esa-sceva/satcom-chunk-collection huggingface dataset:
# Cyberattack on Flight Safety: Detection and Mitigation Using LoRa
## 3 Flight Safety Framework
### LoRa 2.4 GHz Band  
A new LoRa chip is available on the market, offering the 2.4 GHz ISM band with bandwidths up to 1.6 MHz and having a built-in Time-of-Flight (ToF) ranging engine [17]. Transmitting a message using this new chip is similar to transmitting a message with the previously described chip, but with higher data rates due to the higher bandwidth and frequency. The ToF ranging functionality is based on the measurement of a round-trip ToF between a pair of these new LoRa 2.4 GHz transceivers (Figure 3). This process is based on the following sequence. One module assumes the role of a master and initiates a ranging request message toward a receiver that has been configured as a slave. The master starts an internal timer once the ranging request message has been sent. Both the master and slave modules must be configured the same. The slave module that receives the ranging request message synchronizes itself with the incoming signal and sends the synchronized ranging response back to the master. The master then deduces the round-trip ToF from the time elapsed and calculates the ranging results between the two modules. Calibration and correction need to be performed on the ranging result to obtain a precise and accurate range measurement.  
Figure 3: The LoRa 2.4 GHz module.  
The ranging protocol between the two modules is based on data sent from the master to the slave without revealing the master's information other than the request itself. Even if an adversary is able to respond to a ranging request, it will not be able to derive information other than the master's ID. However, this can be resolved by using anonymous ranging requests.
## 5 Case Study
### Data Collection  
The proposed hybrid model is applied to a case study to forecast the grouting power of consolidation grouting in hydropower project X, which is located on the Yalong River in Sichuan Province in southwestern China. Hydropower project X is currently under construction; once completed, it will have a hydroelectric generating capacity of 1500 MW and the dam height will reach 155 m. The whole dam can be divided into 17 continuous segments. One grouting hole is selected randomly to serve as the research object. A grouting power dataset of the selected hole is collected with a time interval of 5 s based on a grouting real-time monitoring system. A total of 600 data collected during the grouting process were selected as a sample. Five-hundred data were selected to construct the training set for determining the optimal hyperparameters of the SVR model, while the remaining 100 data were selected as the testing set to evaluate the prediction performance of the obtained model. The statistical information i.e., maximum, minimum, mean, standard deviation and median of this grouting power  
Figure 3: The flow chart of SVR optimized by IJaya.  
series are calculated and given in Table 1. Figure 4 shows the shapes of the grouting pressure, injection rate, and grouting power.
# Increasing Photovoltaic Systems Efficiency Through the Implementation of Statistical Methods
## 2 Literature Review
### Analysis of the PV Market in Romania  
The cumulative global PV installed capacity increased from 483.1 GW in 2018 to 580.2 GW in 2019, representing a 21% relative growth [27]. It further increased from 866.02 GW in 2021 to 1055.03 GW in 2022, representing a 22% relative growth [28]. The highest contribution of currently installed PV systems is identified in Asia. Relevant examples come from China (175 GW in 2018 and 392.46 GW in 2022), Japan (55.5 GW in 2018 and 83.1 GW in 2022), and India (26.8 GW in 2018 and 62.85 GW in 2022). Europe comes in second regarding PV installed capacity, with considerable shares in Germany (45.9 GW in 2018 and 66.66 GW in 2022), while the UK has an insignificant increase (13.4 GW in 2018 and 14.66 GW in 2022).  
Specific prices for PV panels decreased approximately linearly starting in 2008, by about 35% on the American market and by about 80% on the European market, likely due  
\begin{table}
\begin{tabular}{c c} \hline \hline
**PV Panel Type** & **Efficiency [\%]** \\ \hline PERC (passivated emitter rear contact) & 22.8 \\ Bifacial PERC & \textless{}25 \\ PERL (passivated emitter with rear locally diffused cells) & 24.5 \\ HIT (heterojunction with intrinsic thin layer) & 26 \\ HJ-IBC (heterojunction-interdigitated back contact) & 26.7 \\ TOPCon (tunnel oxide passivated contact) & 26.1 \\ Thin-film CIGS (copper indium gallium selenide) & 22.2 \\ Tandem solar cells based on silicon and perovskite & 32 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Efficiency of various PV panels [16; 23; 24].  
to cheaper imports from China [29; 30]. For instance, in the USA, the mean price of PV panels varied from USD 2.51 to USD 3.31 per watt in 2020, while the average costs were USD 0.40-USD 0.60 per watt for polycrystalline panels and USD 0.60-USD 0.90 per watt for monocrystalline panels as of January 2020. Chinese production became the market leader relatively quickly, starting in 2007. Since China is the world's largest manufacturer and consumer of PV cells and modules, Chinese production has driven down prices from USD 4.73 per watt in 2007 to USD 0.19 per watt in 2020 [29].  
Considering the significant variability of the statistical data available in the literature, Table 2 includes several relevant commercial offers of photovoltaic modules available on the Romanian market in February 2024 based on various technologies: Simono and poly-crystalline, PERC and TOPCon technologies, bifacial variant, and GaAs technology with concentrators (Fresnel lenses), all with an efficiency between 17% and 28%. Table 2 is based on the prices provided by retailers' online shops in Romania.  
The efficiency shown in Column 4 is calculated by dividing the PV module's peak power output from Column 3 by the maximum irradiation in the region (1000 W/m\({}^{2}\)) for the panel area from Column 2.
# Deep Learning at the Mobile Edge: Opportunities for 5G Networks
## 4 Challenges of DL for 5G Operations at the Mobile Edge
### Security and Privacy  
Two main concerns that have deterred the deployment of large-scale DL systems at the mobile edge computing are the rightful concerns regarding data security and privacy with collected data. Security must be guaranteed when working in 5G mobile edge computing because of the necessary sharing of physical infrastructure between slices and the potential for information leaks about data or usage patterns. As NFV is deployed, isolation between virtual machines and slices must also be guaranteed to promote privacy and reduce performance interference [89]. 5G mobile edge computing networks must also protect themselves from malicious actors, and can use DL to detect and protect against attacks [86; 87], though new slicing infrastructure and virtualized networks may require deviation from industry-standard security techniques. Efforts to enhance DL performance should also be built with privacy in mind [88]. Network providers must investigate for any privacy violations in the collection or use of user data before large-scale ML systems are deployed, especially in the case of smart cities or personal electronics, such as connected cars and IoT, which can reveal intimate details about the public as a whole.
# Credibility Based Imbalance Boosting Method for Software Defect Proneness Prediction
## 6 Discussion
### Is CIB Comparable to Rule-Based Classifiers?  
In this section, we provide a comparison between CIB and some rule-based classifiers, including OneR [98], Bagging (C4.5, i.e., J48, is used as the base classifier in this study) [99], and RIPPER [100]. We implement OneR, Bagging, and RIPPER by WEKA with the default parameters. We perform experiments on NASA datasets and use five-fold CV (see Section 4.5) to evaluate the prediction performance. On each dataset, we run each model 30 times with 5-fold CV and report the average  
Figure 4: Barplots of PD and PF for all methods across all benchmark datasets. The horizontal line in the box denotes the median, while the asterisk indicates the mean.  
performance. Owing to the space limitation, we just report the experimental results of \(F_{1}\), AGF, AUC, and MCC with box plots, as shown in the Figure 5.  
From the figures, we can notice that (1), with respect to \(F_{1}\), AGF, and MCC, CIB obviously outperform the baselines, i.e., OneR, Bagging, and RIPPER; (2) with respect to AUC, CIB has similar performance to Bagging and obviously outperforms OneR and RIPPER.
# A Random Forest-Based Precipitation Detection Algorithm for FY-3C/3D MWTS2 over Oceanic Regions
## 3 Data Preprocessing and Model Training
### Data Preprocessing
#### 3.1.2 Data Augmentation and Downsampling  
As a data-driven algorithmic model, the \"long-tailed\" phenomenon of the training dataset (i.e., where a few classes have many samples but the remaining majority classes possess much smaller sample sizes) in the machine learning model can lead to poor classification performance for a few classes with limited samples [25], which in turn affects the overall performance of the model. Among the 271,410 training samples used in the experiment, those with \(SI>30\) are treated as precipitation samples, and those with \(SI<30\) K are treated as nonprecipitation samples. In this way, the number of precipitation samples is 26,913 (approximately 10%), and the number of nonprecipitation samples is 244,497 (approximately 90%), which is a typical \"long-tailed\" case. The data distribution is shown in Figure 6a. To solve the \"long-tailed\" issue, one can scale the sample probabilities such that the sample probability of each class drawn is at the same level as that in [26; 27]. Data augmentation techniques can also be used by adding minor changes to the available data (e.g., translation, rotation, clipping, scaling, and noise addition) to create new datasets with larger sizes [28].  
In this work, white noise is added to the dataset to augment the precipitation data, as shown in (14).  
\[x_{new}=x+\mu+\sigma\times Gaussian\_number \tag{14}\]  
where \(x\) represents the original data, \(x_{new}\) represents the new sample obtained after performing data augmentation, \(\mu=0\) and \(\sigma=0.01\) represent the mean and variance in the Gaussian function, respectively, and \(Gaussian\_number\) is a random number that is automatically generated in MATLAB (2017a) and obeys the Gaussian distribution. The data augmen  
Figure 5: FY-2F and NOAA-19 observations that satisfied the pairing conditions **(a)** at 06:00 UTC during the day and **(b)** at 21:00 UTC during the night on 2 January 2018. Red markers indicate observations satisfying the matching criteria, and yellow markers denote observations that do not meet the matching conditions.  
tation process expands the original 26,913 precipitation samples to 53,826, the number of precipitation samples increases by 100%, and the total number of samples increases from 271,410 to 298,323. The data augmentation results are shown in the red samples in Figure 6b, and the SIs of all the enhanced samples are greater than 30 K  
To achieve dataset equilibrium, after the precipitation samples are augmented, the nonprecipitation samples are randomly downsampled to make them consistent with the number of precipitation samples (Figure 6c). Then, 80% of the data are randomly selected as the training set, and 20% are selected as the testing set. The results of the data preprocessing strategy are shown in Table 2.
## 2 Results and Discussion
### GC-MS-Based Metabolite Profiles
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Monoterpenes(26)** & **Chemical** & **Tissues** & **Types and Number** \\ \hline \(\beta\)-Myrcene * & C10H16 & All tissues & \\ \(trans\)-\(\beta\)-Ocimene * & C10H16 & Bud, FL, SL, Stem & \\ Linalool * & C10H18O & All tissues & \\ Geraniol * & C10H18O & All tissues & \\ \(\alpha\)-Citral * & C10H16O & All tissues & \\ \(\beta\)-Citral * & C10H16O & All tissues & \\ Methyl geranate * & C11H18O2 & ML, SL & \\ Nerol * & C10H18O & All tissues & Acyclic(17) \\
8-Hydroxylinalool & C10H18O2 & All tissues & Acyclic(17) \\
3,7-Octadiene-2,6-di-ol, 2,6-dimethyl & C10H18O2 & FL, SL, Stem & \\ neryl acetate * & C12H20O2 & FL & \\ Isogeraniol & C10H18O & All tissues & \\ \(\gamma\)-Isogeraniol & C10H18O & All tissues & \\ Geranyl acetone * & C13H22O & All tissues & \\ Citronelly formate & C11H20O2 & All tissues & \\ DMDD & C10H18O2 & All tissues & \\ Hotriend & C10H16O & All tissues & \\ \hline \(D\)-Limonene * & C10H16 & All tissues & \\ \(trans\)-Linalool oxide (furanoid) * & C10H18O2 & All tissues & \\ \(L\)-Terpineol * & C10H18O & All tissues & Monocyclic(6) \\ \(\alpha\)-Terpinen * & C10H16 & All tissues & \\
4-Terpinenyl acetate & C12H20O2 & All tissues & \\ Thymol * & C10H14O & Stem & \\ \hline
2,3-Dehydro-1,8-cineole & C10H16O & Stem & \\ \((+)\)-Camphene & C10H16 & All tissues & Bicyclic(3) \\ \(trans\)-\((-)\)-Pinocarveo & C10H16O & Stem & \\ \hline \hline \end{tabular} Note: DMDD: (\(E)\)-2,6-Dimethylocta-3,7-diene-2,6-diol. * were identified using authentic standard compounds.  
\end{table} 
Table 1: Monoterpenoids detected in different tissues of tea plants.
## 3 Results & Observations
### Transmission Power Allocation and Relation to Distance  
We have conducted some line-of-sight experiment simulations related to range, DR allocation and TX power. Our observation is that TX and SF are inversely proportional regarding the impact of communication range. For example, an ED link of 4 km distance could be achieved by both DR4 with TX 10 and DR3 with TX 6. Additionally, for TX power levels, our experiments showed that sensitivity fails may occur when a transmission is weak to travel a wide distance. The highlight of TX power centric experiments is the discovery of the abilities of DR5 in terms of range, which could be from around 2 km or less with TX 2, to a maximum of around 4 km with TX 14 (Figure 9).  
A derivative from that is that DR5 must be allocated to the majority of the EDs on a network, with TX distribution to achieve less power consumption wherever is possible. In a lot of studies, SF7 allocation (DR5) is reported as an option that monopolizes the nodes where ADR is enabled. With our experiments we can conclude that DR5 is the best choice for medium-distance deployments (<4 km) because it achieves the shortest ToA which is directly related to interference, due to less possible channel occupation, and also to power consumption. For these reasons, it should be used in a higher ratio among other DRs and in wider range network deployments. Figures 10 and 11 demonstrate the DR range limits (as per the simulator implementation) for TX = 14 dBm and 2 dBm respectively.  
An additional conclusion that has been derived from our distance-based simulations is that as the SF increases the TX should be smaller in order to achieve common maintenance time between the EDs. If this does not happen, EDs with higher SF will need bigger batteries. This can be shown by the fact that, when the spreading factor is increased, the possibility of successfully receiving a packet on a weak link is also increased.  
Figure 10: Range of communication with TX power of 14 dBm.  
Figure 9: DR5 range of communication with different TX power.
# Scheduling a Single Machine with Primary and Secondary Objectives
## 3 Basic Definitions and Properties
By the above observation, if we merely increase the release time of any above available job \(j\) with \(d_{j}>d_{o}\) and that of any emerging job from set \(E\) to magnitude \(r(K)\), then the ED heuristics will create complementary schedule \(S_{E}\) for the modified instance. **Observation 3**. : _Let 1 be the delaying emerging job in schedule \(S\). Then, in complementary schedule \(S_{l}\), the earliest scheduled job of kernel \(K=K(S)\) starts at its release time \(r(K)\) and is preceded by a gap._  
Proof. : By the definition of complementary schedule \(S_{l}\), no job \(j\) with \(d_{j}>d_{o}\) may be included before kernel \(K(S)\) in schedule \(S_{l}\). As we have already observed, no available job \(i\) with \(d_{i}\leq d_{o}\) may be included before kernel \(K\) in complementary schedule \(S_{l}\). Then, clearly, the time interval before the earliest released job of kernel \(K\) will remain idle in complementary schedule \(S_{l}\), and the observation follows. For the problem instance of Example 1, we form set \(E\) with a single emerging Job 1 and obtain a complementary schedule \(S_{1}=(2,0)(3,30)(1,35)(4,40)(5,50)(6,60)(7,70)\), as depicted in Figure 2. There is a single kernel \(K_{2}=K(S_{1})\) consisting of Jobs 5, 6 and 7 with \(L_{\max}(S_{1})=L_{7,S_{1}}=80-52=28\) in schedule \(S_{1}\) (note that \(L_{3,S_{1}}=35-11=24\)). The proof of our next observation can be seen in [26]. **Property 1**. : _Suppose in schedule \(S_{E}\) there arises a (possibly zero-length) gap before kernel \(K=K(S)\). Then, that schedule is optimal for problem \(1|r_{j}|L_{\max}\) if \(K(S_{E})=K(S)\)._  
Note that the above property yields another halting condition for problem \(1|r_{j}|L_{\max}\). The following example illustrates that activation of an emerging job does not always yield an improvement in terms of the minimization of the maximum job lateness. **Example 2**. : _Now, we form an instance with six jobs as follows. Jobs 4 and 6 are kernel jobs, each defining a distinct kernel with the delaying emerging Jobs 3 and 5, respectively. Jobs 1 and 2 are other emerging jobs. All emerging jobs are released at Time 0, whereas kernel Jobs 4 and 6 are released at Times 20 and 50 respectively; \(p_{1}=p_{2}=p_{4}=p_{6}=5\), \(p_{3}=25\), \(p_{5}=20\). The due dates of emerging jobs are large enough numbers, Jobs 1 and 2 being slightly more urgent than the delaying emerging jobs 3 and 5; \(d_{4}=25\) and \(d_{6}=55\). Then ED heuristics will deliver solution \(S=(1,0)(2,5)(3,10)(4,35)(5,40)(6,60)\) with the delaying emerging Job 3. The lateness of Job 4 is 15, and that of Job 6 is 10; Job 4 forms the first kernel with the only overflow Job 4. In the complementary schedule \(S_{4}=(1,0)(2,5)(4,20)(3,25)(5,50)(6,70)\), there arises a gap \((10,20)\). In that schedule, a new kernel is formed by Job 6, which completes at Time 75 and has the lateness of 20. This example suggests that, instead of creating schedule \(S_{l}\), it could have been better to consider schedule \(S_{E}\), for a properly selected set \(E\)._
## 2 Current Capabilities of Modern FPGAs
### Summary  
Hence, despite the gap between ASIC and FPGA in terms of energy consumption, the general vendor's road-map can lead very soon to a situation where sensor nodes benefit from the high-processing capabilities at low-cost. This will be achieved by either using low-power FPGAs with a lower computation capability (Actel) or by high-performance FPGAs with an accurate control to save power. In this respect, the mid-range Actel IGLOO platforms have a static power consumption between 0.030 mW and 0.012 mW at 1.2 V (Table 3). On the other hand, the Xilinx Artix-7 board has a similar static power consumption as the old-fashioned Spartan-3. However, it provides large space and better optimized blocks (Table 1). As can be seen from Table 2, static power consumption of the Altera V series is higher than those of the 7 series from Xilinx. Compared with the total active power consumption of commercial sensor nodes (Table 4), the Altera V series as well as the Virtex 4, 5 and 6 are ill-suited in sensor node construction since the static power consumption ranges from 0.206 to 1.977 W.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Mode** & **Platform** & **Platform Details** & **Communication** & **Total Active Power (mW)** \\ \hline Mica & Microcontroller & ATMega103 @ 4 MHz & TR1000, ASK & 27 \\ Mica2 Dot & Microcontroller & ATMega128 @ 7.4 MHz & CC1000, FSK & 44 \\ Mica2 & Microcontroller & ATMega128 @ 7.4 MHz & CC1000, FSK & 89 \\ Tmote Sky & Microcontroller & TiMSP430 @ 8 MHz & CC2420, OQPSK & 32 \\ Telos B & Microcontroller & TiMSP430 @ 8 MHz & CC2420, OQPSK & 32 \\ Imote2 & Microprocessor & Intel PXA271 @ 13–400 MHz & CC2420, OQPSK & 86.8 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Details of commercial sensor nodes.  
Finally, it is worth noting that, recently, Lattice Semi presented its ultra-low power FPGA iCE40, based on the Silicon Blue iCE65 FPGA and built on a 40 nm CMOS process. The logic elements of the FPGA are made up of a 4-input LUT and a FF. The FPGAs are equipped with block RAM and one or two PLLs. There exists four models whose logic elements range from 640 to 7,680 and whose block RAM varies from 32 K to 128 K. However, as in the case of Actel IGLOO, they sacrificed specialized DSP at the cost of very small footprint and reduced power consumption.  
The next section presents the current challenges in sensor networks and link them to the features of modern FPGAs. Those can be addressed in the future by constructing and initiating new paradigms in sensor network architectures, based on an extra layer of intelligence via high performance capabilities.
## 4 Simulation Experiment  
The experiments are divided into two groups. The first involves a typical numerical simulation, primarily aimed at comparing the effectiveness of the proposed iFFDL-MFA-PID method to that of the FFDL-MFAC method. The second employs the proposed method to control an electric balanced forklift to validate its efficacy. Moreover, the FFDL-MFAC and traditional PID methods are included in the comparison analysis using indicators such as the MSE, MAE, settling time, and rise time. These comparisons aim to demonstrate the superiority of the proposed method.
# Random Frequency Division Multiplexing
## 6 Conclusions  
In this paper, we propose a novel RFDM scheme. Specifically, the signal is compressed to lower dimensions at the transmitter and can be reconstructed at the receiver. This means that we need fewer subcarriers or antennas to transmit the same amount of data. The DNN model is trained offline to learn the mapping function between an original signal. The BER performance of this proposed scheme is not better than the MMSE detection algorithm; however, we think it is a good attempt when the same quantity is transmitted, although only three quarters of the number of subcarriers of the MMSE scheme is used. It is still very important to improve the transmission speed. We will attempt to ascertain a better performance by avoiding the inversion of the channel matrix \(\mathbf{H}\), since its properties are directly considered and incorporated into DNN samples to train a good neural network model. There are too many training methods for neural network models, and ours used in this paper is too simple. We must optimize or find a better model to detect the signal. In addition, considering the properties of matrix \(\mathbf{A}\), the transmission scheme must be optimized comprehensively.  
Conceptualization and methodology, C.L., J.W. and Q.Z.; software, validation and writing--original draft preparation, C.L.; writing--review and editing, C.L., J.W. and Q.Z.; All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by NSFC under Grant 62171126 and in part by Guangdong Basic and Applied Basic Research Foundation 2022A1515140137.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflicts of interest.