esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Works
### Multicarrier Modulations in DVB-T2
These PDPs were exploited to evaluate the performance of DVB-T2 in Bandung and Jakarta using channel coding or not, with various transmission parameters. The results were useful to identify the optimal parameter on DVB-T2 technology. In 2020, the performance of turbo channel coding were analyzed and compared to Low-Density Parity Check (LDPC) in DVB-T2 system [21]. The turbo decoder is an iterative decoder that presents the principle of a turbo engine. The system performance were evaluated first when turbo codes were used or not and second, when turbo codes and LDPC codes were compared. It was shown that LDPC codes presented better performance than turbo codes. In the same year, instead of turbo codes applied in DVB-T2, the ML detector performance was evaluated in that system with a MIMO transmission scheme [22]. That detector's performance was analyzed when LDPC channel coding was applied or not. The Minimum Mean Square Error (MMSE) channel estimation method was used for the channel estimation. These performance values were evaluated in the case of an ideal channel estimation and the case of the MMSE estimation method. It was demonstrated that the channel response estimated by MMSE was not the same as the ideal channel response, leading to significant errors in the data received from the ML detector. When LDPC coding was applied, an efficient SNR improvement was observed. In 2024, DVB-T2's performance was examined within High-Speed Train (HST) communication systems [23]. The main challenges were the high Doppler shifts and multipath effects management. The results proved that the error rates varied with speed and environmental conditions. The speeds considered during the simulation process were 10 m/s, 50 m/s, and 100 m/s. The CCDF, the Doppler effect, and the bit error number in excess were calculated for precise values of the mobile system's speed. From all the above, we can see that the OFDM used in DVB-T2 has certain shortcomings. To overcome these shortcomings, several techniques are used, such as the MMSE channel estimation, the use of coding rates other than LDPC, etc. Other authors have proposed filtered-based multicarriers more efficient than OFDM.
## 4 The Proposed Routing Protocol: HPA-SR
### The Proposed Hybrid-Price Auction-Based Secure Routing Protocol: HPA-SR
* **Step 7: Calculation of Cost/Bidding Value for Secure Route Establishment** The intermediate node NB\({}_{j}\) calculates cost/bidding value \(b_{j}\). NB\({}_{j}\) will compare the cost/bidding value \(b_{j}\) receiving with the bidding threshold \(B_{th}\). If the \(b_{i}\) is greater than \(B_{th}\), NB\({}_{j}\) will use second price auction (SPA) to determine the route to be pursued by the next node. Otherwise, if the \(b_{i}\) is less than \(B_{th}\), then NB\({}_{j}\) will use first price auction (FPA) to determine the route to be traversed by the next node. NB\({}_{j}\) will select the next node for data transmission based on a hybrid price auction process model that adaptively decides the auction model among the first price auction and the second price auction against the sinkhole attack, then we will obtain the routing table that can be summarized in Table 2, where PN is previous node, NB\({}_{j}\) as next node NN, Cost is cost/bidding value, S\({}_{\text{ID}}\) is source ID and D\({}_{\text{ID}}\) is destination ID, respectively. The routing table will be used to determine the next node to the destination node that will be passed by the data packet during the data transmission process. Besides, we will explain the detailed process of hybrid price auction in Section 4.4. Then, go to data transmission process in **step 8**. **Data Transmission Process:**  
* **Step 8: Data Transmission at Source Node** The source S sends data packet to the destination D based on the routing table, which is determined in **Step 1 to Step 7**.
## 2 Materials and Methods
### Characterization of Activated Carbon  
The textural properties were evaluated through static gas adsorption in a Sync 200 instrument (3P Instruments(r), Odelzhausen, Germany). Samples were degassed at 150 \({}^{\circ}\)C under vacuum for 12 h using a Prep J4 unit (3P Instruments(r), Odelzhausen, Germany). The N\({}_{2}\) adsorption-desorption isotherms were obtained at 77 K, leading to the total surface area (S\({}_{\text{BET}}\)) ascertained using the BET method. The micropore surface area (S\({}_{\text{MP}}\)) and micropore volume (V\({}_{\text{MP}}\)) were determined using the t-plot method. The pore size distribution was derived from CO\({}_{2}\) adsorption at 273 K, applying the Horvath-Kawazoe (HK) model [46; 47].  
The bulk chemical composition was determined by proximate and elemental analysis. Proximate analysis was performed using a Perkin Elmer(r) STA 6000 thermobalance (Waltham, MA, USA). Samples were heated under N\({}_{2}\) (20 mL min\({}^{-1}\)) with the following temperature profile: held at 30 \({}^{\circ}\)C for 20 min, then heated to 110 \({}^{\circ}\)C (20 \({}^{\circ}\)C min\({}^{-1}\)), and held for 20 min to measure moisture content. The volatile matter was determined by heating to 850 \({}^{\circ}\)C (20 \({}^{\circ}\)C min\({}^{-1}\)) and holding for 20 min. Ash content was measured by switching the gas from N\({}_{2}\) to O\({}_{2}\) (20 mL min\({}^{-1}\)) and maintaining it at 850 \({}^{\circ}\)C for 20 min. Elemental analysis (CHNS) was carried out using a Thermo Scientific(tm) Flash 2000 analyzer (Waltham, MA, USA). Samples were combusted at approximately 1400 \({}^{\circ}\)C, producing CO\({}_{2}\), H\({}_{2}\)O, NO\({}_{x}\), and SO\({}_{2}\), which were separated chromatographically and detected with a thermal conductivity detector. The oxygen content was tentatively estimated by subtracting the CHNS values and ash content.  
The surface chemical composition was analyzed using X-ray photoelectron spectroscopy (XPS) on a Kratos AXIS Ultra-DLD device from Jenck S.A. (Buenos Aires, Argentine), with Al K\(\alpha\) radiation as the X-ray source. Spectra were calibrated to the C\({}_{1s}\) peak (284.6 eV) of adventitious sp\({}^{3}\) carbon, and peak deconvolution was performed using XPS-peak software (version 4.1) under a Shirley background correction. The chemical surface was further analyzed by thermal programmed desorption (TPD) with NH\({}_{3}\) on an AMI-300 Lite chemisorption station from Altamira Instruments(r) (Pittsburgh, PA, USA) equipped with a Thermal Conductivity Detector (TCD). Briefly, 100 mg of sample was loaded and  
Figure 1: Activation of the char with Na\({}_{2}\)CO\({}_{3}\) (**A**) and post-thermal citric acid treatment (**B**).  
submitted to adsorption with NH\({}_{3}\) (10%NH\({}_{3}\) in He) under 100 \({}^{\circ}\)C, and a temperature program from 100 \({}^{\circ}\)C to 1000 \({}^{\circ}\)C was applied.
# Asynchronous Chirp Slope Keying for Underwater Acoustic Communication
### Historical Overview  
Evidence suggests that the general idea to sweep the frequency of a carrier to transmit information may be as old as (breathing) life on earth [27], but the first modern record we found is [PERSON] et al.'s patent for a distance measurement method from 1940 [28]. The idea to use chirps for modulating a signal, e.g., as Chirp Slope Keying (CSK) is often attributed to [PERSON] et al.'s work in the early 1960s [29], and also sometimes referred to as linear frequency modulation (LFM) [30]. The concept although was patented as early as 1949 by [PERSON] et al. [31]. After the application in satellite radio transmission during the Cold War era [32; 33], the interest in chirp modulations mostly vanished due to the low achievable data rates. For radio communications, this changed in 2013 with the establishment of the LoRa protocol as a low-power long range radio modulation for small consumer applications [34; 35].
## 3 Results and Discussion
For a direct comparison of both spectrogram types, see Figure 12.  
Overall, the greater the noise level, the less stable the results of classification were across multiple runs. Both spectrogram types sometimes failed to learn anything from the data and returned just 50% classification accuracy in very noisy datasets. This happened quite often to data from spectrogram 2. On the other hand, with the use of this type of spectrogram, the network reached its highest accuracy rates on less noisy data by approximately 2%, compared to data transformed by spectrogram 1.
###### Abstract  
\(\beta\)-glucan extraction from spent brewer's yeast is a long process that starts with the lysis of yeast cells, this step lasting up to 36 h and can be disadvantageous when working on a small scale. In this study, a rapid cell rupture method was selected for the lysis of spent brewer's yeast to obtain \(\beta\)-glucans. Optimal parameters were determined for the lysis of a cellular suspension of spent brewer's yeast by vortexing with glass beads. Thus, parameters such as the number of 10 min vortex cycles from 1 to 3, the concentration of cell suspension (5, 10, and 15%), and the ratio of yeast/glass beads (1:1, 1:2, and 1:3) were varied in a Box-Behnken design. A cell lysis mechanism using glass beads allows the cell to rupture and permits the removal of intracellular content. An increase in yeast suspension concentration decreased the disruption efficiency, while a proportional increase was observed with the yeast/glass beads ratio and the increasing number of vortexing cycles. The optimal parameters for cell lysis were found to be a cell suspension concentration of 5%, a ratio of yeast/glass beads of 1:2, and a vortexing cycle of 3, with a disruption efficiency of 99.8%. The \(\beta\)-glucan fraction extracted from the optimal sample showed characteristic absorption bands at 1370.77 and 1153.92 cm\({}^{-1}\), the content of \(\beta\)-glucan being 78.53%.  
glass beads; \(\beta\)-glucan; mechanical disruption; spent brewer's yeast; _Saccharomyces cerevisiae_
## Abbreviations  
\begin{tabular}{l l} ECS & Energy consumption scheduler \\ DR & Demand response \\ EM & Energy management \\ DGs & Distributed generators \\ GWDO & Genetic wind-driven optimization \\ ESSs & Energy storage systems \\ SPG & Smart power grid \\ DSM & Demand-side management \\ DE & Differential evaluation \\ EDE & Enhanced differential evaluation \\ ICT & Information and communication technologies \\ PSO & Particle swarm optimization \\ EUCs & Electric utility companies \\ GA & Genetic algorithm \\ IBT & Incline block tariff \\ RTP & Real-time pricing \\ PV & Photovoltaic \\ BFA & Bacterial foraging algorithm \\ EDGE & Enhanced differential genetic evolution algorithm \\ ACO & Ant colony optimization \\ GPSO & Genetic particle swarm optimization \\ GBFA & Genetic bacterial foraging algorithm \\ GACO & Genetic ant colony optimization \\ PADR & Peak to average demand ratio \\ MILP & Mixed integer linear programming \\ EVs & Electric vehicles \\ FPS & Flat pricing scheme \\ ANN & Artificial neural network \\ STSMC & Supper twisting sliding mode controller \\ PID & Proportional integral derivative controller \\ FO-PD & Fractional order-proportional derivative controller \\ ToU & Time of use pricing scheme \\ CPP & Critical peak pricing scheme \\ MINLP & Mixed integer non-linear programming \\ NSGA-II & Non-dominated sorted genetic algorithm \\ AMI & Advanced metering infrastructure \\ DSO & Distribution system operator \\ WT & Wind turbine \\
**Notations** \\ \(A^{\prime}_{pi}\) & Portable interruptible appliances \\ \(A^{\prime}_{pni}\) & Portable uninterruptible appliances \\ \(A^{\prime}_{c}\) & Consistent appliances \\ \(e^{\prime}_{pi}\) & Energy consumption per day portable interruptible appliances \\ \(\lambda^{\prime}_{pi}\) & Power rating of portable interruptible appliances \\ \(\alpha(\tau)\) & Appliances On/off status \\ \(p(\tau)\) & Real-time pricing signal \\ \(\lambda^{\prime}_{pni}\) & Power rating of portable uninterruptible appliances \\ \end{tabular}
## 3 System Model
### Communication Model  
#### 3.1.1 Train-to-Wayside (T2W) Link  
The transmission of T2W link is on an independent channel; thus, there is no other link interference, and the signal-to-noise ratio (SNR) of the T2W transmission is  
\[\gamma_{T_{n},R_{m}}=\frac{p_{T_{n}}\left|h_{T_{n},R_{m}}\right|^{2}}{N_{0}}, \tag{2}\]  
where \(p_{T_{n}}\) is the transmission power of the train \(T_{n}\), \(h_{T_{n},R_{m}}\) is the channel gain between the train \(T_{n}\) and the wayside relay \(R_{m}\), and \(N_{0}\) is the noise power. Hence, the channel throughput [15] between train \(T_{n}\) and the wayside relay \(R_{m}\) is  
\[C_{T_{n},R_{m}}=B\log_{2}(1+\gamma_{T_{n},R_{m}}). \tag{3}\]  
As for the final hop, wayside node \(R_{m}\) transmits to destination train \(T^{\prime}{}_{n}\), which is also calculated in the same way as above, except that the transmission direction is different. The throughput between \(R_{m}\) and destination train \(T^{\prime}{}_{n}\) can be expressed as  
\[C_{R_{m},T^{\prime}{}_{n}}=B\log_{2}(1+\gamma_{R_{m},T_{n}}). \tag{4}\]
# TRAL: A Tag-Aware Recommendation Algorithm Based on Attention Learning
## 5 Experiments
### Ablation Studies
#### 5.4.1 Effect of the Attention Network  
To investigate the effect of the attention network, we replace it with average pooling to generate a variant method called TRAL-no-A. Figure 3 presents the recommendation results on two datasets in terms of HR and NDCG metrics. It can be observed that TRAL performs  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Dataset** & \multicolumn{4}{c}{**Delicious**} & \multicolumn{4}{c}{**LastFM**} \\ \hline
**Metrics** & **HR@10** & **NDCG@10** & **HR@20** & **NDCG@20** & **HR@10** & **NDCG@10** & **HR@20** & **NDCG@20** \\ \hline CCF & 0.6103 & 0.3851 & 0.6346 & 0.4123 & 0.5420 & 0.2548 & 0.5618 & 0.2652 \\ ACF & 0.6524 & 0.4216 & 0.6812 & 0.4520 & 0.5624 & 0.2651 & 0.5816 & 0.2764 \\ NCF & 0.6836 & 0.4435 & 0.6970 & 0.4712 & 0.5961 & 0.2872 & 0.6108 & 0.3056 \\ DSPR & 0.8041 & 0.6182 & 0.8166 & 0.6315 & 0.6854 & 0.3125 & 0.7012 & 0.3204 \\ TRSDL & 0.7925 & 0.6012 & 0.8104 & 0.6214 & 0.7052 & 0.3356 & 0.7126 & 0.3468 \\ BPR-T & 0.7123 & 0.5556 & 0.7492 & 0.5725 & 0.6532 & 0.3173 & 0.6750 & 0.3325 \\ STEM & 0.7458 & 0.5423 & 0.7643 & 0.5680 & 0.6726 & 0.3027 & 0.6953 & 0.3252 \\ TRAL & **0.8518** & **0.6345** & **0.8618** & **0.6505** & **0.7336** & **0.3621** & **0.7582** & **0.3820** \\ \hline Imp. & 5.9\% & 2.6\% & 5.5\% & 3.0\% & 4.0\% & 7.9\% & 6.4\% & 10.2\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Overall performance comparison on two datasets in terms of HR and NDCG metrics. Boldface represents the highest score and underline denotes the best result of the baselines. The improvement (imp.) of the proposed model compared to the best baseline is calculated.  
significantly better than TRAL-no-A on both metrics, indicating that the recommendation performance will be seriously degraded without the attention network. Benefiting from the attention mechanism, different tag-based features are automatically compressed into a fixed-length vector with different weights. More accurate representations of users and items are thus derived to facilitate subsequent recommendations.  
As the model capability of the attention network is affected by the attention factor, we conducted several experiments to further study the impact of the attention mechanism. Figure 4 displays the results of different attention factors on the two datasets in terms of NDCG@10. Note that for Delicious and LastFM, the range of NDCG values under different attention factors falls within [0.62, 0.64] and [0.35, 0.37], respectively. The results show that the performance of TRAL is relatively stable across different attention factors on both datasets. It demonstrates that the design of attention network can make the model have strong robustness while improving the algorithm performance.  
Figure 3: Performance comparison in terms of HR@n and NDCG@n on the two datasets. (**a**) HR@n on Delicious. (**b**) NDCG@n on Delicious. (**c**) HR@n on LastFM. (**d**) NDCG@n on LastFM.
###### Abstract  
Rapidly developed deep learning methods, widely used in various fields of civil engineering, have provided an efficient option to reduce the computational costs and improve the predictive capabilities. However, it should be acknowledged that the application of deep learning methods to develop prediction models that efficiently assess the nonlinear dynamic responses of cross-fault hydraulic tunnels (CFHTs) is lacking. Thus, the objective of this study is to construct a rational artificial neural network (ANN) prediction model to generate the mass data and fragility curves of CFHTs. Firstly, an analysis of 1080 complete nonlinear dynamic time histories via incremental dynamic analysis (IDA) is conducted to obtain the mass data of the drift ratio of the CFHT. Then, the hyper-parameters of the ANN model are discussed to determine the optimal parameters based on four examined approaches to improve the prediction capacity and accuracy. Meanwhile, the traditional probabilistic seismic demand models of the predicted values obtained by the ANN model and the numerical results are compared with the statistical parameters. Eventually, the maximum likelihood estimation couping IDA method is applied to assess the seismic safety of CFHTs under different damage states. The results show that two hidden layers, ten neurons, and the ReLU activation function for the ANN model with Bayesian optimization can improve the reliability and decrease the uncertainty in evaluating the structural performance. Moreover, the amplitude of the seismology features can be used as the neurons to build the input layers of the ANN model. It is found through vulnerability analysis that the traditional seismic fragility analysis method may overestimate the earthquake resistance capacity of CFHTs compared with maximum likelihood estimation. In practical engineering, ANN methods can be regarded as an alternative approach for the seismic design and performance improvement of CFHTs.  
deep learning; hyper-parameters; hydraulic tunnels; maximum likelihood estimation; fragility analysis +
Footnote †: journal: Applied Sciences
# An Algorithm to Find the Shortest Path through Obstacles of Arbitrary Shapes and Positions in 2D
## 3 Materials and Methods
### Our Definition of Simpler Graph Edges  
The bitangents that link consecutive obstacles in the shortest path need to be connected together to make a continuous path. A new feature of our proposed method consists in doing this connection through a convex arc tangent to the obstacle boundary. Such convex arcs would be the paths that a bug-robot with an infinite range sensor would follow when moving under the [PERSON] et al. TangentBug algorithm [32]. They are the shortest paths that link two points on the surface of an obstacle. Figure 7 shows such a convex arc that links the two points \(A\) and \(B\) in the counter-clockwise direction.  
Upon adopting the convention of Moravec, we characterize the direction in which a path follows the boundary of an obstacle, by virtually duplicating the obstacles and assigning a different direction to the boundaries of these duplicates. Thus, if \(O\) is the absolute value of the identity number of an obstacle, then \(+O\) and \(-O\) represent, respectively, the version this obstacle with the boundary in the counter-clockwise or in the clockwise direction. The notation \(O\) will stand for a signed obstacle number: either \(+O\) or \(-O\).  
The nodes of our graph are the starting point \(S\), the destination point \(T\) and the points of arrival of the free rectilinear bitangents in the paths from \(S\) to \(T\). The edges are made up of two connected segments: a convex arc that starts at a node on an obstacle and is tangent to its boundary, followed by a bitangent that extends from its end to another obstacle. Figure 8a shows a situation in which there are two edges: the segment \(ABC\) that links the two obstacles \(-O_{1}\) and \(-O_{2}\), and the segment \(ADE\) that links the obstacles \(-O_{1}\) and \(+O_{2}\). In Figure 8a, the parts of the convex hull of the obstacles, which are outside of the obstacles, are shown in pale grey. We note that that since \(+O\) and \(-O\) are considered as two different obstacles, there could well be an edge between them, as is the edge \(ABC\) in Figure 8b that links \(-O\) to \(+O\). The graph so defined is actually a directed graph.  
The graph so defined has a much smaller cardinality than the graphs of the L-A graph family. For example, it has only 8 nodes and 8 edges for the obstacles shown in Figure 3, while the L-A graphs have 18 nodes and 21 edges. This smaller cardinality implies that fewer steps will be required for the path optimization algorithm to construct the shortest path.  
Figure 6: Tangent segments in a path going through a passage.  
Figure 7: The tangent convex arc linking the points \(A\) and \(B\). Obstacle shown in gray.
# Improved RSSI Indoor Localization in IoT Systems with Machine Learning Algorithms
## 5 Results and Discussion
### Accuracy vs. k Value in kNN  
Figure 10 shows the effect of the k value in the kNN on the accuracy. When k increased from 2 to 6, we can observe an exponential increment in accuracy. Additionally, from 6 to 20 the accuracy increased. A low number of k indicates that noise will have a more significant impact on the outcome. A considerable value makes it computationally expensive and somewhat contradicts the fundamental tenets of KNN.  
Figure 10: Accuracy vs. number of samples with different k values.  
Figure 9: Accuracy vs. number of samples, with different number of vectors.
## 3 Results and Discussion
### Data Processing using Machine Learning Methods
#### 3.4.1 Data Preparation  
For accuracy of the experiment, the data consisted of AFM-scans of substrates and was brought to a unified format by means of standard methods, which are used in the field of atomic force microscopy (AFM). The following procedures were carried out consistently: shifting of the mean value to zero, normalization by the standard deviation, polynomial background subtraction, rows alignment, defects corrections.