esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
Results of application of the conventional fixed amplitude threshold-based algorithm, STA/LTA, FilterPicker [57] and the proposed \(\phi\)-based algorithm for signal detection in the generated small and large test streams are summarised in Tables 3 and 4, respectively. As expected, all methods show excellent reliability in the detection of signals with high SNR \(>>\) 0 (noise levels 1 and 2), which is compromised as the SNR is reduced. As expected, both the STA/LTA and FilterPicker algorithms appear more sensitive and flexible than the fixed threshold triggering. One can see that the STA/LTA trigger significantly increases the percentage of successfully detected weak events compared to the classical amplitude threshold method in both streams, while the number of false alarms is kept nearly the same for both methods. Not surprisingly that the \(\phi\)-algorithm is as efficient as the others, provided the conditions are favourable for signal dist \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Noise Level**} & \multicolumn{2}{c}{**Amplitude Threshold**} & **STA/LTA** & \(\Phi\)**-Algorithm** & **FilterPicker** \\ \cline{2-5}  & \multicolumn{4}{c}{**True Alarms/False Alarms**} \\ \hline
1 & 1000/3 & 986/19 & 1000/0 & 988/11 \\
2 & 926/55 & 975/51 & 977/19 & 925/72 \\
3 & 570/49 & 888/48 & 919/51 & 832/256 \\
4 & 314/54 & 674/53 & 780/35 & 542/67 \\
5 & 186/37 & 431/59 & 630/38 & 309/36 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of the effectiveness of three detection algorithms on the long stream from Figure 5 (1000 events). Figure 5: Example showing the results of signal detection by the \(\phi\)-algorithm applied to the dataset with the largest noise level (5 in Figure 3). One can notice that the signals 4 and 5 are missing while the others are correctly detected, yet no false alarms are triggered. reasonably high. However, the \(\phi\)-triggering substantially outperforms all other techniques at low SNR where the threshold method has completely failed to detect any weak signals at the noise levels 4 and 5 (\(SNR\leq-4.2\) dB), c.f. Table 3 and Figures 5 and 6. Figure 5 exemplifies the results of signal detection by the \(\phi\)-algorithm applied to the small dataset with the lowest SNR corresponding to the largest noise level 5 in Figure 3. One can notice that the signals 4 and 5 are missing while the others are correctly detected, yet no false alarms are triggered. Thus, the detection reliability of the \(\phi\)-triggering method is enhanced compared to other tested methods: the rate of correctly identified events is remarkably higher while the rate of false alarms is consistently smaller than that for other signal detectors at any SNR, including the lowest one as illustrated in the bar chart in Figure 6 comparing the correct detection rates for all tested methods.
## 5 Results  
### Segmentation Performance  
Figure 3 shows four examples of segmentation on T1 wCE images and the mapped results on the derived ADC maps.  
For quantitative evaluation of the tumor segmentation mapping results, we randomly selected 31 subjects' baseline data. The 31 tumors are from an ADC mapping database, 20 of which have different image resolutions between the T1 wCE and ADC images in all three dimensions and 11 of which have exactly the same 3D image resolution in both modalities. We calculated the overlap ratio between the mapped ROI generated automatically by the tool and an ROI corrected by a neuro-radiologist. The overlap ratio (OR) is defined by Equation 6, where A and B are two tumor ROIs and size(.) is the number of voxels in that ROI.  
\[2*size(A\bigcap B)/(size(A)+size(B)) \tag{7}\]  
The results are shown in Table 3 with 20 out of 31 ROIs (64.5%) have an overlap ratio over 90%.  
\begin{table}
\begin{tabular}{l l l l l l l} \hline
**Overlap ratio** & 100\% & 95\(\sim\)100\% & 90\(\sim\)95\% & 80\(\sim\)90\% & 60\(\sim\)80\% & 0\(\sim\)60\% \\ \hline
**Number of patients** & 10 & 7 & 3 & 2 & 5 & 4 \\ \hline \end{tabular}
\end{table}
Table 3: Distribution of overlap ratios.  
_Algorithms_**2009**, 2  
Figure 3: **(a)-(d)** and **(i)-(l)** show four examples of tumor segmentations on T1 wCE images; **(e)-(h)** and **(m)-(p)** show the corresponding mapped tumor contours on ADC maps.
# Comparison of Heuristic Algorithms in Discrete Search and Surveillance Tasks Using Aerial Swarms
## 5 Comparison of the Algorithms  
In order to compare all the search algorithms considered in this work, random scenarios are generated considering the parameters (and their valid ranges) shown in Table 1 for each of the four  
Figure 12: Example of billiard random movement behavior. When an agent encounters a boundary, it redirects its heading randomly. Otherwise, the course is kept constant (unless a collision may occur).  
Figure 13: Example of the lanes following behavior. In the first lane assigned to the yellow agent, it travels first to the closest extreme cell. When each lane is completely observed, a new one is assigned.  
scenario types. Recall that each scenario is defined by the area per agent, \(A_{s}/N_{a}\), the number of agents, \(N_{a}\), the nominal velocity of the robots, \(v_{n}\), the sensor footprint radius, \(R_{f}\), and the aspect ratio of the area, \(f_{a}\). For each scenario, 100 different initial conditions are generated, and each algorithm is tested having considered them. The mean values of the efficiencies as per Models 1, 2 and 3 (see Equations (10), (11) and (13)) are calculated and considering the standard deviations among the 100 trials, the fitness value is obtained according to Equation (14). For this last figure, whether the search has been completed or not is also taken into account.  
First, we carry out a quantitative comparison based on the measurement of the efficiency and fitness values for each scenario type. Secondly, we present a qualitative analysis discussing the complexity of the communications needed and how appropriate each algorithm could be for a surveillance mission.
## 5 Experiments and Results
### Comparison Algorithms  
For policy-based algorithm comparison against the G-MAPPO, we choose COMA [45], MAPPO [32], HAPPO [28], MAT [46], and HetGPPO [24] as baselines. For value-based algorithm comparison against G-QMIX, vanilla QMIX [31], ROMA [49], RODE [48], CDS [55], ASN [30], UNMAS [50] and GHQ [27] are used to conduct experiments.  
In general, all of the aforementioned policy-based algorithms, except HetGPPO, are actor-critic algorithms using the \"centralized critic decentralized actor\" (CCDA) architecture, which means that the state \(s\) is only available for the critic network to estimate a proper value function \(V(s)\), and the actor network is only capable of using the observation-action trajectory \(\tau\) to generate the policy function \(\pi(a|\tau)\). COMA and MAPPO use a shared actor network for all agents across heterogeneous agent types. HAPPO uses independent actor network parameters and proposes a theoretically monotonic policy-improving architecture with a sequential decision-making scheme. MAT further modifies the network architecture of HAPPO into the Transformer style, and considers the MARL problem as a sequential decision-making problem to solve. On the contrary, HetGPPO is based on the independent PPO (IPPO) [61] using the \"decentralized training decentralized execution\" (DTDE) architecture. HetGPPO uses different parameters for different groups, and each group contains a translation-invariant message-passing Graph Neural Network for aggregating messages from other groups.  
QMIX is a fundamental value-based MARL algorithm. Its introduction of the monotonic value factorization architecture has been proven to be powerful yet efficient in the SMAC environment. RODE and ROMA are both _role-based_ algorithms, which learn and apply role policies end to end. The default number of training steps of ROMA is 20M, and thus, it generally performs badly within our 5 M training step restriction. In RODE, several key hyper-parameters define the generation and usage of role policies, which require extra fine-tuning and restrict the performance of RODE. CDS introduces diversity into the optimization and representation of QMIX to overcome the shortage of global parameter sharing. ASN considers the action semantics of different agents and divides the network to represent and leverage the semantics. UNMAS is capable of adapting to changes in the number and size of MAS with the self-weighting mixing network. GHQ divides agents into different groups using the IOG method and optimizes the networks of these groups with hybrid factorization and IGMI loss.  
Generally, due to the discrete action space for both SMAC and MPE, value-based algorithms perform better than policy-based algorithms [27; 31; 32; 62], which means that value-based methods usually require fewer training steps to achieve similar _WR_ or _return_ than policy-based methods. Therefore, for a fair comparison and better performance, the number of training steps for policy-based algorithms is 10 M for SMAC and 5 M for MPE.
# EBR: Routing Protocol to Detect Blackhole Attacks in Mobile Ad Hoc Networks
## 1 Introduction
Also, EBR can detect the congestion status of a route using the RTT. We call this combination the TTL/RTT (TR) algorithm. In the EBR protocol, each node can send an RREQ test message to a dummy destination node. If the receiving node(s) reply with an RREP message within a limited RTT, this will be considered as a blackhole node, and the confidence level will be low and, therefore, the route will not be selected. Otherwise, if the RTT is long, the testing node will assume that the route has an additional blackhole attacker in one or more of its nodes, and, also, these intermediate nodes of such route might be congested. TTL should have a small value to only examine the neighboring nodes and also to dodge immersing the network in unwanted routing overhead traffic. In this regard, our contributions in this paper are:  
* Propose TR algorithm utilizing TTL and RTT;
* Propose EBR protocol based on TR algorithm. EBR enhances the network performance. EBR is able to do the following main functions:
* Detect blackhole attacker(s) in a transmission route,
* Detect data congestion at intermediate nodes in a transmission route;
* Filter out routes returned by AOMDV to avoid blackhole attackers and congested nodes;
* Compare our EBR protocol with other comparative methods through simulation. The structure of this paper is outlined as follows: Section 2 presents a literature review. Section 3 outlines the proposed protocol. Section 4 presents the simulation results. Section 5 elaborates the protocol complexity. Section 6 summarizes the conclusions.
## 1 Introduction
These advanced methods remain underexplored regarding their effectiveness in extracting tomato seed oil and their impacts on its bioactive composition. [PERSON] et al. (2023) used UAE-MAE with ethanol and ethyl acetate, enhancing the extraction efficiency and yielding 23.07% oil [20]. [PERSON] et al. (2010) used accelerated solvent extraction with hexane, obtaining a 20% oil yield, though without providing data on the resulting oil's fatty acid content [21]. Overripe and damaged tomatoes, along with tomato juice by-products from processing, can be repurposed into high-value functional products. Vinegar has demonstrated beneficial effects on hyperglycemia and hypertension, as well as reducing body weight, fat mass, and triglyceride levels [22]. It is traditionally produced from sugar- or starch-rich raw materials such as rice, malt, apples, and grapes via a two-stage fermentation process, where ethanol is produced by yeast and then converted into acetic acid by acetic acid bacteria [23]. Growing health concerns have increased the demand for vinegar from diverse fruits and vegetables, leading to novel vinegar varieties made from onion, pineapple, strawberry, and sweet potato [24] [PERSON] et al. (2022) studied tomato vinegar production, achieving an acetic acid concentration of 4.12% (\(w/v\)) within 3-5 days of fermentation at 36 \({}^{\circ}\)C [24]. This study aimed to valorize tomato by-products through innovative extraction methods and bioprocessing strategies. The synergistic application of UAE-MAE was employed to recover lycopene from tomato peels using ethyl lactate as the solvent. Extraction parameters, including solid/liquid ratio, microwave power, and ultrasound power were optimized using the response surface methodology (RSM) to maximize the extraction yield. The recovery of tomato seed oil was also investigated through various extraction methods, including Soxhlet extraction, conventional stirring, UAE-MAE, and PLE. The final oil product was analyzed for yield, carotenoid content, and unsaturated fatty acid composition using HPLC and GC analyses, respectively. Lastly, the conversion of tomato juice into vinegar through a two-stage fermentation process was studied. The final tomato vinegar product was characterized for its lycopene content, acetic acid concentration, ethanol concentration, and pH. This comprehensive approach highlights the potential of innovative extraction and bioprocessing techniques to enhance the value of tomato by-products, promoting sustainability in the food industry.
## 2 Materials and Methods
### Methodology
#### 2.3.2 Maximum Value Sampling  
Due to the randomness of wind in nature and its physical generation process's autocorrelation over long time scales, assume a set of continuous random variables \(Y_{1}\), \(Y_{2}\), \(\ldots\), \(Y_{n}\) representing wind speeds at different time points. Their distribution may change over time due to seasonal variations. In this context, maximum value sampling is introduced to handle the complexity.  
Under the maximum value sampling framework, the data are divided into consecutive blocks (each block representing a month or a day), each containing all wind speed values \(Y_{i}\) within that period. Define \(V_{\max}\) as the maximum wind speed in each block:  
\[V_{max}=max(Y_{1},Y_{2},\ldots,Y_{n}) \tag{2}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \(\mathbf{Y_{n}}\) & \(\mathbf{T_{n}}\) & \(\mathbf{M_{n}}\) & \(\boldsymbol{p\) (\%)} \\ \hline
1991 & 88,575 & 3066 & 96.5 \\
1995 & 109,215 & 3348 & 96.9 \\
2000 & 123,000 & 3003 & 97.6 \\
2005 & 122,040 & 3442 & 97.2 \\
2010 & 123,075 & 2366 & 98.1 \\
2015 & 129,885 & 2092 & 98.4 \\
2020 & 129,855 & 3294 & 97.5 \\ \hline \hline \end{tabular} \(Y_{n}\)—number of years; \(T_{n}\)—total number of data; \(\mathbf{M_{n}}\)—total number of missing values; \(p\)—percentage of missing values.  
\end{table}
Table 1: Remaining missing values and total data counts after processing.  
Figure 3: Comparative analysis of wind speeds at 100 m: 1991–2000 before and after processing. (a) Raw wind speed data. (b) Wind speed data after outlier removal.  
At this time, the distribution of \(V_{max}\) will be influenced by the distribution characteristics of \(Y_{i}\). This method allows us to capture the strongest wind speeds that might be encountered in each period, while considering the autocorrelation and distribution changes of wind speed data within these periods. Although the original \(Y_{i}\) variables may follow different distributions and exhibit time dependence, we do not directly assume that the original wind speed data are independently and identically distributed. Instead, using the block sampling method, we focus on the extreme values \(V_{max}\) within each period, which then allows us to apply extreme value theory for subsequent analysis.
# Estimating Position from Millisecond Samples of GPS Signals (the \"FastFix\" Algorithm)
## 1 Introduction
### Alternative Algorithms  
Advances in algorithms provide another technique to reduce power consumption, and several alternative approaches to the traditional positioning algorithm have been developed. Of these, the most widely used is Assisted GPS (A-GPS). This employs satellite ephemeris data, and estimates of position, time to eliminate the need to receive this data via the GNSS signal [14]. This has limitations in tracking applications as a separate telemetry channel is needed for the augmentation data. Time-to-first-fix from a cold start situation, signal sensitivity, and position-accuracy are all improved with respect to a stand-alone GPS receiver [15].  
Another algorithm family estimate position using only millisecond samples of data, with the satellite ephemeris provided externally. This technique is referred to as time-free, or snapshot positioning and was first proposed by [PERSON] et al. in 1995 [16]. This was refined in 2009 [17] to show that positioning was possible using only codephase measurements. [PERSON] et al. [18] was proposed in 2015 that uses an initial approximation of receiver time and position, to reconstruct the transmit time relative to the received time such that pseudorange can be calculated, and position estimated. In 2012, [PERSON] and [PERSON] [19] introduced a positioning method that use a combination of Doppler and code-phase measurements to provide a position estimate unknown initial position. [PERSON] et al. [20] in 2017 introduced an integer ambiguity searching method based on the relationship between the maximum pseudorange difference and the inter-satellite distance. The accuracy possible with these snapshot algorithms is typically \(\sim\)20 m [21].  
This algorithm described in this paper uses Doppler information to provide an a priori position estimate, in a manner similar to [PERSON] [19], and combines a relative-codephase measure, in a manner analogous to the work of [PERSON] et al. [20] who instead use an inter-satellite distance. In practice, the algorithm is able to determine position anywhere on earth from as little as 2 ms of GPS L1-band signal using measurements of Doppler shift and _relative codephase_ recovered from the data. Section 2 describes the well-known process of _acquisition_, and shows that small errors in the knowledge of the local time at the receiver will affect the measurement of codephase; however, this effect is the same for each satellite signal, and introduces relative codephase which is not affected by these errors. Section 3 describes the FastFix algorithm for positioning, and Section 4 presents results for stationary device performance including typical accuracy. Finally, Section 5 presents a real-world application of this algorithm in tracking albatross.
## 1 Introduction
Researchers have proposed models using artificial neural networks (ANN) [19] and Gaussian process regression (GPR) [20] to predict the soil permeability coefficient, achieving promising results. In general, computing-based models are excellent techniques for predicting soil permeability coefficients, and related research outcomes can provide more accurate and scientific methods for predicting the permeability coefficient of artificially improved loess. In summary, it can be seen that the current methods of improvement for loess materials work mainly through the compaction and using admixtures. Methods of improvement of grain size and gradation for excavation to obtain on-site soil materials are relatively rare. Furthermore, the research on the law of the change of the permeability of improved loess and the method of prediction of the permeability coefficient mainly focused on the individual effect of a certain influencing factor; the research on the law of the change of permeability performance under the combined effect of multiple factors, the contributing degree of each influencing factor, and the prediction model of the permeability coefficient under the influence of multiple factors is still relatively lacking. Therefore, in this paper, indoor infiltration tests of improved loess materials were conducted to study the relationship between each influencing factor and the infiltration coefficient, and the degree of influence of each factor on the infiltration coefficient was evaluated by gray correlation analysis, with Malan loess as the research object. Finally, the SVM regression algorithm was used to establish the prediction model of the saturated permeability coefficient of improved loess. Then, the permeability of improved loess materials was evaluated. The research results are of guiding significance for engineering construction in loess areas, especially for developing artificial water-impermeable materials.
## 3 Results and Analysis  
### Thickness and Hardness of Boronizing Layer without Rare Earth  
Table 3 shows the thickness of boronizing layer obtained in experiment for H13 steel under different temperature and time process conditions without adding rare earth (take the average value of 10 points, Hardness value of boronizing layer measured at 40 \(\upmu\)m from the surface of boronizing layer). It can be seen from Tables 3 and 4 that when the boronizing temperature is 950 \({}^{\circ}\)C and the boronizing time is 4 h, the boronizing layer thickness is the largest (42 \(\upmu\)m).  
The hardness at 40 \(\upmu\)m from the surface of the boronizing layer still reaches 1246.90 HV. Therefore, it is concluded that the ideal basic process parameters for boronizing H13 steel are: the boronizing temperature is 950 \({}^{\circ}\)C and the boronizing time is 4 h. This can provide a reference for further b  
\begin{table}
\begin{tabular}{l l} \hline Paste material & B\({}_{4}\)C, KBF\({}_{4}\), C, Bentonite, CeO\({}_{2}\), Cr\({}_{2}\)O\({}_{3}\), H\({}_{2}\)O, etc. \\ Paste thickness & 3–5 mm \\ Pre-treatment & Ventilated environment, 12–14 h \\ Pre-heating & In the drying oven, 200 \({}^{\circ}\)C, 2–3 h \\ Boriding temperature & 850 \({}^{\circ}\)C, 900 \({}^{\circ}\)C, 950 \({}^{\circ}\)C, 1000 \({}^{\circ}\)C, respectively \\ Boriding time & 2 h, 3 h, 4 h, 5 h, respectively \\ CeO\({}_{2}\) content & 0%, 2%, 4%, 6%, respectively \\ Cr\({}_{2}\)O\({}_{3}\) content & 1%, 2%, 3%, 4%, respectively \\ \hline \end{tabular}
\end{table}
Table 2: Experimental parameters.  
\begin{table}
\begin{tabular}{l l l l l} \hline
**Temperature** & **850 \({}^{\circ}\)C** & **900 \({}^{\circ}\)C** & **950 \({}^{\circ}\)C** & **1000 \({}^{\circ}\)C** \\ \hline
2 h & 16 \(\upmu\)m & 23 \(\upmu\)m & 27 \(\upmu\)m & 28 \(\upmu\)m \\ \hline
3 h & 19 \(\upmu\)m & 27 \(\upmu\)m & 32 \(\upmu\)m & 31 \(\upmu\)m \\ \hline
4 h & 20 \(\upmu\)m & 36 \(\upmu\)m & 42 \(\upmu\)m & 42 \(\upmu\)m \\ \hline
5 h & 21 \(\upmu\)m & 35 \(\upmu\)m & 40 \(\upmu\)m & 41 \(\upmu\)m \\ \hline \end{tabular}
\end{table}
Table 3: Boronizing layer thickness at different temperature and time.
## 2 M2M Service Networks
### Related M2M Standardization Approaches  
An analysis of some M2M standardization approaches is provided in Table 1. At least a tiny IP stack is required in the end-to-end Internet based approach to enable connectivity for small devices. If such a stack is available, then this approach may be possible. However, the challenge is that also the embedded devices which are not necessarily Internet-capable are required to be connected to the Internet. The M2M gateway based approach may enable also their connectivity; however, the challenge may be dynamic behavior of the wireless systems and need to adapt with different kinds of service back-end systems. A number of industry associations have been established to cover some specific category of M2M asset devices, such as electronic product codes, generic identification numbering, RFID tags, video devices, electricity, gas, water and heat meters and different kind of sensors (e.g., Geospatial) and devices for specific applications (e.g., medical devices). The capability of user interface to adapt to the dynamic behavior of wireless devices has been a challenge. UIML type of approach seems to be too heavy to be applied with the UIs of embeddedM2M asset devices. It is also possible to apply the natural need of the M2M asset device manufacturers to make drivers and UIs for their products, and connect these SW components into the dynamic configuration and discovery process [20].