esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Characteristics of Activated Fly Ash
The relationship between fineness, wide particle size distribution, and self-hardening properties has also been highlighted by [PERSON] et al. [37]. Both FA(B)63 fly ash and FA(B)125 fly ash are mainly composed of particles in this range of 1-100 \(\upmu\)m. Especially, FA(B)63 fly ash had a much smoother particle size distribution than untreated FA(B)N fly ash (Figure 2), which indicates that the grain size composition of that \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Element** & **Spot 1** & **Spot 2** & **Spot 3** & **Spot 4** & **Spot 5** & **Spot 6** & **Spot 7** & **Spot 8** \\ \hline C & 10.8 & - & 15.5 & 20.6 & - & - & 29.1 & 19.8 \\ O & 52.9 & 44.9 & 42.0 & 35.9 & 38.4 & 38.0 & 39.5 & 45.1 \\ Na & - & - & 1.6 & - & 5.2 & - & - & - \\ Mg & 1.8 & - & 6.4 & - & 10.3 & 8.5 & 6.0 & 3.4 \\ Al & 0.8 & - & 1.3 & 11.5 & - & 2.2 & 0.9 & - \\ Si & 1.1 & 55.1 & 3.3 & 12.3 & 2.0 & 4.4 & 2.1 & 1.7 \\ Cl & - & - & - & - & - & - & - & 1.5 \\ P & - & - & 4.9 & - & 13.0 & 6.3 & 3.6 & - \\ K & 1.6 & - & 1.6 & 17.1 & 10.4 & 1.4 & 1.9 & 1.8 \\ Ca & 31.0 & - & 23.4 & 2.6 & 20.7 & 34.8 & 16.9 & 26.7 \\ Mn & - & - & - & - & - & 2.7 & - & - \\ Fe & - & - & - & - & - & 1.7 & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Chemical elements detected in EDS spots of fly ash sieved with 63 \(\upmu\)m (by weight %). Figure 4: EDS spot distribution in an SEM photograph of fly ash sieved with 63 \(\upmu\)m.  
fly ash had fewer clusters. The modification of the particle size distribution influenced the early compressive strength of cement mortars. Sieving increased the observed loss-on-ignition values of activated fly ash. These results might indicate that a substantial amount of unburnt carbon is in form of particles smaller than 125 \(\upmu\)m and might need to be removed by other means of activation to further enhance the properties of fly ash in the context of its utilization in concrete production. The amount of unburned carbon and the increase in the specific surface can explain the difference in which FA(B)63 fly ash influenced the fresh consistency of cement mortar. It is important to note that loss-on-ignition measurements cannot give a precise estimate of unburned carbon in other types of fly ash than siliceous fly ash [38]. The results may overestimate the amount of organic carbon due to the fact that ignition loss of mass is also due to reactions such as the calcination of carbonates, the dehydration of portlandite, the removal of physically bound water, and the oxidation of sulfur and iron minerals [39]. Most of these compounds were detected by XRD studies of the fly ash (Figure 1).
## 3 Related Work
### Privacy-Preserving Deep Learning (PPDL)
#### 3.1.4 Works Based on Secure Multiparty Computation  
Multi-party computing (MPC) protocols use cryptographic techniques including homomorphic encryption, secret sharing, and oblivious transmission to allow participants to jointly compute a function over their private inputs [53]. Traditional secure computation protocols are not easily scalable for Machine Learning (ML) applications. Therefore, many improvements have been proposed to make them suitable for training and inferencing in ML models [12; 54; 55]. However, the main drawback of MPC-based frameworks is the communication cost and computation overhead. Therefore, recent works [56; 57; 58] recommended utilizing graphics processing units (GPUs) to accelerate cryptographic operations and MPC implementation.  
However, a recent work by [PERSON] et al. [59] doubts the privacy guarantees in multi-party computation protocols. [PERSON] et al. demonstrated that adversaries could increase information leakage by modifying their input even if they honestly follow the protocols. Therefore, the MPC-based frameworks fail to recognize all adversary threats in collaborative learning scenarios [59]. Moreover, combining MPC with other cryptographic primitives like differential privacy has issues such as limitations of both techniques will be applied to the hybrid framework, and the suitability of these mixed frameworks is a concern in their applications since they involve more complex assumptions and hence need more security investigation [60].  
In contrast, our work eliminates these costs and overhead issues by sending a reduced subset of the encodings without too many privacy concerns. Furthermore, the RBM's generation of the encodings has less computation overhead compared to cryptographic approaches like MPC and HE. Also, Applying the Instance Reduction Technique reduces training time.
# Extending Battery Lifetime by Avoiding High SOC
## 4 Drive Cycle and Drive Cases
### Drive Cycle Study  
Table 5 displays the results from the vehicle model while running the different drive cycles with the different vehicles.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameters Case** & **DOD (\%)** & **C-Rate** & \(\mathbf{T}_{\text{hirt}}\) (\({}^{\circ}\)C) & \(\mathbf{SOC}_{init}\) **(\%)** \\ \hline S1 & 10 & \(0.5,1,2\) & 30 & 30 \\ S2 & 10 & \(0.5,1,2\) & 30 & 50 \\ S3 & 10 & \(0.5,1,2\) & 30 & 90 \\ S4 & 20 & 1 & 30 & 30 \\ S5 & 20 & 1 & 30 & 50 \\ S6 & 20 & 1 & 30 & 90 \\ S7 & 50 & 1 & 30 & 50 \\ S8 & 50 & 1 & 30 & 90 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Synthetic drive cases at 30 \({}^{\circ}\)C in various SOC, Depth of Discharge (DOD) and C-rates.  
Figure 7: The Worldwide Harmonized Light Vehicles Test Cycle (WLTC) drive cycle for Class 3b vehicles used to estimate the energy consumption [13].  
Based on these results, the three vehicles were tested to see how long lifetime they would achieve if only running full discharges, i.e., 90% DOD, for the three different drive cycles. Five full discharges/charges during one week (4.5 FCE/week) and a battery temperature of 30 \({}^{\circ}\)C were assumed.  
The vehicles will reach different distances where Vehicle 3 only has a short fully electric drive range. It should be mentioned that, in a real case, the hybrid mode would probably be used in the highway driving, unless a choice for pure electric mode exists. Still, for the sake of presenting the impact on battery ageing, we will assume only fully electric of a fully internal combustion engine (ICE) mode for this vehicle.
## 6 Simulations Results
### Effect of SNR and Epsilon on Success Rate and RMSEs  
The simulation conditions were the same as in Section 6.1. Among them, the SNR changed from \(-10\) dB to \(10\) dB, and the step size was \(1\) dB. The epsilons were \(0.1\), \(0.05\), and \(0.01\). The relationship curve between success rate and SNR and epsilon is shown in Figure 12, and the relationship curve between RMSEs and SNR and epsilon is shown in Figure 13. With the increase in SNR, the success rate increased and the RMSEs decreased. In the case of high SNR, the smaller the cross terms extraction parameter in A9, the higher the success rate and the smaller the RMSEs. At low SNR, the cross terms extraction parameter in A9 had little effect on success rate and RMSEs. In other algorithms, the cross terms extraction parameters had little effect on the success rate and RMSEs. A9 with small cross terms extraction parameters was the overall best, with a high success rate and small RMSEs.  
Figure 11: The relationship curve between RMSEs and snapshots number.  
Figure 12: The relationship curve between success rate and SNR and epsilon.  
Figure 10: The relationship curve between success rate and snapshots number.
# Intrusion Detection and Prevention in CoAP Wireless Sensor Networks Using Anomaly Detection
## 1 Introduction  
It is a well known fact that most of the applications envisioned for the IoT will be supported (at least partially) by sensing and actuating devices which are constrained in terms of the available energy, memory and computational performance. The integration of such devices with the Internet communications infrastructure will enable end-to-end communications with other devices, anywhere on the Internet, but also new avenues for attacks from such less resource-constrained hosts. The aforementioned integration scenario is becoming a reality, thanks to the design and adoption of a standardized communications stack being designed for the IoT [1]. This stack is enabled by protocols such as 6 LoWPAN (6 LoWPAN adaptation layer) [2], Routing Protocol Layer (RPL) [3] and CoAP Constrained Application Protocol (COAP) [4]. We verify that there is currently a lack of proposals in the literature focusing on the specificities of detecting and dealing with attacks against the security and stability of 6 LoWPAN and CoAP devices and communication environments. With this motivation in mind, in this article we propose an anomaly-based intrusion detection and prevention framework for Internet-integrated CoAP sensor networks, in the context which we implement and experimentallyevaluate the effectiveness of complementary anomaly-based intrusion detection techniques in dealing with DoS attacks and attacks against the operations of the 6 LoWPAN and CoAP protocols.  
In our experimental methodology, we start by programming CoAP intrusion (attack) scenarios using the Contiki operating system [5] via the IoT-LAB [6] platform. The traffic generated during the experiments is used to apply feature extraction and complementary machine-learning approaches, validating the models built on top of the classification algorithms employed. We thus analyze the traffic patterns employing 6 LoWPAN and CoAP communications, with the goal of training a machine learning algorithm to detect abnormal or suspicious communications. Our article proceeds as follows. In Section 2 we identify the challenges of intrusion detection and prevention in the context of IoT communication environments, and observe the current shortage of solutions designed for Internet-integrated 6 LoWPAN and CoAP communication environments. In Section 3 we present the proposed framework for intrusion detection and prevention, and the techniques employed in our work to detect anomalies. In Section 4 we analyze the results obtained from the experimental evaluation of the proposed approach, and in Section 5 we conclude the article and discuss future research work in this area.
# MB-OFDM-UWB Based Wireless Multimedia Sensor Networks for Underground Coalmine: A Survey
## 3 Key Supporting Technologies for the WMSNs  
The overall design of the WMSNs for underground coalmine is introduced in the previous section and the main focus is on the modulation scheme in physical layer. As for the WMSNs, there are some technologies which are also very important for its performance in underground coalmine. These technologies are called key supporting techniques, mainly including coverage-enhancing algorithms, media access control (MAC), routing mechanism, cross-layer optimization, and video encoding. All these key supporting techniques have been discussed to construct an integrated monitoring system based on the WMSNs for underground coalmine. Before further discussion of each section, a block diagram organized in layer-specific structure and giving the main research issues for coalmines is shown in Figure 4.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Name** & **Interference** & **Robustness to Multipath** & **Performance** & **Complexity** & **Achievable Range-Date Rate** \\ \hline Multiband & MB-OFDM & ++ & ++ & ++ & + & ++ \\ IR & DS-UWB & ++ & ++ & ++ & ++ & ++ \\ IR & TH-UWB & ++ & ++ & ++ & ++ & ++ \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison summary of UWB techniques.
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Device**} & **Gap Length** & **Volume** & **H\({}_{2}\)O\({}_{2}\)** & **NO\({}_{2}\)** & **Medium** & **Ref.** \\  & **(mm)** & **(\(\mu\)L)** & **(\(\mu\)M)** & **(\(\mu\)M)** & **(\(\mu\)M)** & **Medium** & **Ref.** \\ \hline Ar 60 Hz 7 kV NEAPP & 13 & 3000 & \(17\pm 3\) & \(642\pm 10\) & DMEM 65796 & [43] \\ \hline Ar 60 Hz 2-6 kVNEAPP & 3 & 6000 & \(227\pm 42\) & 265 & DMEM 65796 & [44] \\ \hline Ar 1 MHz 2-6 kV & & & & & & RPMI + FBS(8\%) + & \\ plasma jet (kinpen & 9 & 5000 & 33 & n.d. & P/S(1\%) & [45] \\ GmbH) & & & & & & \\ \hline Ar 1.1 MHz plasma jet & 5 from the top of the & & & & & \\ (kinpen 09 GmbH) & 5 from the top of the & & & & & \\  & well & & & & & \\ \hline Air 5 kHz 2.7 5 kV Liquid & & & & & & \\ surface & 1,5 & 1000 & 87 & n.d. & MEM + FBS (10\%) + P/S & [47] \\ \hline He + 0.3\%/02 13.56 MHz & 20 & 1500 & n.a. & \(7.4\pm 1\) & MEM + FBS 10\% & [48] \\ \hline Ar 60 Hz AC 7 kV & 13 & 3000 & \(1.8\times 10^{4}\) & \(6.3\times 10^{5}\) & DMEM 65796 + P/S + 10\% & [43] \\ \hline Ar 60 Hz AC 7 kV & 13 & 3000 & \(2.1\times 10^{4}\) & \(6.3\times 10^{5}\) & DMEM 65796 + 10\% FBS & [43] \\ \hline He 30 KHz 3.85 kV & 30 & 30 & 50 & n.a. & DMEM 611965-118 & [49] \\ \hline He 24 kKz 12 kV & 5 & 2000 & \(2.3\times 10^{3}\pm 240\) & \(55\pm 9\) & DMEM + 15\% FBS & [50] \\ \hline Ar 2 kV 1.1 MHz & n.d. & 100 & 60 & 22 & RPMI #1640 + 10\% FBS & [51] \\ \hline Ar 2 kV 1.1 MHz & n.d. & 100 & 60 & 31 & RPMI #1640 & [51] \\ \hline He + H2O 10 KHz 8 kV & 1500 & 300 & 50–60 & n.d. & RPMI #1640 & [52] \\ \hline Ar 10 kHz 10 kV & 1500 & 300 & \textless{}20 & n.d. & RPMI #1640 & [52] \\ \hline \multirow{2}{*}{He 5 kHz 5-9 kV} & \multirow{2}{*}{2500 from the bottom of the well} & \multirow{2}{*}{500} & \multirow{2}{*}{28} & n.d. & MEM & [53] \\ \cline{2-2} \cline{5-5}  & & of the well & & & & \\ \hline Air 1 kHz 4 kV & 2 & 200 & \(8.4\times 10^{3}\pm 2.7\times 10^{3}\) & \(873\pm 15\) & RPMI \#1640 + 5\% FBS & Author’s \\ \hline Air 1 kHz 4 kV & 2 & 200 & \(1.9\times 10^{4}\pm 0.3\times 10^{4}\) & \(414\pm 47\) & RPMI \#1640 + 15\% FBS & Author’s \\ \hline Air 1 kHz 4 kV & 2 & 200 & \(3.9\times 10^{3}\pm 1.3\times 10^{3}\) & \(929\pm 24\) & DMEM \#5648 + 5\% FBS & Author’s \\ \hline Air 1 kHz 4 kV & 2 & 200 & \(2.4\times 10^{3}\pm 1.3\times 10^{3}\) & \(742\pm 82\) & DMEM \#5648 + 15\% FBS & Author’s \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Concentration of H\({}_{2}\)O\({}_{2}\) and NO\({}_{2}\) generated by 60 s plasma compared with other plasma sources.
# Research on the Applicability of Transformer Model in Remote-Sensing Image Segmentation
## 4 Discussion  
In the Potsdam data set experiment, SwinUnet was more suitable for the feature-segmentation extraction of large-scale remote-sensing images, while TransUnet performed relatively poorly. The overall accuracy performance of TransUnet was inferior to the other models. This is because the whole network structure of SwinUnet is built using a transformer, which can improve the interaction ability of the global semantic information of the model. Moreover, the fusion of features at different scales by jump linking enhances the model's ability to segment predictions at the pixel level [42]. Using the Vaihigen data set experiment, TransUnet not only had the best overall accuracy performance but also had the highest accuracy for different features. This is because the encoder constructed by the transformer of SwinUnet is still inferior to CNN for small-scale image feature extraction in the small-scale case. However, TransUnet is a combination of CNN and a transformer that enhances the transformer and accelerates its convergence by using appropriate convolutional bias to obtain more local feature information. Thus, TransUnet has better segmentation results in the Vaihingen data set and better segmentation details and contours for features [43]. Therefore, before selecting a transformer, it should be considered according to the remote-sensing image scale. SwinUnet was preferential for large-scale images and TransUnet for smaller-scale images, while SETRnet was not suitable as a remote-sensing image segmentation network. Meanwhile, the comparison experiment between a transformer and a CNN proves that a transformer is inferior to a CNN for the segmentation of edge features. However, it is significantly better than a CNN for large-scale feature segmentation. This situation may be related to the fact that a transformer itself focuses too much on the global features, resulting in ignoring some edge features.
###### Abstract  
_Research highlights_: Estimates of fine root production using ingrowth cores are strongly influenced by decomposed roots in the cores during the incubation period and should be accounted for when calculating fine root production (FRP). _Background and Objectives_: The ingrowth core method is often used to estimate fine root production; however, decomposed roots are often overlooked in estimates of FRP. Uncertainty remains on how long ingrowth cores should be installed and how FRP should be calculated in tropical forests. Here, we aimed to estimate FRP by taking decomposed fine roots into consideration. Specifically, we compared FRP estimates at different sampling intervals and using different calculation methods in a tropical rainforest in Borneo. _Materials and Methods_: Ingrowth cores were installed with root litter bags and collected after 3, 6, 12 and 24 months. FRP was estimated based on (1) the difference in biomass at different sampling times (differential method) and (2) sampled biomass at just one sampling time (simple method). _Results_: Using the differential method, FRP was estimated at 447.4 \(\pm\) 67.4 g m\({}^{-2}\) year\({}^{-1}\) after 12 months, with decomposed fine roots accounting for 25% of FRP. Using the simple method, FRP was slightly higher than that in the differential method after 12 months (516.3 \(\pm\) 45.0 g m\({}^{-2}\) year\({}^{-1}\)). FRP estimates for both calculation methods using data obtained in the first half of the year were much higher than those using data after 12-months of installation, because of the rapid increase in fine root biomass and necromass after installation. _Conclusions_: Therefore, FRP estimates vary with the timing of sampling, calculation method and presence of decomposed roots. Overall, the ratio of net primary production (NPP) of fine roots to total NPP in this study was higher than that previously reported in the Neotropics, indicating high belowground carbon allocation in this forest.  
biomass; calculation method; necromass; NPP; production; sampling interval +
Footnote †: journal: Physics Letters B
## 4 Improved Bat Algorithm for Resolving DRT
### Update Rules for Speed and Location of Bats  
In the hybrid BA, each bat \(i\) updates itself at time \(t\) by tracking the global optimum, resulting in a halt in the search of the rest of the solution space if all the bats gather in the same location. Drawing from the GSO's idea that \"a small number of out-of-group rogues randomly walk\", some bats randomly generate a head angle and distance, before exchanging part of their vector positions to traverse the solution space to maintain the diversity of groups. The updated formulas for the position \(X_{i}^{t}\) and speed \(V_{i}^{t}\) of each bat \(i\) at the time \(t\) areas follows:  
\[V_{i}^{t}=V_{i}^{t-1}+(X_{i}^{t-1}-X^{*})f_{i} \tag{16}\]  
\[X_{i}^{t}=\left\{\begin{array}{l}X_{i}^{t-1}+V_{i}^{t},others\\ X_{i}^{t-1}+l_{i}D_{i}^{t}(\varphi^{t+1}),rand>\sigma\end{array}\right. \tag{17}\]  
where \(f_{i}=f_{\min}+(f_{\max}-f_{\min})\beta\), for \(\beta\in[0,1]\) the echo frequency of each bat \(i\); \(X^{*}\) denotes the current global optimal solution; \(l_{i}\) is a random distance between \(0\) and the maximum search distance \(l_{\max}\); \(\varphi^{t+1}\) is a random head angle between \(0\) and the maximum search angle \(\theta_{\max}\); \(D_{i}^{t}(\varphi^{t+1})\) denotes the vector corresponding to this angle; and \(\varepsilon\), \(\beta\), and \(rand\) are uniformly distributed random numbers in the interval of \([0,1]\).
## 3 Case Study  
The Barreda micro-hydropower plant is located in the town of Torrelavega in northern Spain (latitude 43\({}^{\circ}\)21\({}^{\prime}\)11\({}^{\prime\prime}\) N, longitude 4\({}^{\circ}\)2\({}^{\prime}\)45\({}^{\prime\prime}\) W, and altitude of 35 (m)). The project is led by the Spanish company SinFin Energy [28]. The River Saja feeds this micro hydropower plant. Only a part of the river flow can be used for the Barreda micro-hydropower plant, and this changes with the season and the river flow itself. The nominal flow of the plant is defined in the water permit as 5 (m\({}^{3}\)/s). However, this is only the case in the winter months. The flow is lower throughout the rest of the year. The nominal Barreda micro-hydropower plant flow of 5 (m\({}^{3}\)/s), in combination with a nominal head of 1960 (mm), requires the use of two _ASTs_. Thus, the Barreda micro-hydropower plant has two identical units, each incorporating an _AST_, a speed multiplier (gearbox), an induction generator, and a back-to-back dual _PWM_ converter. The history of the actual hydrological conditions indicates that power higher than the rated power of the _AST_ is almost never obtained as the actual flow rate almost never exceeds the rated value of 5 (m\({}^{3}\)/s). Although the Barreda micro-hydropower plant features two identical units, there may be a scenario where both units do not operate simultaneously. In other words, only one unit or both units in parallel may be operated depending on the available flow. Figure 6 shows an aerial photograph of the plant. The most important data on the individual units, as extracted from the manufacturer's specifications, are shown below.  
It is a 70 (kW) grid-connected run-of-river-type micro-hydropower plant with no dam or water storage. This plant uses two groups (Archimedean screw turbines) [28]. The micro-hydropower plant uses two 35 (kW) Archimedes screw turbines, specifically designed for the project, with the following characteristics: a radius (diameter) of the screw's outer cylinder of 1232.03 (mm), a radius (diameter) of the screw's inner cylinder of 660 (mm), a total length of 5338.93 (mm), a screw turbine angle of 22 (\({}^{\circ}\)), and three blades with a pitch of 4247.74 (mm) each. The design flow is 5 (m\({}^{3}\)/s). The rated speed of the _AST_ is 27.46 (rpm), and therefore, a gearbox (see Figure 7a) (Nidec, model Geared Motors 3000 Range [35]) with a ratio of 1:50.5 is needed to adjust the speed of the _AST_ to the speed of the electric generator. There are two 45 (kW) electric generators (see Figure 7a) (Nidec, model 4 PLSES225 MR [36]), one of which was specifically designed for the turbine. The output voltage of this electric generator is 400 (V). There are two generator-side converters (see Figure 7b) (Nidec Unidrive M701, model M701-074-00770 [36]) and one grid-side converter (see Figure 7b) (Nidec, model M700-094-02000 [36]). The _PLC_ used is from the manufacturer  
Figure 6: Aerial photograph of the micro-hydropower plant.  
Eaton and the model is PLC Eaton XV-300 HMI-PLC [37]. Figure 7 shows the different elements of the Barreda micro-hydropower plant.