listed in Table 1.  
Raw bamboo is the natural, untreated species more traditionally used as a construction material for basic habitats to complex structures [16].  
Bamboo scrimber, on the other hand, is a composite material that maximizes the mechanical properties of natural bamboo by processing and treating the crushed bamboo fibers with truncation, splitting, drying, assembly, cold-pressing, and heat-curing or hot-pressing. These bundled fibers are laminated using resin, maintaining the longitudinal strength in raw bamboo [23]. It is typically provided as prefabricated slats, which eliminates the cross-sectional variability common in raw bamboo [24].  
Bamboo scrimber possesses twice the compressive and tensile strength as raw bamboo [24], despite having similar flexural and shear capacities. While bamboo scrimber may perform significantly stronger in design when compared to raw bamboo, it is more costly, as it requires additional treatment and processing [23].  
For the purpose of this study, the properties of raw bamboo will used. In future studies, we recommend analyzing the mechanical performance of bamboo scrimber.  
For raw bamboo, many high-quality bamboo resources have not been effectively developed, especially for some clumping bamboo species in Southeast Asia and South Asia. Clumping bamboo is a preferred species for construction, due to its density and easy harvest. _Thyrsostachys oliveri_ is one tropical example. As a moderately large clumping bamboo with long-spanning culms, it possesses the closest properties to homogenous materials when compared to Moso bamboo _Neosinocalamus affinis_, and other clumping bamboos [25]. It is noted that no sufficient structural data have been published for this species. The following properties in Table 2 will be used for the Stand7 analysis in Section 3.  
\begin{table}
\begin{tabular}{l l l} \hline \hline  & **Raw Bamboo** & **Bamboo Scrimber** \\ \hline Material Properties &
\begin{tabular}{l} Anisotropic and \\ non-homogeneous \\ \end{tabular} & Isotropic and homogeneous \\ Cost & In-expensive & Larger due to manufacturing \\ Curvature & Harder to form curves & Easier to form curves \\ Density \(\rho\) (kg/m\({}^{3}\)) & 666 [9] & 1010 \\ Compression f\({}_{\text{c}}\) (Mpa) & 53 [9] & 134.9 \\ Tension f\({}_{\text{t}}\) (Mpa) & 153 [9] & 296.2 \\ Shear \(\tau\) (Mpa) & 16 [9] & 15 \\ Elevural f\({}_{\text{b}}\) (Mpa) & 135 [9] & 119 \\ Elastic Young’s Modulus (GPa) & 15 [9] & 22.9 \\ Poisson’s Ratio & 0.52 [9] & 0.22 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of raw bamboo and bamboo scrimber properties.
## Materials and Methods
### PHA Extraction Procedure  
The extraction of PHAs was performed after 48 h fermentation periods. The flasks were removed from their incubators and the contents were filtered with a sieve to remove any PS flakes or powder residue. The filtrate was then separated into tubes and centrifuged in a Sigma 6-16 KS centrifuge (Sigma Laborzentrifugen GmbH, Osterode am Harz, Germany) for 10 min at 4500 rpm at room temperature. At this point the supernatant was discarded and the pellet containing the biomass was frozen overnight at \(-20\)\({}^{\circ}\)C. The biomass was then lyophilized in an Edwards freeze-dier (Modulyo, Crawley, UK) for 48 h at \(-40\)\({}^{\circ}\)C and 5 MBAR. The dry biomass was then weighed and recorded as the cell dry weight (CDW). For extraction, the sample was placed inside an extraction thimble capped with cotton and, using Soxhlet extraction, for 48 h with HPLC grade chloroform, and the PHA was collected as a chloroform/biopolymer mixture. This stage was followed by rotation evaporation (at 47-50 \({}^{\circ}\)C) to remove the chloroform. Polymer precipitation using \(n\)-hexane was performed in a 250 mL round-bottom flask and then this was separated further by filtration (using Watman No. 1 paper, Watman Laboratory, Maidstone, Kent, UK). The PHA sample was then left in a sterile fume cupboard to dry before the yield was recorded using:  
PHA percentage yield = (weight of extracted biopolymer)/(dry cell biomass weight) \(\times\) 100.
## 2 Materials  
### Trajectory Data  
This study utilised sensor technology stamp data to automatically collect the latitude and longitude coordinates, along with corresponding timestamps, of a wild giant panda every two hours between 22 November 2017 and 10 May 2018. The data collection was focused on the region within Sichuan Province, China, specifically between the coordinates 28.8532659\({}^{\circ}\) to 29.0683029\({}^{\circ}\) N and 102.3025714\({}^{\circ}\) to 102.4376521\({}^{\circ}\) E. Figure 1 displays the trajectory data, with the horizontal axis representing longitude and the vertical axis representing latitude. We gathered a grand total of 1925 data points for giant pandas. During the process of data manipulation, we eliminated a total of 123 data points as a result of their absence or corruption. The data utilised in this investigation were exclusively gathered for this study and are currently not accessible to the public.  
Additionally, we utilised a publicly available African elephant dataset, which includes the migration trajectory data of three elephants, specifically identified by the codes A9, A8, and A7. This dataset was sourced from Movebank (movebank.org, accessed on 22 July 2024, study name \"African elephant (Migration) Chamaille-Jammes [PERSON] NP\", study ID 307786785), and published in the Movebank Data Repository.
# Securing Wireless Sensor Networks Using Machine Learning and Blockchain: A Review
## 8 ML and Cyberattack Detection  
ML algorithms are used to build self-learning classifiers consisting of behaviors, which are able to act without human intervention by using mathematical techniques based on specialized datasets. These algorithms enhance the network nodes' ability to learn without being explicitly programmed. Such models are used to make future predictions based on new input data. ML algorithms are currently used in various applications, such as smart cities, energy, agriculture, intelligent transportation systems, industry and manufacturing, search engines, social media, cyberattack detection, spam email filtering, and recommendation systems. Different ML techniques are used to improve the functionalities of WSNs, such as data sensing, CH selection, routing and optimal path determination, data aggregation, minimizing packet delivery latency, duty cycle management, quality of service (QoS) provisioning, resource management, and to increase network lifetime. ML algorithms have been used to design lightweight detection and mitigation systems to secure WSNs against cyberattacks. They allow sensor nodes to detect possible attacks and immediately take appropriate actions to mitigate the impact of an attack by triggering an alarm, determining the degree of the risk, and isolating the attacker node from the next round of network progress [60]. The ML pipeline spans data collection and pre-processing feature selection, model training using proper ML algorithms, hyperparameter tuning, model testing, validation, and deployment.
## 5 Performance Evaluation Results and Discussion
### Effect of Configuration Parameter \(T_{b}\) on Successful Bids  
Figure 21 shows the number of different successful bids between the cases with \(T_{b}\) (value used in other simulations) and with \(T_{b}=3\). Figure 21 shows the number of successful bids with the evaluation score of successful bids with \(T_{b}=3\) being higher (\(T_{b}=3>T_{b}=1\)) and the number of data requests with the evaluation score of successful bids with \(T_{b}=1\) being higher (\(T_{b}=1>T_{b}=3\)). The results in Figure 21 show that the number of different successful bids between the cases with \(T_{b}=3\) and \(T_{b}=1\) is small for  
Figure 20: Number of D2D messages.  
both the pedestrian and vehicle cases. The results also show that the number of different successful bids between the case with \(T_{b}=3\) and the case with \(T_{b}=1\) is larger in the vehicle case than in the pedestrian case. Figure 20 shows the average number of D2D messages per host per second. From Figure 20, we can see that the number of transmitted and received D2D messages was reduced with \(T_{b}=3\). From these results, we confirmed that the number of D2D messages can be reduced by changing \(T_{b}\) from 1 to 3 s with few changes in the successful bids for this simulation scenario.  
The reason why the number of different successful bids is larger in the vehicle case than in the pedestrian case is related to the NEA accuracy. As explained in Section 5.1.3, host movement reduces the accuracy of NEA information. As the mobile hosts move faster, the error in the information on the location of the mobile hosts used to calculate the NEA increases, which is further increased with a larger \(T_{b}\). This explains why the number of different successful bids in the vehicle case is larger than in the pedestrian case. In Figure 20, the average number of transmitted and received D2D RevAuc messages is a little bit larger with \(T_{b}=3\) than with \(T_{b}=1\) in both the pedestrian case and the vehicle case. This indicates that the number of mobile hosts participating in a RevAuc through D2D communication is larger with \(T_{b}=3\) than with \(T_{b}=1\). We think that this is the reason why the number of successful bids with higher evaluation scores with \(T_{b}=3\) is greater than the number of successful bids with higher evaluation scores with \(T_{b}=1\).
# Inertial Sensors-Based Assessment of Human Breathing Pattern: A Systematic Literature Review
## 2 Methodology
### Data Extraction and Categorization
#### 2.4.4 Experimental Task Performed  
As expected, all 22 selected studies referred to the breathing activity as the primary experimental task used to measure BP metrics. This was carried out either through simulated and guided methods (such as mimicking yawns, coughs, or sighs and adjusting breath speed or volume) or in a more natural and spontaneous manner. Nevertheless, a subset of studies specified the measurement of breathing in a particular posture [48; 53; 59; 61; 65; 68], alternating between different postures [49; 50; 56; 58; 66; 67], and during sleep [57; 63; 69], physical activity [62], and speech [53].
## 2 Materials and Methods
### Subclustering  
To further examine the geospatial data and their clusters, we performed a clustering analysis on each of the initial \(k\) clusters. We note that the subclusters identified by this analysis would generally not be identified in the initial clustering analysis, even allowing for different numbers of initial \(k\) clusters, because the data considered during clustering are different. During the initial clustering analysis, data from all of the CONUS are used, but during this subclustering analysis, data are separated by their initial clustering assignment. Hence, subclustering identifies natural groupings or clusters within each of the initial clusters, which would not have been apparent in the initial clustering.  
Similar to the initial clustering analysis, we had to identify an appropriate number of subclusters for each of the initial clusters. We used silhouette analysis and found that for all clusters, the optimal number of subclusters was determined to be two. Note that the silhouette score cannot be calculated for a single cluster, so silhouette analysis cannot indicate whether a single cluster (i.e., no subclustering) should be preferred.  
Therefore, to determine if subclustering into two subclusters is beneficial for any individual cluster, the estimated probability densities of the distance (as measured by the Euclidean norm) between instances within each subcluster and the corresponding initial cluster centroid were plotted. These plots were overlaid with similar plots using the identified subcluster centroids (rather than the initial cluster centroid). This was performed for each cluster to compare the results of subclustering into two subclusters and performing no subclustering. These distributions can be seen in Appendix B, Figures 11-18. For the case in which adding a second centroid (i.e., subclustering into two clusters) significantly moved both distributions to the left, it is more likely that subclustering is beneficial for further describing the data. Marginal shifts indicate that subclustering made only minor improvements in accurately clustering the data at the cost of simplicity in the model.  
In this paper, we present results of performing subclustering for all clusters into two subclusters, independent of the changes in the distributions of the distance to centroids shown in Figures 11-18. Depending on the desired application of clustering results, sub clustering may prove useful even when the results of subclustering do not immediately indicate improved clustering. Although we do not discuss which cases of subclustering appear most beneficial, the interested reader is referred to Appendix B for further subclustering results.
# A Linear Memory CTC-Based Algorithm for Text-to-Voice Alignment of Very Long Audio Recordings
## 3 Method
### Linear Memory DTW Algorithm
#### 3.2.3. Pivot Search on \(\mathbf{d^{k}}\) and \(\mathbf{d^{k-1}}\)  
The point on \(\mathbf{d^{k}}\) or \(\mathbf{d^{k-1}}\) with the maximal alignment measure only reflects the local optimal alignment between the corresponding prefixes of sequences \(\mathbf{a}\) and \(\mathbf{b}\), _not between the entire sequences_\(\mathbf{a}\) and \(\mathbf{b}\). This point is thus not necessarily on the optimal path.  
Let \(\mathbf{a}_{R}\) and \(\mathbf{b}_{R}\) denote the sequences \(\mathbf{a}\) and \(\mathbf{b}\) taken in the reversed order, and let \(\alpha_{R}(i,j)\) denote the alignment measure between the prefixes \(\mathbf{a}_{R}[1:i]\) and \(\mathbf{b}_{R}[1:j]\). [PERSON] and [PERSON] demonstrated that the optimal alignment measure \(\mathrm{A^{*}}\) of Equation (5) can be re-formulated as:  
\[\mathrm{A^{*}}=\alpha(i,j)+\alpha_{R}(|\mathbf{a}|-i+1,|\mathbf{b}|-j+1)- \mathcal{S}(a_{i},b_{j})\qquad\forall(i,j)\in\boldsymbol{\pi}^{*} \tag{8}\]  
In other terms, for any point \((i,j)\)_on the optimal path_, the global optimal alignment measure between \(\mathbf{a}\) and \(\mathbf{b}\) is equal to the alignment measure from \((1,1)\) to this point \((i,j)\), plus the alignment measure in reversed order from \((|\mathbf{a}|,|\mathbf{b}|)\) to this point \((i,j)\), minus the similarity measure at \((i,j)\), otherwise counted twice.  
For a given \(k\), there is at least one point on the pair of diagonals \(\mathbf{d^{k}}\) or \(\mathbf{d^{k-1}}\) that also lies on the optimal path and that can be used as a pivot: it is the point \((i,j)^{*}\) maximised in the right hand side of Equation (8), by definition of the optimal alignment measure:  
\[(i,j)^{*}=\operatorname*{arg\,max}_{(i,j)\in\mathbf{d^{k}},\cup\mathbf{d^{k-1 }}}(\alpha(i,j)+\alpha_{R}(|\mathbf{a}|-i+1,|\mathbf{b}|-j+1)-\mathcal{S}(a_{ i},b_{j}) \tag{9}\]  
To compute the right hand side of Equation (8), for all points belonging to \(\mathbf{d^{k}}\) and \(\mathbf{d^{k-1}}\), let \(\mathbf{d^{k}_{R}}\) denote the \(k^{th}\) diagonal of the \(\mathbf{a}_{R}\) matrix. Importantly, note that the diagonal \(\mathbf{d^{k}}\) (respecting \(\mathbf{d^{k-1}}\)) and \(\mathbf{d^{K-k+1}_{R}}\) (respecting \(\mathbf{d^{K-k+2}_{R}}\)) overlap, as depicted on Figure 6. For a given \(k\), the computation of the diagonals \(\mathbf{d^{k}}\), \(\mathbf{d^{k-1}}\) in a forward direction, and \(\mathbf{d^{K-k+1}_{R}}\) and \(\mathbf{d^{K-k+2}_{R}}\) in a backward direction is therefore enough to find the corresponding pivot.
## 2 Background and Related Works
### Navigation Challenges in a Physical Space, Ethics and Privacy Related to Telepresence  
The majority of ethical issues afflicting robotic telepresence applications concern user privacy and safety. Physical safety issues are quite obvious, as the robot is a heavy mobile object controlled by someone who is not physically present. Depending on the quality and quantity of sensors available on the robotic platform, the operator might not be completely aware of its surroundings. The risk of breaking objects or harming human beings is not negligible. It is even possible for the robot itself to incur damages (e.g., falling down the stairs). The risks increase when the robot is introduced to a dynamic environment, i.e., not every object around the robot is stationary. If the robot is operating in crowded areas, the probability of colliding with others increases.  
A second set of important issues are those related to privacy. Within this context, privacy must be considered in two different facets: privacy of the users participating in the communication from the rest of the world (hereby \"external\" privacy), and privacy of the owner of the Telepresence robot from its potential users (\"internal\" privacy).  
External privacy is the expectation that the conversation between users of a telepresence application is private and cannot be intercepted by third parties. This also implies having the certainty that the peer in the communication is in fact who she claims to be (i.e., authentication). These are the same issues that afflict classic teleconferencing applications, like videechat or even the telephone.  
More interesting are the issues regarding internal privacy. Having a mobile robot, equipped with a camera, inside one's own house, poses a lot of concerns. The obvious one is: who can access the robot? Giving a user access to the telepresence application is the equivalent of inviting them inside one's own house. Technically, the robot could go and look everywhere. Even assuming that the owner can control who can use the robot, there is the more concerning possibility (always present) of unauthorized access (e.g., from a hacker).  
Additionally, the robot may collect data necessary for its correct functioning (e.g., map of the environment). It is not possible to avoid collecting this data, as it would compromise the robot's correct operation. This data might contain sensitive information, and therefore must be kept secure.
## 2 Antenna Design and Optimization
### Parametric Analysis  
After exhaustive simulation studies focusing on the antenna's dimensions, practical lengths and positions were identified to optimize performance. Two merged cuts were responsible for creating the dual band at 2.45 and 5.0 GHz with the circular cut positioned at the 20 \(x\)-axis and 19.25 \(y\)-axis, and the horizontal rectangle at the 32.9 \(x\)-axis and 22 \(y\)-axis, with a small gap of g1 at both edges of the patch. On the bottom side, the optimum value for the length of the ground plane (Lg) is 24 mm and below this value it turns the response into the UWB, which is far from the operating band. The L-shaped slot starts from the gap below the edge of the ground plane at g3 and a distance from the right and left sides of g2. The parameters WV_slot and LH_slot affect return loss and gain especially at 2.4 GHz, whereas the parameters WH_slot and LV_slot affect return loss and gain at 5 GHz. The parameters Wsq and Lsq affect return loss and gain in dual-band operation. These slots are affected only in return loss and gain.
# Action Recognition Algorithm of Spatio-Temporal Differential LSTM Based on Feature Enhancement
## 4 Experiments
### Experimental Results and Analysis
#### 4.3.2 The Influence of the Number of Stacking Layers  
In the performance verification and comparison experiment, the recognition accuracy obtained by stacking five-layer ST-D LSTM units was used. However, in the actual process of parameter adjustment, it can be found that the performance of stacking different layers of ST-D LSTM units is different in accuracy and training speed. Therefore, the ST-D LSTM units are stacked one layer, two layers, three layers, four layers, and five layers, respectively, and the LRCN network is applied for experiments. In this experiment, only the number of layers varies, the other parameters such as batch size, parameters of the hidden layer, training steps and so on are consistent. The process of accuracy climbing is shown in Figure 6, and the stable accuracy is shown in Table 2.  
When ST-D LSTM units with different layers are stacked, there is a significant difference in training speed. The impacts are studied from two aspects of accuracy and training speed. The network training speed is shown in Table 3. In the speed experiment, the fps index is used, that is, the number of video frames  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **UCF-101** & **HMDB-51** & **Hollywood2** \\ \hline basic LSTM & 71.15\% & 39.99\% & 46.49\% \\ ST-LSTM & 72.73\% & 42.53\% & 47.41\% \\ ST-D LSTM & 75.70\% & 44.11\% & 49.02\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: The accuracy of different LSTM units on three datasets.  
Figure 6: The comparison of the accuracy increasing process of stacked ST-D LSTM units with different layers.  
Through experiments, it can be found that increasing the number of layers can improve the accuracy. When five layers are stacked, ST-D LSTM units perform the best on the HMDB-51, UCF-101, and Hollywood2 datasets. However, increasing layers will also increase the time needed for reading data and training. Stacking too many layers will slow down the training. When studying the translation task based on LSTM, [PERSON] et al. (2021) found that the network can work well by simply stacking four layers of LSTM units, and six layers is the limit. Stacking more than eight layers makes the network fail. Table 2 shows that when ST-D LSTM units are stacked to layers 4 and 5 on the HMDB-51 dataset, the recognition accuracy only increases slightly. Therefore, although stacked LSTM layers can increase network performance, in general, the LSTM units can better balance the training speed and accuracy with 4-5 stacked layers.