esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### Signature-Based Detection for Encrypted Malicious Traffic  
Signature-based inspection is one of the earliest approaches for detecting malicious traffic, relying on matching packet content against predefined byte patterns of known attacks [3]. While effective in plaintext environments, this method faces fundamental limitations in encrypted scenarios, where payload content is inaccessible to traditional pattern matching [2]. As a result, its detection capability significantly degrades when applied to TLS/SSL-protected traffic. Moreover, the effectiveness of signature-based detection heavily depends on large and continuously updated signature libraries [19,20]. In most systems, signatures are manually constructed and maintained, leading to slow adaptation to evolving threats and leaving detection gaps for zero-day attacks. Even recent enhancements such as abstract signature codes, dynamic rule updates, and multi-policy engines only marginally improve flexibility and do not resolve the inherent inability to handle unseen or encrypted threats [21]. In summary, while signature methods offer fast and interpretable detection with low FPR for known attacks [22], they lack generalization and scalability. Their limitations in encrypted traffic environments underscore the need for alternative approaches that can infer malicious intent from observable metadata or behavioral patterns [23].
# Trajectory Tracking Outer Loop Regressor-Based Adaptive Controller for a Quadrotor
## 4 Experimental Results  
The functionality of the proposed controller was demonstrated through an experimental test. Furthermore, its performance was assessed through comparison with other controllers reported in the literature. The comparison consists of achieving the trajectory tracking task. Four controllers were used in the comparison, an outer-loop PD scheme, a neural network-based scheme with integral sliding modes, an adaptive generalized regression neural network-based scheme, and an adaptive neural network-based scheme.
# Reviews Based on the Reconfigurable Intelligent Surface Technical Issues
## 3 RIS Field Trials  
### RIS Performance Evaluation Parameters [28]  
#### 3.1.1 Rate/Throughput  
The first consideration in RIS performance evaluation is data throughput, which should be considered from the user and system perspectives. From the user perspective, user throughput is defined as the number of correctly received bits, i.e., the number of bits contained in the Service Data Unit (SDU) sent to Layer 3, and averaged over a period of time. In an RIS-enabled system, correctly received bits may be received directly from the BS/user in the case of DL/UL, or from RIS, or a combination thereof. If the total bandwidth is aggregated across multiple bands (one or more transmission and reception point (TRxPs) layers), the user throughput is summed across bands. From a system perspective, system throughput is the total throughput of all users.
# A Survey of Applications of Deep Learning in Radio Signal Modulation Recognition
## 3 Radio Modulation Recognition Methods Based on Deep Learning  
Recently, a variety of radio AMR methods based on DL have been proposed by the academic community. After investigation, it is found that the commonly used DL models are CNN, RNN, LSTM, AE, DBN, etc. In addition, there are some hybrid DL models. Although they are not commonly used, they have great advantages, such as, Deep Multiscale Convolutional Neural Network (DMCNN), Convolutional Long Short-Term Deep Neural Network (CLDNN). We classify literature according to the different DL models they have used. According to the literature survey, major papers of DL models in radio modulation recognition are shown in Table 1 and the applications of different models will be described below in detail.
# Distributed Localization with Complemented RSS and AOA Measurements: Theory and Methods
## 5 Summary and Future Prospects
As a direct consequence, this will stimulate researchers to develop novel signal processing approaches for efficient (accurate and fast) distributed localization solutions. Furthermore, we expect that all these activities will require some level of security; hence, we expect that secure localization (localization in the presence of one or more malicious/corrupted nodes) will attract many research interest in the upcoming years. This is because today's localization systems are susceptible to location spoofing, where devices can deceive other devices with regards to their own locations or can manipulate the measured locations of other devices [73]. Finally, we saw that node cooperation does not bring benefit by default for any of the considered estimators (e.g., when \(M\) and/or \(R\) (m) is increased). From the localization accuracy perspective, this result is very important since it indicates that there is still room for improvements, as it was also confirmed by comparing the methods against the theoretical bound. Essentially, this might imply that _unlimited_ node cooperation is not the solution for the localization problem at hand, or simply that one needs to come up with a better way to exploit the additional information due to node cooperation in order to benefit from it. All authors were involved in the mathematical developments and writing of the paper. The computer simulations were carried out by S.T. All authors have read and agree to the published version of the manuscript. All authors have read and agreed to the published version of the manuscript. This work was partially supported by Fundacao para a Ciencia e a Tecnologia under Projects UID/EEA/00066/2019, foRESTER PCIF/SSI/0102/2017, and IF/00325/2015. The authors declare no conflict of interest.
## 2 Materials and Methods
### Methodology
#### 2.3.2 Theil-Sen Median Trend Analysis and Mann-Kendall Test  
The combination of Theil-Sen trend analysis method and Mann-Kendall test method is widely used in long time series analysis of vegetation on pixel scale [7; 17; 44]. Theil-Sen  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **Year** & **Cropland** & **Forest** & **Grassland** & **Wetland** & **Water Body** &
\begin{tabular}{c} **Impervious** \\ **Surface** \\ \end{tabular} & **Bare Area** \\ \hline \multirow{3}{*}{Total area} & 2000 & 5667.19 & 7158.44 & 1712.97 & 8.18 & 255.93 & 1589.96 & 0.23 \\ \cline{2-8}  & 2010 & 5342.04 & 7332.59 & 1321.45 & 9.50 & 151.19 & 2235.85 & 0.30 \\ \cline{2-8}  & 2020 & 3902.51 & 7388.60 & 1371.74 & 11.87 & 238.33 & 3475.76 & 4.11 \\ \hline Neighborhood weights & 0.06 & 0.54 & 0 & 0.38 & 0.28 & 1 & 0.38 \\ \hline Land demands & 2030 & 2927.05 & 7407.67 & 1395.65 & 12.04 & 307.42 & 4338.19 & 4.89 \\ \hline \hline \end{tabular} Note: Unit of land use type unit: km\({}^{2}\); neighborhood weights have no unit.  
\end{table}
Table 2: Total area, Neighborhood weights and land demands of land use in Beijing.  
Figure 2: Technical flow chart.  
trend analysis does not require data to obey specific distribution characteristics and is less affected by outliers. This method quantifies the change trend by calculating the median slope of \(n(n-1)/2\) data combinations:  
\[\beta=median\left(\frac{NPP_{j}-NPP_{i}}{j-i}\right),\ 2000\leq i<j\leq 2020 \tag{4}\]  
where \(\beta\) is the changing trend of NPP, \(\beta>0\) indicates an upward trend, while \(\beta<0\) indicates a downward trend; \(NPP_{i}\) and \(NPP_{j}\) represent the NPP values of pixels in year \(i\) and year \(j\), respectively.  
The Mann-Kendall test method is a nonparametric statistical test used to judge the significance of trends, which does not require samples to follow a certain distribution and is not disturbed by a few outliers [44]. This method is used to evaluate the significance of the NPP trend, and the formula is as follows:  
\[S=\sum_{j=1}^{n-1}\sum_{i=j+1}^{n}\text{sgn}\big{(}NPP_{j}-NPP_{i}\big{)},\ 2000 \leq i<j\leq 2020 \tag{5}\]  
\[\text{sgn}\big{(}NPP_{j}-NPP_{i}\big{)}=\left\{\begin{array}{c}1, NPP_{j}- NPP_{i}>0\\ 0, NPP_{j}-NPP_{i}=0\\ -1, NPP_{j}-NPP_{i}<0\end{array}\right. \tag{6}\]  
\[Z=\left\{\begin{array}{c}\frac{S-1}{\sqrt{var(S)}},\ S>0\\ 0,\ S=0\\ \frac{S-1}{\sqrt{var(S)}},S>0\end{array}\right. \tag{7}\]  
\[var(S)=\frac{n(n-1)(2n+5)}{18} \tag{8}\]  
where \(S\) is the test statistics; sgn is a sign function; \(n\) is the length of the time series; \(Z\) is the standardized statistics. The trend test method is the null hypothesis. Under a given significance level \(\alpha\), \(\pm Z_{1-\alpha/2}\) is normalized normal deviation. When \(|Z|>Z_{1-\alpha/2}\), refuse the null hypothesis and it indicates that there are changes at level \(\alpha\). On the contrary, the change is not significant. In this paper, significant change was defined when the trend was significant at \(\alpha\) = 0.05. When \(\alpha\) = 0.01, it was extremely significant.
## 3 Dark Current Noise Correction Experiment for the LWIR QWIP System  
### The Validity of Dark Pixels  
In the experiment, the detection system was placed on the platform at an approximate 5 cm distance from the blackbody, with the window parallel to the blackbody's emission surface. The cooler was powered on and the blackbody's temperature was adjusted when the focal plane reached the target temperature. The system acquired data at each blackbody temperature. The calibration relationship between imaging pixels and dark pixels was calculated and is shown in Figure 7; the vertical coordinate Grey value is the regional pixel mean response of dark pixels and imaging pixels and the horizontal coordinate is the blackbody's radiation flux. Imaging pixels had good sensitivity; the dark pixels' response was negligibly related to the target temperature. Experimental results verify the functional validity of dark pixels; their response can be used as a reference value for the real-time dark current level at the focal plane.
## 3 Preliminaries  
### Variational Formulation  
Variational data assimilation (VarDA) estimates the optimal analysis state \(\mathbf{x}^{a}\) by minimizing the cost function:  
\[J_{cost}(\mathbf{x})=\frac{1}{2}(\mathbf{x}-\mathbf{x}^{b})^{T}\mathbf{B}^{-1} (\mathbf{x}-\mathbf{x}^{b})+\frac{1}{2}(\mathbf{y}^{o}-\mathcal{H}(\mathbf{x }))^{T}\mathbf{R}^{-1}(\mathbf{y}^{o}-\mathcal{H}(\mathbf{x})). \tag{1}\]The nonlinear observation operator \(\mathcal{H}\) is locally linearized around \(\mathbf{x}^{b}\) as follows:  
\[\mathcal{H}(\mathbf{x})\approx\mathcal{H}(\mathbf{x}^{b})+\mathbf{H}(\mathbf{x}- \mathbf{x}^{b}), \tag{2}\]  
where  
\[\mathbf{H}=\frac{\partial\mathcal{H}(\mathbf{x})}{\partial\mathbf{x}}\Big{|}_ {\mathbf{x}=\mathbf{x}^{b}}. \tag{3}\]  
The observation discrepancy expands as follows:  
\[\mathbf{y}^{o}-\mathcal{H}(\mathbf{x})=\mathbf{y}^{o}-\mathcal{H}(\mathbf{x}^ {b})-\mathbf{H}(\mathbf{x}-\mathbf{x}^{b}), \tag{4}\]  
Thus, the gradient of the cost function is expressed as follows:  
\[\
abla\!J_{cost}(\mathbf{x})=\mathbf{B}^{-1}(\mathbf{x}-\mathbf{x}^{b})- \mathbf{H}^{T}\mathbf{R}^{-1}(\mathbf{y}^{o}-\mathcal{H}(\mathbf{x}^{b})- \mathbf{H}(\mathbf{x}-\mathbf{x}^{b})). \tag{5}\]  
Setting \(\
abla\!J_{cost}(\mathbf{x})=0\) results in the following analysis update:  
\[\mathbf{x}^{a}=\mathbf{x}^{b}+[\mathbf{B}^{-1}+\mathbf{H}^{T}\mathbf{R}^{-1} \mathbf{H}]^{-1}\mathbf{H}^{T}\mathbf{R}^{-1}(\mathbf{y}^{o}-\mathcal{H}( \mathbf{x}^{b})). \tag{6}\]  
Below Table 1 is the summary of mathematical notations used in Section 3.
# An Overview of Emergency Communication Networks
## 5 Sea Ad Hoc Network (SANET)
#### 3.1.4 Wireless Private Networks
LTE networking has the following advantages: it has a fast wireless network communication rate and high throughput, which can provide strong support for extensive services; with a slight delay, it can meet most application scenarios having high real-time requirements; and the LTE base station can be conveniently deployed and used to supplement the existing wireless communication infrastructure [97]. LTE is mainly applied in private networks, such as for emergency communication, smart construction sites, and forest fire prevention. In critical areas, the 4G-LTE broadband cluster is used for signal coverage, and the high bandwidth of the LTE system is used to realize video and high-speed data interaction and other services. In recent years, 4G-LTE and 5G technologies have been increasingly used in search and rescue work in disaster areas. [PERSON] et al. [98] designed an Aerial Base Station (AeNB) based on an LTE advanced network that could communicate with tethered balloons providing LTE payloads. The representative technology of the 4G broadband cluster is the B-TrunC standard, which has the characteristics of flexible bandwidth, high spectral efficiency, low delay, and high reliability, and meets the requirements of critical voice communication, positioning, multimedia information, real-time video, large bandwidth data, and multimedia command and scheduling. [PERSON] et al. [99] proposed a communication system that integrates WiMAX and Wi-Fi, which can restore communication services in emergencies such as floods and other disaster scenarios. With the development of the private network industry, especially in the field of public security, the evolution of private network communication based on LTE technology has become a general trend. However, for a long time in the future, private network broadband clusters will be influenced and limited by spectrum policy, technical standard maturity, network security and reliability, user cost, and many other factors. Therefore, the popularization of broadband cluster technology cannot be achieved overnight. It is necessary to integrate broadband and narrowband technology according to the actual situation, and finally transition to PDT + LTE mode. The PDT cluster private network provides emergency voice services, while the LTE public network provides data services. The PDT narrowband private network is the most economical and efficient way for private network users to evolve to broadband to supplement image, video, and high-speed data transmission capabilities through the LTE public network.
## 3 Results and Discussion
### Agoronomic Potential
#### 3.1.2 Macronutrient in Biosolids and the Amount Required by Different Agricultural Crops
[53], in a field investigation with long-term application of biosolids, documented that the K deficiency in the soil was evident during 17 years of application of biosolids. Moreover, the barley and corn crops obtained reduced growth, with symptoms of nutritional deficiency of K, such as white necrotic lesions and brown necrotic spots, similar to the K deficiency in all the cycles grown with biosolids. However, when there was the addition of inorganic K in the biosolids, there was growth improvement in both crops, high development, dry matter, and an increase in the K content in the soil. Therefore, researchers suggest that it is necessary to complement the K contents with mineral fertilizers, in that the biosolids require additional K fertilization already in the second or third growing season for maintaining an acceptable K concentration in the plants [54]. As regards Ca, the corn crop demand was met at 18%, rice 12%, wheat 48%, forage grasses 67%, sugar cane 3%, beans 6%, coffee 5%, and cotton 22%. As to Mg, the percentage meeting crop demand was 2% for corn, 7% rice, 7% wheat, 21% forage grasses, 1% sugar cane, 3% beans, 2% coffee, and 5% cotton. At last, S with an average concentration of \(11.13\pm 1.76\) kg S ton\({}^{-1}\) obtained the best result among the macronutrients since this nutrient is available in large quantities in the biosolids and the needs of the crops are lower for S, relative to the other macronutrients. The nutrient met demands of forage grass, coffee, and cotton at >100%, and 86% in corn, 93% rice, 25% sugar cane, and 43% in beans. Fertilizing tropical soils with S is of extreme importance because there exist high growth demands in areas naturally deficient in this macronutrient that experience an increase in the demand for greater productivity [55]. Results in the present study corroborate others that show evidence that biosolids can replace or complement mineral fertilizers for N and P [15; 47; 48; 49; 56], as well as Ca, Mg, and S, with mineral K complementation [17; 53]. Furthermore, the biosolids of IBRS WWTP in the present research and of other tropical climate WWTPs obtain a content of nutrients higher than the organic waste of animals found in the literature. Therefore, the doses to be applied to the soil must take into consideration the availability of nutrients and the characteristics of the soil intrinsic to each culture [27].
# Path Diversity Improved Opportunistic Routing for Underwater Sensor Networks
## 1 Introduction
Generally, OR protocols can provide multiple potential candidates for packet forwarding. Void nodes can take themselves out of the packet forwarding to provide the opportunity for other available nodes [20], which is called passive participation void-handling technique [19]. Thus, a well-designed candidates location is more important to improve the path reliability. In this paper, we propose a path diversity improved strategy in candidates selection procedures. This strategy takes the impact of channel quality on packet reception and the influence of candidates  
Figure 1: Opportunistic routing diagram. location distribution on path connectivity into account. Our main goal is finding an available candidate set with the maximum advancements and diverse forwarding path towards the next-hop to improve packet forwarding efficiency. Then, we propose two effective location-free routing protocols, position improved candidates selection (PICS) and position random candidates selection (PRCS), for UWSNs based on the path diversity improved strategy. PICS and PRCS adopt different candidate selection range, which leads to different candidate set selection algorithm and candidate coordination mechanism. Both PICS and PRCS employ the sender-based scheme in candidate set selection. The current forwarder selects a candidate set at each hop with the information provided by the periodic beacon and broadcasts the packets to them. The routing information, including candidate members and coordination scheme, is embedded in the packet header. When the neighbor nodes receive the packets, they first check the header of the packet. If they belong to the current candidate set, they will forward the packets based on the coordination scheme. Otherwise, they remain silent. This process is repeated until the packets arrive at the sink nodes. Finally, we show the path diversity improved strategy can efficiently improve routing performance by simulation: PICS and PRCS outperform previous solutions, i.e., DBR, EPA (Hydrocast) and OVAR, in terms of packet delivery ratio, energy efficiency and end-to-end delay. The rest of the paper is organized as follows. In Section 2, we briefly review some related works. We introduce the path diversity improved opportunistic routing in detail in Section 3, including path diversity improved forwarding mechanism and the candidate coordination methods. The simulation results are shown in Section 4. In Section 5, we conclude this paper.
# DERN: Deep Ensemble Learning Model for Short- and Long-Term Prediction of Baltic Dry Index
## 4 Experiments
### Long-Term Prediction
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Portion** & **Accuracy Measure** & **Model** & \(\mathbf{t_{1}}\) & \(\mathbf{t_{2}}\) & \(\mathbf{t_{3}}\) & \(\mathbf{t_{4}}\) & \(\mathbf{t_{5}}\) & \(\mathbf{t_{6}}\) & \(\mathbf{t_{7}}\) \\ \hline \multirow{9}{*}{RMSE} & ARIMA & 739.21 & 889.88 & 1008.98 & 1208.98 & 1568.72 & 1759.66 & 1973.26 \\  & MLP & 131.09 & 197.15 & 229.82 & 267.11 & 299.56 & 337.98 & 353.31 \\  & Deep RNN & 90.57 & 157.58 & 212.35 & 255.03 & 288.60 & 310.13 & 324.06 \\  & LSTM & 73.67 & 143.11 & 200.43 & 249.52 & 290.83 & 317.09 & 329.46 \\  & GRU & 77.74 & 150.71 & 214.46 & 270.16 & 323.02 & 364.61 & 390.65 \\  & & DERN & **69.53** & **143.08** & **198.09** & **240.92** & **287.43** & **300.41** & **320.04** \\ \hline \multirow{9}{*}{70–30\%} & ARIMA & 699.93 & 734.08 & 962.73 & 974.23 & 999.00 & 1020.87 & 1127.11 \\  & MLP & 90.62 & 147.18 & 179.69 & 199.04 & 244.42 & 261.81 & 280.00 \\  & Deep RNN & 63.38 & 109.58 & 151.40 & 186.30 & 212.95 & 232.66 & 250.13 \\  & LSTM & 57.11 & 101.85 & 142.55 & 181.92 & 211.23 & 230.28 & 243.53 \\  & GRU & 56.63 & 107.01 & 153.64 & 196.62 & 232.58 & 264.67 & 290.33 \\  & DERN & **56.00** & **99.93** & **136.03** & **167.39** & **201.04** & **225.01** & **237.33** \\ \hline \multirow{9}{*}{MAPE} & ARIMA & 98.01 & 112.78 & 134.49 & 156.03 & 175.53 & 187.67 & 199.90 \\  & MLP & 14.91 & 15.91 & 18.93 & 22.09 & 250.0 & 26.22 & 29.34 \\  & Deep RNN & 7.45 & 12.17 & 16.48 & 19.99 & 21.93 & 24.14 & 31.27 \\  & LSTM & 5.37 & **10.34** & 14.40 & 18.44 & 21.51 & **23.41** & 24.79 \\  & GRU & 5.52 & 10.81 & 15.62 & 20.07 & 23.82 & 27.05 & 29.73 \\  & DERN & **4.05** & 10.87 & **13.96** & **18.01** & **20.11** & 23.48 & **23.69** \\ \hline \multirow{9}{*}{80–20\%} & ARIMA & 699.23 & 887.92 & 1004.21 & 1220.89 & 1400.67 & 1502.77 & 1701.12 \\  & MLP & 159.01 & 181.25 & 209.92 & 220.90 & 250.11 & 270.99 & 300.86 \\  & Deep RNN & 87.46 & 123.43 & 166.68 & 192.01 & 219.95 & 237.83 & 255.86 \\  & LSTM & 62.74 & 117.33 & 155.29 & 186.05 & 209.26 & 227.93 & 242.47 \\  & GRU & 61.07 & 117.77 & 156.74 & 188.74 & 214.68 & 237.32 & 257.57 \\  & DERN & **60.94** & **102.33** & **154.21** & **178.00** & **208.11** & **217.43** & **240.12** \\ \hline \multirow{9}{*}{80–20\%} & ARIMA & 900.06 & 1001.22 & 1202.49 & 1442.11 & 1558.00 & 1872.71 & 1991.91 \\  & MLP & 69.91 & 198.18 & 218.31 & 236.16 & 257.12 & 277.09 & 296.66 \\  & Deep RNN & 69.08 & 93.11 & 129.07 & 153.14 & 177.12 & 191.42 & 211.68 \\  & LSTM & 55.76 & 86.98 & 118.25 & 147.24 & 168.10 & 182.84 & 194.88 \\  & GRU & 47.63 & 87.48 & **118.09** & 146.51 & 170.46 & 187.73 & 207.20 \\  & DERN & **46.51** & **84.35** & 118.76 & **146.13** & **165.10** & **180.33** & **194.00** \\ \hline \multirow{9}{*}{MAPE} & ARIMA & 102.90 & 141.08 & 185.03 & 198.01 & 210.77 & 240.11 & 290.19 \\  & MLP & 17.68 & 26.71 & 38.14 & 40.11 & 43.03 & 45.32 & 47.71 \\  & Deep RNN & 8.48 & 13.71 & 16.04 & 20.88 & 22.03 & 26.12 & 30.80 \\  & LSTM & 5.52 & 10.19 & 14.02 & 17.87 & 20.77