esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Calorimetric Measurements
### Results  
As a first step, the expected dependence of \(P_{cal}\) from the operational frequency was verified (see Figure 5a) and a linear fit was found to be suitable. The calculated slope \(A_{GR}\) for the grounded sample was found to be much larger (up to one order of magnitude) than the slope \(A_{FL}\) calculated for the floating sample. This result suggests that the key mechanism for the power deposition to the sample is the current flow within the target itself. This hypothesis is confirmed by the dependence of \(P_{cal}\) from the target thickness. In particular it was found that thicker samples have a significantly lower power deposition (see Figure 5b). Furthermore, for the same sample geometry, a smaller power (about 20% less) was measured on brass samples than on steel samples (steel electrical resistivity is about 70% larger).  
Figure 4: Time evolution of the average temperature of a stainless steel target (12.7 \(\times\) 12.7 \(\times\) 0.08 mm\({}^{3}\)) at a distance \(d_{PCC}\) = 10 mm from the source nozzle, at different working frequencies. The transparency area indicates the time interval in which the thermal power \(P_{cal}\) is estimated.  
The dependence of \(P_{cal}\) on the target distance was found (for grounded samples) to be clear and monotonous as shown in Figure 6. In the first approximation, this is consistent with the lower interaction time between the plasma discharge (which propagates forward) and the substrate, as the distance source-substrate is increased. On the other hand, the plasma metal interaction region is strongly affected by the distance variation--this implies a difference in the resistive coupling, since the current patterns cannot be considered constant.
## 2 Materials and Methods
### Feature Selection  
As mentioned above, the SCADA dataset used in this study consists of 55 variables. The variables were reduced to prevent the model from becoming too complex and to thus improve the learning of the features related to the target component. For this purpose, the variables were selected based on [PERSON]'s correlation.  
Figure 2: Percentage of failures and alarms of all wind turbines at VWF. (**a**) Representative faults from the maintenance record. (**b**) SCADA alarms related to the fault component.  
Figure 3 depicts a correlation matrix for several variables related to the operation of the wind turbines and their components, particularly the converter and the IGBT. This matrix shows the strength and direction of the relationships between pairs of variables, represented by colors, where red indicates a high positive correlation, and blue has a high negative correlation.  
The variable _igbt_temperature_max_ shows a strong positive correlation with several other variables, such as _wind_speed_avg_, _grid_active_power_avg_, _generator_speed_avg_, and _ambient_temperature_avg_. This suggests that the IGBT temperature is closely linked to the overall WT operation and environmental conditions.  
Three converter faults were identified in the preliminary analysis, which prevented us from observing significant correlations between the IGBT failure variable and the other variables. To address this challenge, the 10,000 data prior to each failure were labeled under the assumption that the component degrades over time and indications of failure may exist in such data. This allowed the data set to be balanced and correlations to be observed. This procedure is similar to that employed in [24]. In addition, although the correlations  
Figure 3: Feature selection by correlation.  
observed are small, they provide insight into the variables that could be involved in the faults.  
Despite the low direct correlation between IGBT failures and the other variables, including tags in the pre-failure data provides valuable insight into understanding conditions that could contribute to converter failures.  
In summary, this study chose variables based on their correlation with the target variable, correlation with converter failures, correlation with converter alarms, and the authors' domain knowledge. Thus, nine variables related to the target component were selected, and are shown in Table 2.  
This selection ensures that the model effectively captures the operating conditions of the target component, anticipates possible failures, and minimizes the detection of failures in other components, improving the WTs reliability and efficiency.
## 2 Results and Discussion
### LC-MS Conditions Optimization  
#### 2.1.1 Chromatographic Column Selection  
When the Acquity UPLC(r) BEH C18 1.7 \(\upmu\)m and the Kinetex(r) 1.7 \(\upmu\)m F5 columns were used, the chromatographic separation was incomplete and target analyte peaks were overlapped, regardless of the applied gradient of mobile phases. The best separation of analytes was obtained using column Kinetex(r) 2.6 \(\upmu\)m C18 100 A 100 mm \(\times\) 3 mm.
# Implementation and Evaluation of Activity-Based Congestion Management Using P4 (P4-ABC)
## 6 Performance Evaluation
### Experimental Results
#### 6.2.1 Resource Sharing with CBR Traffic  
In a first experiment series, both clients send constant bit rate (CBR) traffic using UDP packets with 1448 bytes payload. Client 0 transmits at different rates \(R_{0}\) while Client 1 sends at \(R_{1}=40\) Mb/s. Figure 4 shows the obtained goodput for both clients.  
Without ABC, the goodput of Client 0 continuously increases with increasing traffic rate \(R_{0}\) while the goodput of Client 1 decreases. In particular, the goodput is proportional to the sent traffic rate of both clients. Thus, unfair sending behaviour is rewarded with higher goodput. This is different with ABC. The goodput of Client 0 continuously increases with increasing traffic rate \(R_{0}\) as long as Client 0 sends less traffic than Client 1. As a consequence, the goodput of Client 1 continuously decreases. If Client 0 sends more traffic than Client 1, the traffic of Client 0 is preferentially dropped due to increased activity so that the goodput of Client 0 becomes clearly smaller than the goodput of Client 1. Thus, ABC creates an ecosystem in which senders benefit from decreased activity in case of congestion instead of being rewarded for sending at high rate. This incentivizes the use of congestion-controlled transport protocols.  
Figure 3: Experimental setup.
## 2 Multi-UAV Cooperative Persistent Monitoring Path Planning Model  
### Problem Description  
As the monitoring environment changes and the node quantity increases, computer resources onboard are often insufficient when performing persistent monitoring tasks in the stand-alone operation mode. As a result, the waiting time of nodes increase, causing some nodes to monitor overdue. Compared with a single drone, a drone group performing persistent monitoring tasks will face huge challenges. For example, each node will maintain a parameter that represents how long it has been waiting since its last monitoring. Once any drone visits a node position and completes that monitoring, the waiting-time parameter maintained by this node will be cleared-- demonstrating a rigid nonlinearity. Other difficulties include collision avoidance between multiple drones, information synchronization, and collaborative work between drones.  
This study focuses only on the multi-UAV cooperative path planning problem of persistent monitoring. A graph model is used to describe the distribution of the candidate nodes, i.e., \(G=(V,E)\), where \(V=\{1,\,2,\,\ldots,\,N\}\) represents the nodes set, \(N\) represents the total number of nodes, and \(E=\{e_{ij},\forall i,j\in V\}\) represents the edges set of \(G\). The UAV set is \(M_{UAV}=\{1,2,\ldots,M\}\), where \(M\) is the total number in the given UAV group, \(M<<N\). Here are some assumptions about the background of this study.  
(1) For safety and efficiency purposes, the same nodes cannot exist for multiple drones at the same time. This means that different UAV are permitted to monitor the same node on different time.  
(2) Without loss of generality, all UAVs fly with a constant speed, \(v\).  
(3) After a UAV accesses a node, the waiting time of the node is cleared, and all other UAVs need to be notified to ensure information synchronization.  
This research tries to find the optimal flight path of a UAV group, so that the path meets the requirements of high monitoring frequency and strong monitoring path privacy.
## 5 Experimental Results and Analysis
### Description of the Benchmark Datasets
#### 5.2.2 UNSW-NB15 Dataset  
The UNSW-NB15 is a new dataset that reflects real modern normal activities and contains synthetic contemporary attack behaviors [26]. This dataset is completely different from NSL-KDD, which reflects a more modern and complex threat environment. The raw network packet of the UNSW-NB15 dataset was created by the Tcpdump tool, and then 49 features with the class label were generated by Argus, Bro-IDS tool and 12 algorithms [27]. The full dataset contains a total of 25,400,443 records. The partition of the dataset is configured as a training set and a test set, namely, UNSW_NB15_training-set.csv and UNSW_NB15_testing-set.csv, respectively. The number of features in the partitioned dataset [26] is different from the number of features in the full dataset [25]. The partitioned dataset has only 43 features with the class label, removing 6 features (i.e. \"sricy\", \"sport\", \"dstip\", \"dsport\", \"Stime\" and \"Ltime\") from the full dataset. There are ten different class labels in the partitioned dataset, one normal and nine attacks, namely, Generic, Exploits, Fuzzers, DoS, Reconnaissance (Reconn), Analysis, Backdoor, Shellcode and Worms. We used a stratified sampling method to randomly select 20% of the records from the UNSW_NB15_training-set and UNSW_NB15_testing-set datasets as training and test datasets. The training dataset consists of 35,069 records, whereas the testing dataset contains 16466 records. Table 4 shows in detail the class distribution of the UNSW-NB15 subset.
# Maximizing the Capacity of Edge Networks with Multicasting
## 2 Related Work
### Multicasting in Edge Networks  
The authors of [28] investigated the task offloading with multicast in vehicular edge networks. The study [29] studied the multicast-aware resource allocation for edge computing, and computing and caching are jointly optimized in multicast scenarios. Furthermore, the authors of [30] proposed a cooperative multicast-aware caching scheme to improve the delivery delay. The transmission bandwidth gain was analyzed in [27], where the local caching and computing policy are jointly considered subject to multicast transmission constraints.
## 5 Experimental Results and Discussion
### Gas Consumption for User Management  
In this section, we report the gas consumption for user management operations. With user management and user access control, the deployment cost for our smart contract is 2,742,751 gas, which is significantly higher than without. Table 2 shows the gas consumption for grant roles (GrantAdmin, Grantwriter, Grantreader) and delete roles (deleteAdmin, deleteWriter, deleteAder) operations. As can be seen, the most expensive operation is to grant a new user the Admin role, which costs 95,198 gas because three entries in the roles mapping are updated. It costs a little less to grant a new user with the Write role, at 72,867 gas, where two entries in the roles mapping are updated. It costs the least to grant a new user with the Read role, at 49,977 gas, where only a single entry in the roles mapping is updated.  
An account may be stripped off the Admin, Writer, or Reader role via the deleteAdmin, deleteWriter, deletereader functions. Only the account that created the smart contract (i.e., the Owner) may remove users with the Admin role. Accounts with the write or read roles may be deleted by any account with the Admin role. The deleteAdmin incurs 37,262 gas. The deleteWriter incurs 31,347 gas. The deletereader incurs 28,613 gas. The gas consumption for user management is summarized in Table 2 and illustrated in Figure 12. As expected, the gas consumption for addition and deletion of admin accounts is higher than those of the writer accounts, which in turn is higher than those of the reader accounts.
## 3 Results and Discussion
The luminance reached a maximum of 28,450 cd/m\({}^{2}\) at 11.0 V. A maximum EQE of 3.11%, a current and power efficiency of 10.00 cd/A and 4.5 lm/W were obtained, far from the values previously reported (0.57 cd/A). Figure 3: Luminance–voltage characteristics obtained with the two device configurations (A and B). Clearly, optimization of the device structure allowed to drastically improve the EL efficiency. OLEDs also showed a low current efficiency roll-off, with a decrease of less than 10% at 200 mA/cm\({}^{2}\). On the opposite, a more severe decrease of 35% at 200 mA/cm\({}^{2}\) was determined for the external quantum efficiency (See Figure 6). While coming back on the turn-on voltages of type B-devices and  
Figure 4: Electroluminescence spectra of OLEDs with device configurations A and B recorded by varying the applied voltage. Inset: variation of the CIE coordinates with the driving voltage. considering that type B- and type C-devices can both efficiently confine electrons and holes within the emissive layer, a lower turn-on voltage is observed for type B-devices (2.8 V) compared to type C-devices (3.4 V) and this difference can be assigned to the difference of thickness. Indeed, the addition of the BCP interlayer has increased the overall thickness of 10 nm, justifying the higher turn-on voltage for the corresponding devices. Finally, device lifetime of type C-devices was investigated. Indeed, for future applications in displays and lighting, operational stability of devices is a critical issue [23; 24]. Devices were operated at room temperature and at constant applied voltage starting from an initial luminance of 1500 cd/m\({}^{2}\). As evidenced in the inset of Figure 7, an initial luminance corresponds to a driving voltage of 6.3 V, and this value was maintained constant during the lifetime measurements. As observed in the Figure 7, OLEDs could maintain over 90% of the initial luminance during 30 min. This value is comparable to that reported for (mdppy)\({}_{2}\)Iracac as triplet emitter in a comparable device structure [25]. A half-life of about 300 min was also determined. Figure 5: Electroluminescence spectra of OLEDs with device configuration C recorded by varying the applied voltage. Inset: variation of the dominant wavelength with the driving voltage and CIE coordinates of the emitted light. These results indicate that the phthalimide accepting group is a promising structure for the design of light-emitting materials. While examining the modification of the emitted light over time, almost no  
Figure 6: Current and external quantum efficiencies vs. current density characteristics for type C-OLEDs. Figure 7: Time evolution of the normalized luminance, starting from an initial luminance of 1500 \(\mathrm{cd/m^{2}}\). Inset: Evolution of CIE coordinates over time. color shift was observed after 1350 min of continuous operation. Only a decrease of the EL intensity of OLEDs was detected.
## 3 Results
### Main Effects of Operation Method  
The ANOVA results revealed that the main effects of the operation method were statistically significant for all measures except for preference: accuracy \(F(2,78)=6.44\)  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**DesignMeasure** & **Accuracy** & **Efficiency** & **Rapidity** & **Learnability** & **Intuitiveness** & **Safety** & **Preference** & **Memorability** & **Satisfaction** \\ \hline \(\mathbf{M_{LS}L_{IWS}}\) & 2.65 (1.82) & 3.38 (2.07) & 3.56 (1.99) & 3.30 (1.94) & 2.68 (1.67) & 2.40 (1.55) & 2.45 (1.80) & 3.63 (2.03) & 2.50 (1.69) \\ \hline  
\(p<0.01\); efficiency \(F(2,\,78)=5.27\), \(p<0.01\); rapidity \(F(2,\,78)=3.20\), \(p<0.05\); learnability \(F(2,\,78)=6.36\), \(p<0.01\); intuitiveness \(F(2,\,78)=7.13\), \(p<0.01\); safety \(F(2,\,78)=6.38\), \(p<0.01\); preference \(F(2,\,78)=2.97\), \(p=0.06\); memorability \(F(2,\,78)=6.36\), \(p<0.01\); and satisfaction \(F(2,\,78)=4.34\), \(p<0.05\). The mean and standard deviation values of each operation method for each of the dependent measures are shown in Figure 2, with asterisks indicating the statistical significance of the post hoc comparisons.  
The statistically significant differences between the operation methods are as follows:  
* \(\mathbf{M_{LS}}\) showed significantly higher mean scores than \(\mathbf{M_{DR}}\) in accuracy, learnability, intuitiveness, safety, and memorability.
* \(\mathbf{M_{BP}}\) exhibited significantly higher mean scores than \(\mathbf{M_{DR}}\) in efficiency, learnability, intuitiveness, safety, memorability, and satisfaction.
# Underwater Communications for Video Surveillance Systems at 2.4 GHz
## 5 RTT Measurements for Each Modulation at Several Temperatures  
Another important parameter to be considered on network performance is the RTT value between devices. This section presents the results of the average value of RTT registered for each test as a function of the temperature, modulation and working frequency, for maximum distances.
# Jamming Detection and Suppression Technique for OTFS Systems in an AWGN Channel
## 2 System Model
### Jamming Suppression Technique  
Once the jamming signals have been recognized and the detailed frequencies are obtained, jamming suppression techniques can be employed to mitigate the adverse impact of interference. The most commonly used methods are nulling suppression and clipping suppression.  
Let \(R(k)\) denotes the FFT result of received signal \(r(t)\) and \(\Psi_{J}\) represent the frequency index set of the jamming signal. The nulling suppression method can be expressed as  
\[\hat{R}(k)=\begin{cases}R(k),&k\
otin\Psi_{J}\\ 0,&k\in\Psi_{J}\end{cases}. \tag{9}\]  
Figure 2: Partial-band jamming in time–frequency domain and delay-Doppler domain.  
Signals identified as jamming are completely eliminated by setting their values to zero. This approach can effectively remove strong interference; however, it may also result in the loss of some useful signals, particularly when the interference overlaps with the signal spectrum.  
The clipping suppression method mitigates the jamming signal by limit the amplitude of the signal to a certain threshold, which can be expressed as  
\[\big{|}\hat{R}(k)\big{|}=\left\{\begin{array}{ll}|R(k)|,&k\
otin\Psi_{J}\\ |\lambda\bar{R}|,&k\in\Psi_{J}\end{array}\right., \tag{10}\]  
where \(\bar{R}=mean\{|R(k)|\}\) presents the mean value of \(|R(k)|\) for all \(k\), and \(\lambda\) is the clipping factor. With this technique, the phase information is retained; however, residual interference may remain if \(\lambda\) is not appropriately chosen, which can affect subsequent demodulation performance.
# A New Improved Multi-Sequence Frequency-Hopping Communication Anti-Jamming System
## 3 System Model and Problem Formulation  
### System Model  
The system model of this paper is illustrated in Figure 1. We consider a wireless communication scenario in a complex jamming environment, involving multiple jammers and a user pair (receiver and transmitter). The user pair counters various types of jammers, including intelligent jammers capable of emitting reactive jamming and non-intelligent jammers that emit sweeping jamming, multi-tone jamming, and other types. Both receivers and intelligent jammers are capable of spectrum sensing and obtaining channel state information. The user employs Multi-Sequence Frequency-Hopping (MSFH) technology for communication. Unlike conventional frequency-hopping communication, MSFH encodes information through channel representation and the hopping points associated with the frequency-hopping sequence. The receiver, using two complementary frequency-hopping sequences, can obtain prior knowledge of the next hop channel selection. It only needs to detect the frequency in each time slot and compare it with the two complementary frequency-hopping sequences to retrieve the corresponding bit information.
# Efficient Route Management Method for Mobile Nodes in 6 TiSCH Network
## 4 Performance Analysis
### Scheduling Performance Analysis  
To analyze the performance of the algorithms proposed in this paper, we utilized a tree topology of up to three hop sizes consisting of ten nodes, as shown in Figure 7. In the topology, the distance per node was the same, with a distance of 5 m. A moving node moves at a constant moving speed, such that at least one node in the network is at a communicable distance, generating packets, and sending them back to the root node until they return to the starting point.  
The composition of the experimental environment is the first time slot in a slot frame, with six channels in the TSCH network, 10 ms in length in time slots, 21 time slots in slot frames, and one shared cell. One packet was generated per second, and the experiment was conducted at a moving speed that took five s to move from one node to another. Each experiment was repeated 10 times, and the average value of the corresponding results was calculated. The RPL configuration followed the OF0 standard.  
\begin{table}
\begin{tabular}{c c} \hline RFC6550, RFC6552 & RPL, non-storing mode, OF0 \\ \hline RFC6206 & Trickle Algorithm \\ \hline draft-ietf-6 lo-minimal-fragment-07 & 6 LoWPAN Fragment Forwarding \\ \hline RFC6282, RFC4944 & 6 LoWPAN Fragmentation \\ \hline draft-ietf-6 tisch-msf-10 & 6 TiSCH Minimal Scheduling Function (MSF) \\ \hline draft-ietf-6 tisch-minimal-security-15 & Constrained Join Protocol (CoJP) for 6 TiSCH \\ \hline RFC8480 & 6 TiSCH 6 top Protocol (6P) \\ \hline RFC8180 & Minimal 6 TiSCH Configuration \\ \hline IEEE802.15.4-2015 & IEEE802.15.4 TSCH \\ \hline \end{tabular}
\end{table}
Table 2: 6 TiSCH simulator protocol stack
# Two Decades of Groundwater Variability in Peru Using Satellite Gravimetry Data
## 4 Discussion  
This study documents a critical 30-40% decline in GWS across Peru's coastal and Andean regions (2003-2023), driven by anthropogenic overextraction and ENSO-related climate variability. Below, we analyze spatiotemporal trends, socioeconomic repercussions, global parallels, and evidence-based strategies to mitigate this crisis, while acknowledging methodological limitations.
## Evaluation and Results  
### Simulation Environment and Parameters  
Work was conducted on the OMNeT++ [41] framework, a platform on which simulations can be built. As such, in this study MiXiM [42] was utilised to build the network environment, including the location of nodes and the size of the actual area within which they are located. In addition, inetmanet [43] was used for all other factors, including physical, MAC, network, and transport layer parameters, as well as the use of mobility and energy models.