esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Methods
### Frequency Domain Phase Correction
As shown in Figure 8, the proposed PLC method first estimates the logarithmic power spectrum and the phase spectrum of speech segment with packet loss through DNN, then converts the logarithmic power spectrum into the amplitude spectrum, and finally corrects the phase spectrum through the GLA to obtain a phase spectrum matching the amplitude spectrum.
# Real-Time Extensive Livestock Monitoring Using LPWAN Smart Wearable and Infrastructure
## 2 Materials and Methods  
### Wearable Design  
The wearable integrates several sensors for measuring temperature, acceleration, and magnetic fields. It also features a GNSS positioning module and a LoRa transceiver able to transmit using FSK and LoRa modulations for communication purposes. In the processing side there is a 64 Mbit NOR flash for storing tracking data, a 16-bit MCU with low-power features and a power management circuitry that allows being powered with any battery chemistry and recharging it from different power sources like solar panels, induction, thermoelectric and piezoelectric (Figure 3[40]).  
The design objectives considered several perspectives, mainly that of the shepherd and that of the animal, as two types of users with different needs. Thus, the design specifications were aimed at ensuring high resistance to shock and water; compatibility with different existing strong attachment methods; ease of attachment and removal; and acceptance of the device by the host. We designed, tested and redesigned various 3D enclosures to verify different formalizations, finishing angles, and manufacturing materials. Finally, we selected a clam design with an o-ring that stays in place with four screws. The enclosure has two slots with different sizes allowing different-sized animals to comfortably wear them (e.g., sheep and cows). We have successfully performed waterproof IP68 testing. Final design can be seen in Figure 4.
## 3 The Proposed Method
### Identifying Redundant Failure Modes  
By treating each of the failure volumes involved as a set, \(G_{i}\) is a redundant failure mode when its corresponding set \(A_{i}\) satisfies  
\[A_{i}\subseteq\mathop{\cup}\limits_{j=1}^{q}A_{j}(i\
eq j,q=1\;\text{or}\;2, \ldots,\text{or}\;m-1) \tag{6}\]  
where \(\cup A_{j}\) denotes the union of the sets corresponding to other failure modes, and \(q\) denotes the number of other failure modes. Figure 2 shows a redundant failure mode in a system with two failure modes, from which the set \(A_{2}\) is a subset of the set \(A_{1}\) and, therefore, the failure mode \(G_{2}\) is a redundant one.  
It is well known that in probabilistic reliability analysis the redundant failure mode may occur when and only when two failure planes are parallel to one another. However, as shown in Figure 2, in non-probabilistic reliability analysis the redundant failure mode may occur when two failure planes are not only in a parallel state but also in an intersecting state because of the limitation of the ellipsoid. This means that the redundant failure mode is more likely to occur in non-probabilistic reliability analysis and, therefore, needs to be further considered.  
The question arises as to how to identify the redundant failure modes in a system. For a practical problem, this is enormously complex and, hence, is a challenging or even impossible issue because of the high dimensionality and multiple failure modes. Considering the feasibility of performing the identification, in the following text a complex structural system with multiple failure modes is decomposed into several simple subsystems with two and three failure modes. For better understanding, we first focus on a subsystem with two failure modes, and then we deal with a subsystem with three failure modes. It should be pointed out that, here, an approximate but effective means instead of a precise one is presented for the identification of redundant failure modes.  
Figure 2: Redundant failure mode in the system.
## 2 Materials and Methods
### Characterization  
To evaluate the dielectric strength of the oil-paper insulation, a test was conducted in accordance with ASTM D149-20 [26], adapted to the specific characteristics of the materials under investigation. Each sample was placed between two electrodes in a test cell filled with insulation oil to replicate realistic transformer operating conditions. An alternating voltage was then applied and gradually increased at a controlled rate until the dielectric breakdown occurred. Each breakdown test was repeated six times per oil type and aging duration to ensure statistical reliability. After breakdown testing, the dielectric constant and dissipation factor of the impregnated paper and oil were measured using a broadband dielectric spectroscopy system (NOVOCONTROL Alpha-A, fabrique par Novocontrol Technologies GmbH & Co. KG, base a Montabaur, en Germany), covering a frequency range from 10\({}^{3}\) Hz to 10\({}^{-3}\) Hz. All measurements were conducted at a constant temperature of 25 \({}^{\circ}\)C. Additionally, attenuated total reflectance Fourier-transform infrared spectroscopy (ATR-FTIR) was used to examine chemical and structural changes in the oil-paper insulation system induced by aging and electrical breakdown. Spectra were acquired using an Agilent Cary 630 spectrometer in transmission mode, spanning the 4000-600 cm\({}^{-1}\) range, with 16 scans per acquisition and a resolution of 2 cm\({}^{-1}\). For semi-quantitative analysis of chemical degradation, the relative intensities of selected FTIR bands were compared by calculating absorbance ratios. The carbonyl band (C=O, -1710-1740 cm\({}^{-1}\)) was normalized against the C-O band (-1030-1060 cm\({}^{-1}\)), which remains relatively stable during aging and serves as an internal reference. Absorbance values were extracted from the spectra using peak maxima, with all measurements performed under consistent baseline correction and acquisition conditions. Figure 1 illustrates the sample preparation procedure and experimental setup.  
Figure 1: Sample pre-treatment process and experimental diagram.
# A Performance Evaluation for Software Defined Networks with P4
## 5 Results and Analysis of Tier 1--Single Type of Traffic Run
### Case Study 2--TCP
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{5}{c}{**a. Network Performance of TCP Transfer in Multi-Path Topology**} \\ \hline \hline  & \multicolumn{2}{c}{**Multi-Path Topology-Single Type of Traffic (TCP)**} \\ \cline{2-4}  & _SDN+OvS_ & _SDN+P4_ & **Improvement** \\ \hline Throughput & \(1.09\times 10^{7}\) bps & \(1.19\times 10^{7}\) bps & \(9.17\%\) \\ \hline Packet Loss & 4\% & 0.75\% & (\(-81.25\%\)) \\ \hline Delay & 0.0041 s & 0.0035 s & (\(-14.6\%\)) \\ \hline Syn Delay & 0.43 ms & 0.096 ms & (\(-77.6\%\)) \\ \hline Data transmitted & 1 GB & 1 GB & N/A \\ \hline Total transmission time & 822.8 s & 752.1 s & (\(-8.5\%\)) \\ \hline \hline \multicolumn{5}{c}{**b. Network Performance of TCP Transfer in Grid Topology**} \\ \hline \hline  & \multicolumn{2}{c}{**Grid Topology-Single Type of Traffic (TCP)**} \\ \cline{2-4}  & _SDN+OvS_ & _SDN+P4_ & **Improvement** \\ \hline Throughput & \(1.05\times 10^{7}\) bps & \(1.175\times 10^{7}\) bps & \(11.9\%\) \\ \hline Packet Loss & 5.9\% & 3.9\% & (\(-33.8\%\)) \\ \hline Delay & 0.06 s & 0.03 s & (\(-50\%\)) \\ \hline Syn Delay & 0.94 ms & 0.05 ms & (\(-94.68\%\)) \\ \hline Data transmitted & 1 GB & 1 GB & N/A \\ \hline Total transmission time & 820.2 s & 746.1 s & (\(-9.0\%\)) \\ \hline \hline \multicolumn{5}{c}{**c. Network Performance of TCP Transfer in Internet Topology**} \\ \hline \hline  & \multicolumn{2}{c}{**Internet Topology-Single Type of Traffic (TCP)**} \\ \cline{2-4}  & _SDN+OvS_ & _SDN+P4_ & **Improvement** \\ \hline Throughput & \(1.04\times 10^{7}\) bps & \(1.169\times 10^{7}\) bps & \(12.4\%\) \\ \hline Packet Loss & 1.8\% & 3.7\% & (\(-51.4\%\)) \\ \hline Delay & 2.87 s & 0.94 s & (\(-67.24\%\)) \\ \hline Syn Delay & 3.89 ms & 1.4 ms & (\(-64.01\%\)) \\ \hline Data transmitted & 1.0796 GB & 1.2106 GB & \(12.13\%\) \\ \hline Total transmission time & 1200 s & 1200 s & N/A \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Network performance for case study 2—TCP traffic. has shown improvements in all three topologies, by 9%, 11% and 12% respectively. Syn delay has also been reduced for SDN+P4, by 77%, 94% and 64%, respectively, for the three topologies. The TCP Syn Delay [74] has been calculated by determining which switch architecture can complete the TCP handshake more efficiently and effectively to establish a TCP connection. Given the nature of the TCP traffic, SYN packets play a crucial rule in establishing a TCP connection. As shown in Table 2, _SDN+P4_ has spent the least amount of time in synchronising the stateful connection between the respective client and server. Having achieved a higher throughput for all cases in _SDN+P4_, total transmission time has been reduced by 8% and 9%, respectively, for multi-path and grid topologies, with a 12% increase on the total data transmitted for the Internet topology. The application of _SDN+P4_ has improved the performance of the network significantly.
# SACT: A New Model of Covert Communication Based on SDN
## 4 System Model
### Anti Detection Module
#### 4.2.2 Mapping Synchronization  
The coding mapping table needs to change constantly. Only when both sides keep the same mapping can the system run normally. In SACT, we use strict clock synchronization based on NTP protocol to solve the synchronization problem of both controllers. The sending and receiving of NTP timestamp is implemented by UDP.  
As shown in Figure 7, the client calculates its time offset and round-trip delay with the server to synchronize the clock. where \(t_{0}\) is the client timestamp when sending the request message, \(t_{1}\) is the server timestamp when receiving the request message, \(t_{2}\) is the server timestamp when sending the response message, \(t_{3}\) is the client timestamp when receiving the response message. Then the round trip delay \(\delta\) and time offset \(\theta\) between client and server are shown in Equations (11) and (12).  
\[\delta =(t_{3}-t_{0})-(t_{2}-t_{1}) \tag{11}\] \[\theta =\frac{(t_{1}-t_{0})+(t_{2}-t_{3})}{2} \tag{12}\]  
The basic process of the module is as follows:  
1. SDN controller sends request message to multiple NTP servers.
2. After receiving the response message, the values of \(\delta\) and \(\theta\) are statistically analyzed through the filter.
3. Removing the outliers and calculating the estimated time offset from the remaining three best candidate values.
4. Setting the system time and provide timestamp for confirming mapping scheme to complete scheme synchronization.
## 4 Fuzzy Logic Model
### Defuzzification  
The defuzzification process converts the fuzzy output back to the crisp or classical output to the control objective, just as fuzzification is the conversion of a precise quantity to a fuzzy quantity. The defuzzification phase compiles the required output by using an accumulation function to combine the endorsements of each rule into the single most certain controller output value using the likelihood of each rule being triggered [6]. Among the available three defuzzification techniques, we used the Mamdani centroid technique, which is the most popular technique, as well as being widely utilized in actual applications. The defuzzification phase generates two outputs _Active mode_ of the sensor nodes and the _ETR_ relay node. The active mode is based on the remaining energy and active time of a sensor node where the selection of the _ETR_ relay node to forward real-time packets is based on the node's remaining energy and velocity. The fuzzy controller uses the following Equations (2)-(4) to calculate the required end-to-end velocity of a packet at each relaying _ETR_ node to achieve real-time service.  
\[V_{ETR_{i}} = \frac{D(ETR_{i},Sink)}{R_{i}(Tx)} \tag{2}\]  
_D(ETR\({}_{i}\), Sink)_ is the Euclidean distance between the _ETR\({}_{i}\)_ and _sink_ nodes. _R\({}_{i(Tx)}\)_ is the amount of time left before the deadline expires at relay node \(i\). The delay occurring between _ETR\({}_{i}\)_ and the next hop _ETR_ relay node _NH\({}_{ETR_{i}}\)_ is represented as:  
\[Delay(ETR_{i},NH_{ETR_{i}}) = LQ(ETR_{i},NH_{ETR_{i}})\] \[\times(T_{i(Ch)}+T_{i(Tx)})\]  
_Delay\((ETR_{i},NH_{ETR_{i}})\)_ represents the one-hop delay that a packet faces in the real-time queue, and _LQ_ is the ratio of the expected number of transmissions successfully delivered and received between relay node _ETR\({}_{i}\)_ and _NH\({}_{ETR_{i}}\)_. The velocity of expected _NH\({}_{ETR_{i}}\)_ is:  
\[V(NH_{ETR_{i}}) = \frac{D(ETR_{i},NH_{ETR_{i}})}{Delay(ETR_{i},NH_{ETR_{i}})}\] \[where\] \[D(ETR_{i},NH_{ETR_{i}})\leq ETR\;value\] \[\&\;\;Delay(ETR_{i},NH_{ETR_{i}})<R_{i(Tx)}\]  
To keep the delay at a minimum, the proposed scheme gives high priority to tight deadline packets in the real-time queue. _RT_ is initially set to be the end-to-end deadline at the source node and decremented at each hop to account for queuing, contention and transmission delays [4]. The power-aware transmission scheme not only improves link quality, but also reduces the number of transmissions needed to deliver a real-time packet.
# Optimized Wireless Sensor Network Architecture for AI-Based Wildfire Detection in Remote Areas
## 3 WSN Architecture
### Routing Algorithms and Applications
#### 3.3.1 Routing in Dense Forests  
In dense forests, network fiers are positioned closely (inter-tier distance \(\leq\) effective sensing radius) to address high environmental obstruction density. This proximity enables adaptive routing algorithms to dynamically reroute data around obstacles (e.g., thick vegetation) through adjacent upper/lower tiers if intra-tier paths are blocked. Critical alerts (e.g., wildfires) bypass blockages via these verticals rerouting, ensuring delivery to the control center. Inter-tier communication enhances resilience by allowing direct data jumps between tiers, eliminating dependency on horizontal paths. In mountainous terrain, nodes adopt a conical configuration around peaks to maximize coverage across elevations while maintaining tiered connectivity. Figure 4 illustrates an example of possible inter-tier adaptive routing in the case of a flat forest.  
Figure 4: (**a**) Example of inter-tier routing in flat forest, (**b**) failed node scenario.  
Figure 3: Examples of mixed forests: (**a**) truncated flat forest, and (**b**) truncated mountainous forest.  
In this example, fires are detected at nodes 8, 10, 14, 15, 17, and 18. The adaptive routing algorithm aims to minimize energy use and avoid unnecessary transmissions. Figure 4a illustrates possible paths for forwarding fire alerts to the sink node. For instance, node 14 sends data to node 5 in the lower tier. When node 5 detects that axis node 1 is busy handling data from node 15 via node 6, it forwards the message to node 12. To reduce latency and prevent data blocking, node 12 routes through node 4, which may also be receiving data from node 10. If nodes 10 and 12 simultaneously request access to node 4, dynamic arbitration decides the priority--e.g., if node 12 arrives first, node 10 is queued. Alternatively, node 5 could wait until axis node 1 becomes free. Adaptive inter-tier routing also proves effective when nodes fail. As shown in Figure 4b, if node 15 detects that node 6 is inactive, it reroutes its data through node 14 to maintain communication continuity.
# Mambo-DQN: Adaptively Tunes Visual SLAM Parameters Based on Historical Observation DQN
## 4 Experiments
### Analysis of Experimental Results
#### 4.2.3 Result3: Memory Usage and Time Performance  
For the purpose of evaluating the efficiency of the method introduced in this article, we carried out comparative experiments focusing on both system memory usage and execution time, using ORB-SLAM3 and DDPG-SLAM methods as comparative baselines.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **VINS-Mono [47]** & **ORB-SLAM3** & **Ours** \\ \hline magistrale1 & 2.19 & 1.13 & **0.81** \\ magistrale2 & 3.11 & 0.63 & **0.49** \\ magistrale3 & **0.40** & 4.89 & 0.98 \\ outdoors1 & 74.96 & 70.79 & **30.62** \\ outdoors2 & 133.46 & 14.98 & **12.92** \\ outdoors3 & 36.99 & 39.63 & **26.87** \\ slides1 & 0.68 & 0.97 & **0.53** \\ slides2 & 0.84 & 1.06 & **0.42** \\ slides3 & 0.69 & 0.69 & **0.43** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Absolute trajectory error of TUM-VI dataset in challenging scenarios. \(RMSE/m\downarrow\).  
Figure 6: Comparison of pose trajectories in some sequences of the TUM-VI dataset. (**a,d**) compare the trajectories of Room3 and Room6, where our method (green) demonstrates reduced drift compared with the baseline method (blue). (**b,e**) focus on the details of orbit alignment, showing that our method provides higher accuracy in key trajectory segments. (**c,f**) present the distribution of absolute attitude errors, indicating that our method maintains lower error levels.  
All tests were conducted on hardware equipped with an NVIDIA 4060 8 GB (NVIDIA Corporation, Santa Clara, CA, USA) graphics card and an Intel(R) Core(TM) i7-10700 [EMAIL_ADDRESS] GHz (NVIDIA Corporation, Santa Clara, CA, USA). The memory usage details are summarized in Table 5.  
In terms of execution time, a comparative analysis was conducted among ORB-SLAM3, DDPG-SLAM, and the proposed Mamba-based SLAM method. The results in Table 6 demonstrate that the execution time of the proposed method is generally comparable to that of ORB-SLAM3 across multiple test sequences, such as MH01, MH04, V202, and Room2. For instance, in the MH01 sequence, the time difference between the two methods is merely 1 s, which is negligible, indicating that the proposed method maintains a real-time performance similar to ORB-SLAM3. Furthermore, compared with DDPG-SLAM, the Mamba-based method exhibits shorter execution times in nearly all test sequences. As shown in Table 6, in the MH01 sequence, the proposed method runs 3 s faster than DDPG-SLAM, while in the MH02 sequence, it achieves a 20-s reduction in execution time, demonstrating a significant advantage in computational efficiency.  
Overall, the introduction of the Mamba component leads to an increase in memory usage; however, this does not result in a significant rise in execution time. On the contrary, in some cases, it achieves higher efficiency than DDPG-SLAM. These findings suggest that the Mamba component effectively enhances computational efficiency while maintaining real-time performance within a reasonable resource overhead.
## 2 Crop Yield
### Soil Quality  
Physical, chemical, and biological indicators are used to assess soil quality. A set of general indicators of soil quality has not yet been defined, mainly because of the difficulty of defining and identifying what soil quality is and how it can be measured [26]. Nevertheless, a good starting point is to draw up a list of measurable soil indicators, which define the main functioning processes in the soil and ensure that the measurements we make reflect the conditions as they exist in the field. The list of indicators includes different nitrogen forms, organic matter content, soil pH, the erosion degree, and the infiltration properties.  
Indicators such as erosion and their impact on crops are not easily predictable or controllable. In contrast, other important indicators are not only predictable, but also have a large long-term impact and can be addressed in a palliative manner. Among those indicators, nitrogen, phosphorus, and potassium levels are the keys to plant growth. It is therefore of great interest to understand the essential processes of these minerals and to define in more detail the means for highly productive and environmentally friendly agriculture.  
An assessment of nutrient amendments, consumption and balances in the environment provides useful practical information on the evolution of nutritional status (maintained, accumulated, or depleted). It provides information about the efficiency level of fertilizer use in terms of quantity absorbed by the crop. This can help to prevent nutrient deficiencies that can be disastrous for plants. Table 1 gives the nutrient consumption of some popular crops in sub-Saharan where traditional agriculture is mainly practiced [4]. From the Table 1, tomato and onion are among the crops that consume a smaller quantity of nitrogen. Table 2 indicates the maximum annual nitrogen rate per hectare in fertilizer equivalent for some crops [27]. However, since we are dealing with organic farms, part of the mineral fertilizer should be replaced by residual organic matter. Nitrogen supply using organic amendments to crops is usually expressed in the amendment's Nitrogen Fertilizer Replacement Value (NFRV) [28], also called the Mineral Fertilizer Equivalent [29]. NFRV is usually expressed in terms of the quantity of mineral fertilizer N that is substituted by an amount of organic amendment N (kg/kg) while obtaining the identical crop yield [30]. Depending on the soil and the source of manure, the NFRV of Mineral Concentrate (MC) can reach more than 80% [31].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Crop** & **Nitrogen** & **Phosphorus** & **Potassium** \\ \hline Cabbage & 3.5 & 1.3 & 4.2 \\ Carrot & 3.9 & 1.7 & 6.6 \\ Onion & 2.7 & 1.3 & 3.9 \\ Tomato & 2.8 & 1.3 & 3.8 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Nutrient consumption of some popular crops (in Kg/ton).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Crop** & **Nitrogen** \\ \hline Cabbage & 240 \\ Carrot & 120 \\ Onion & 120 \\ Tomato & 420 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Maximum annual nitrogen rate per hectare in fertilizer equivalent (in Kg/ha).
## 4 Results
### Model Validation  
In order to validate the numerical tool, the fluid temperatures predicted by the numerical tool were compared with those measured in the He/LOx drainage and He inlet. Table 3 presents the comparison of the numerical predictions with the measured temperature data used for the validation.  
As previously mentioned, both pairs of probes used for validation are placed radially opposite in the outer crown, aligned with the drainage conduit. Therefore, the examined  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Results**} \\ \hline  & & _Test Case 1_ & _Test Case 2_ & _Test Case 3_ & _Test Case 4_ & _Test Case 5_ \\ \hline
**He/LOx Drain** & \(T_{\rm A}\)\(\rm(K)\) & 187.01 & 205.06 & 215.12 & 223.00 & 229.36 \\
**-** & \(T_{\rm 11}\)\(\rm(K)\) & 191.04 & 199.61 & 205.14 & 209.62 & 213.97 \\
**Probe A** & \(E_{\rm r_{\rm v}}\)\(\rm(\%)\) & \(\bf-2.15\) & \(\bf 2.65\) & \(\bf 4.63\) & \(\bf 5.99\) & \(\bf 6.70\) \\ \hline
**He/LOx Drain** & \(T_{\rm B}\)\(\rm(K)\) & 165.58 & 181.12 & 188.81 & 194.13 & 198.59 \\
**-** & \(T_{\rm 18}\)\(\rm(K)\) & 166 & 178.18 & 185.18 & 191.03 & 196.83 \\
**Probe B** & \(E_{\rm r_{\rm v}}\)\(\rm(\%)\) & \(\bf-0.81\) & \(\bf 1.61\) & \(\bf 1.92\) & \(\bf 1.59\) & \(\bf 0.88\) \\ \hline
**He Inlet** & \(T_{\rm C}\)\(\rm(K)\) & 270.95 & 274.93 & 276.83 & 278.27 & 279.60 \\
**-** & \(T_{\rm 29}\)\(\rm(K)\) & 265.71 & 268.90 & 270.86 & 272.49 & 274.21 \\
**Probe C** & \(E_{\rm r_{\rm v}}\)\(\rm(\%)\) & **1.93** & **2.19** & **2.15** & **2.07** & **1.92** \\ \hline
**He Inlet** & \(T_{\rm D}\)\(\rm(K)\) & 250.53 & 257.52 & 260.62 & 263.06 & 265.31 \\
**-** & \(T_{\rm 30}\)\(\rm(K)\) & 245.14 & 250.89 & 254.36 & 257.25 & 260.15 \\
**Probe D** & \(E_{\rm r_{\rm v}}\)\(\rm(\%)\) & **2.14** & **2.57** & **2.40** & **2.20** & **1.94** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Temperatures of the He/LOx drain and He inlet from the computed (numerical tool) and experimental data.  
nodes are 11 and 18 for the He/LOx drain and 29 and 30 for the He inlet. The validation of the tool was carried out by analysing the percentage error between the measurements and the prediction:  
\[E_{\tau_{5}}=\frac{T_{\text{predict}}-T_{\text{meas}}}{T_{\text{meas}}}\,100 \tag{23}\]  
As shown in Table 3, a good agreement of the predicted values with the experimental data (within 7%) can be observed.