esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Fault Diagnosis Method
### Time-Series Segmentation

Table 1: Performance comparison of different segmentation algorithms. Figure 2: Sliding Window based on Exponential Smoothing Segmentation Algorithm flow chart. Linear fit of the segmentation point and the original sequence. The formula for the mean \(\mu\) and the variance \(\sigma\) of the absolute prediction error is as follows:  
\[\mu=\frac{1}{m}\sum_{1}^{m}\Delta Err_{i},\ \ \sigma=\sqrt{\frac{1}{m}\sum_{i=1}^{m} \left(\Delta Err_{i}-\mu\right)^{2}} \tag{2}\]  
The algorithm repeats the loop until all data is processed. After initializing the parameters:  
1. Use a single exponential smoothing method to calculate the smoothed value \(s_{t}\) as the predicted value of the next time point \(t\);
2. Obtain the true value \(y_{t}\) of the time point \(t\), calculate the prediction error \(y_{t}-s_{t}\), and store the value in the vector V;
3. Calculate the mean \(\mu\) and standard deviation \(\sigma\) of V, and the standard deviation and mean are updated in real-time;
4. Determine whether the point is a split point by Equation (3). If not, continue to loop the next point; if yes, set the flag to cache the point at the same time. If the next point also satisfies the requirements of the split point, the previous point is stored in Seg, and the initial \(s_{0}\) of the split segment is reinitialized to continue the loop. \[P[\mu-2x\sigma<R<\mu+2x\sigma]\leq 1-p\] (3) where p is the split compression ratio of the time-series (p needs to be set in advance by user, \(0<p<1\)), R represents a random variable.
## 2 Overview
### Blockchain
#### 2.2.3 Types of Blockchains  
Blockchain technology has been employed in a variety of applications and areas. From the access point of view, there are three types of blockchains, each of which serves a distinct purpose for specific applications: public, private, and federated.  
Public blockchains have no centralized government or regulatory entities. The public chain has a high number of participating nodes, and its nodes' trust level is the lowest of the three blockchain classifications. The public blockchain is employed in various IoT applications, including smart agriculture, smart healthcare, smart traffic, etc. A public blockchain is described as scalable, dynamic, and decentralized, and it supports over 100,000 nodes. However, it has many drawbacks, such as high latency, low throughput, high electricity consumption, and high computing power consumption, and it is susceptible to 51% of attacks [81].  
Private blockchain: The private organization determines this type, and network nodes have varied permissions. The private blockchain is entirely controlled by a single entity, which has the authority to select the final consensus [82]. A private blockchain can reach consensus quickly and is able to resolve byzantine failures, but its complexity is high even if the number of nodes is low.  
Consortium blockchain (federated blockchains): Participation, read, and write permissions are all governed by a set of rules. The consortium blockchain has fewer nodes than the public chain, but there is some trust among the nodes. This type is mainly used in the financial (banking) industry [83] and is gaining momentum in production-oriented supply chains [84]. These are becoming connected with Central Bank Digital Currencies (CBDC) in order to facilitate flexible digital payments for industrial partners [85]. Consortium blockchain solves the Byzantine failure problems and contains multiple consistency algorithms. The main disadvantage of this type is its high complexity.  
Figure 2: Blockchain structure.
# Comparison of Automated Extraction Techniques for Volatile Analysis of Whole Milk Powder
## 3 Results and Discussion
### The Abundance of Volatile Organic Compounds in Whole Milk Powder by Each Extraction Technique  
The greatest abundance of VOCs extracted across all techniques was achieved by DI-HiSorb NS, independent of GC column polarity (Table 4). As mentioned, all other abundances were expressed as a percentage of the technique with the greatest abundance (i.e., DI-HiSorb NS equated to 100% abundance). The abundance of VOCs recovered by DI-HiSorb NS was also impacted by GC column polarity, as abundances achieved by the non-polar GC column were \(\sim\)41% lower than that achieved by the polar GC column (data not shown). Therefore, even though more VOCs were recovered using the non-polar GC column by DI-HiSorb NS, the total abundances were lower. The average total abundance for DI-HiSorb S, HS-HiSorb NS, HS-HiSorb S, HS-SPME NS, TD NS, HS-SPME S and  
Figure 3: Venn diagrams of the number of volatile organic compounds extracted by each technique, including commonalities (**a**) with salting out using the non-polar GC column, (**b**) without salting out using the non-polar GC column, (**c**) with salting out using the polar GC column, and (**d**) without salting out using the polar GC column. Direct Immersion HiSorb without salting out (DI-HiSorb NS), Direct Immersion HiSorb with salting out (DI-HiSorb S), Headspace HiSorb without salting out (HS-HiSorb S), Thermal Desorption without salting out (TD-NS), Thermal Desorption with salting out (TD S), Headspace Solid Phase Microextraction without salting out (HS-SPME NS), Headspace Solid Phase Microextraction with salting out (HS-SPME S).  
TD S for the non-polar GC column was 34.2%, 11.6%, 7.5%, 3.9%, 2.2%, 1.7%, and 1.3%, respectively. A similar trend was evident for the polar GC column, where DI-HiSorb S, HS-HiSorb NS, HS-HiSorb S, TD NS, TD S, HS-SPME NS, and HS-SPME S were 39.7%, 7.7%, 4.0%, 3.1%, 2.5%, 1.3%, and 1.2%, respectively, of that attained by DI-HiSorb NS (100%). The much greater abundance of the DI-HiSorb technique appears mainly due to the advantages of DI over HS, namely, the high capacity of the phase (which is much greater than SPME), and the selectivity of the phases' ability to extract lactones (more volume and quantities of lactones). The abundance of DI-HiSorb was impacted by salting out, as the addition of salt decreased abundances by approximately two thirds. The abundances of TD and HS-SPME were similar and slightly less than those achieved for HS-HiSorb. Therefore, the dynamic nature of TD, in comparison to the static HS-SPME and HS-HiSorb techniques, did not significantly impact VOC abundance. Differences in capacity and selectivity of the difference phases had a lesser impact than DI versus HS on abundance. A study comparing the SBSE (similar to DI-HiSorb), HSSE (similar to HS-HiSorb), and HS-SPME also found a similar trend for the extraction of fruit VOCs [28], in that SBSE extracted more and a greater abundance of VOCs than HSSE and HS-SPME.
# Enhancing Automatic Modulation Recognition for IoT Applications Using Transformers
## 2 Datasets  
* RadioML2016.10b [16]: This dataset is composed of ten modulations, including eight digital and two analog modulation types over SNR values ranging from \(-20\) dB to \(+18\) dB, in increments of \(2\) dB, i.e., \(\{-20,-18,\ldots,+16,+18\}\). These samples are uniformly distributed across this SNR range. The dataset, which includes a total of \(1.2\) million samples with a frame size of \(128\) complex samples, is labeled with both SNR values and modulation types. The dataset is split equally among all considered modulation types. At each SNR value, the dataset contains \(60{,}000\) samples, divided equally among the ten modulation types, with \(6000\) samples for each type. For the channel model, simple multi-path fading with less than five paths was randomly simulated in this dataset. It also includes random channel effects and hardware-specific noises through a variety of models,including sample rate offset, the noise model, center frequency offset, and the fading model. Thermal noise was used to set the desired SNR of each data frame.
* CSPB.ML.2018+ [17]: This dataset is derived from the CSPB.ML.2018 [17] dataset, which aims to solve the known problems and errata [18] with the RadioML2016.10b [16] dataset. CSPB.ML.2018 only provides basic thermal noise as the transmission channel effects. We extended CSPB.ML.2018 by introducing realistic terrain-derived channel effects based on the 3 GPP 5G channel model [19]. CSPB.ML.2018+ contains eight different digital modulation modes, totaling 3,584,000 signal samples. Each modulation type has signals with a length of 1024 \(IQ\) samples. The channel effects applied include slow and fast multi-path fading, Doppler, and path loss. The transmitter and receiver placements for the 3 GPP 5G channel model are randomly selected inside a 6 \(\times\) 6 km square. The resulting dataset covers an SNR (\(E_{s}/N_{0}\)) range of \(-20\) to 40 dB with the majority of SNRs distributed log-normally with \(\mu_{SNR}\approx 0.71\) dB and \(\sigma_{SNR}\approx 8.81\) dB using \(SNR_{dB}=10\log_{10}(SNR_{linear})\) as the log conversion method. The characteristics of these two datasets are detailed in Table 1.
## 1 Introduction
Moreover, most of such research is conducted with commercially available products and services with validated values, unlike the low/medium Technology Readiness Level (TRL) research projects. In this regard, this paper combined both the sense and respond (S&R) approach together with a broader survey as a validation of the value propositions. Indeed, the S&R approach has been applied in other sectors, such as manufacturing, processes, services, etc. [15; 16; 17]. S&R was proposed as one of the methodologies due to the fact that it enables analysis with smaller sample sizes of respondents. Moreover, it is easily adoptable and applicable in various sectors, as long as respondents are able to rate the four attributes, i.e., expectations of the value propositions, experiences, have knowledge about competitors and understand the general trend/direction of technology (see Table 1). S&R needs a clear and understandable attribute (factor to be considered). In this paper, value propositions which were well-designed and formulated based on the RESOLVD technological solutions were implemented. This paper considered the EU-co-funded low/medium Technology Readiness Level (TRL) research project (H2020 RESOLVD) to create a bi-directional communication between technology developers and DSOs for better value creation and to make proactive decisions. The aim of the H2020 RESOLVD project was to develop and validate, in field, a set of software and hardware technologies revolving around contributing to better management of a rural distribution grid towards increasing the hosting capacity for renewable generation. In particular, the technologies targeted an optimal power flow within the grid, exploiting advanced network monitoring devices and an innovative, hybridized battery-based energy storage system. The overall RESOLVD objectives and detailed architecture of the technology, developed together with the needs, involvement and expectations of stakeholders, are presented in [18]. In a broader sense, the paper paves a way towards open innovation by leveraging internal and external sources of ideas and taking those ideas (value propositions) to the market [19; 20].
# Design of a Cargo-Carrying Analysis System for Mountain Orchard Transporters Based on RGB-D Data
## 5 Discussion  
In recent years, researchers have realized the intelligent measurement of cargo using non-contact sensors, and the methods can be divided into three categories according to the types of sensors:  
1. Methods using infrared, laser, and RFID sensors. [PERSON] et al. [33] used laser sensors to measure the geometric dimensions of mechanical parts. [PERSON] [34] used RFID technology to locate cargo. [PERSON] et al. [14] measured the cargo volumes of train carriages based on lidar. This method has a simple hardware structure and a low cost, but it cannot obtain the classification of the cargo.
2. Methods that use RGB images to obtain the classification and location. For example, [PERSON] et al. [35] used an edge operator to position cargo for a vision robot application. [PERSON] et al. [36] obtained an edge contour using the Canny operator. [PERSON] et al. [12] used Faster-RCNN to detect overlapping cargo. Although the above methods can obtain the classification of cargo, it is difficult to obtain the height and volume.
3. Methods that use multi-sensor information fusion. Although the structures of sensors are different, the sensing data can be summarized as RGB and depth information. [PERSON] et al. [37] realized the detection overrun of railway cargo using vision and lidar technology. [PERSON] [38] obtained the classification and the spatial distance of an object using RGB-D data. [PERSON] et al. [39] used stereo vision technology to obtain classification and localization, and realized the dimension measurement of logistics materials.  
In summary, it is necessary to obtain the RGB and depth information of cargo to classify and obtain load volumes. Lidar has high precision and a fast measurement speed, but it is difficult to use widely due to its high cost. RGB-D depth cameras, such as Intel RealSense cameras, are not only affordable but also obtain accurate depth data. Therefore, we chose a RealSense camera as a sensing device, which was used to obtain the classification and load volume.  
Figure 15: Experimental results of overall method: (a) carrying one layer of fruit or baskets; (b) carrying two layers of fruit or baskets.
## 4 Results
### Ionospheric Data Analysis  
#### 4.3.1 Ionosonde Data Analysis  
The application of the ionosonde multiparametric approach [64; 65], that takes into account the variations of three ionosonde characteristics, h'Es, fbEs, and foF2, manually scaled from hourly ionograms of the AIS-INGV ionosonde of Rome [66], revealed a single anomaly occurring 9 days prior to the Fano EQ (i.e., on 31 October) at 06:00 UT (Table 1; Figure 12). Since the anomaly occurred on a day with the geomagnetic index Ap = 11 nT, the criterion was not strictly satisfied; however, we preferred to take this anomaly into consideration, given the moderate magnitude of the Fano EQ.  
As shown in Figure 13, it is worth noting that the anomaly is consistent with the relationship between \(\Delta\)T\(\cdot\)R and M previously found by the analysis of the most powerful Central Italian EQs since 1984 [66], with \(\Delta\)T representing the anticipation time (in days), R the distance (in km) between the epicenter and the ionosonde, and M the EQ magnitude.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Date** & **Hour(UT)** & **Ah’Es** & **\$fbEs** & **\$foF2** & **\$AT(Days)** & **Ap Index** & **AE Index** \\ \hline
31 October 2022 & 06–07 & 06–07 & 0.22 & 0.21 & 9 & =11 nT & \textless{}300 nT \\ \hline \hline \end{tabular}
\end{table}
Table 1: Anomaly detected at the ionospheric station of Rome from ionosonde measurements and possibly related to the Fano EQ.  
Figure 12: The anomaly observed 9 days before the 9 November 2022 Fano EQ using \(\Delta\)h’Es, \(\$\delta\)fbEs, and \(\delta\)foF2 variations, along with 3 h Kp index values given as a reference of geomagnetic activity.
# Feature-Selection-Based DDoS Attack Detection Using AI Algorithms
## 2 Related Works and Contribution  
In order to discriminate between legitimate traffic and DDoS attack traffic, a previous paper investigated various machine learning techniques for DDoS attack detection and suggests leveraging innovative features. Among the machine learning methods used to identify DDoS attacks on SDN are Logistic Regression, Support Vector Classifier, K-Nearest Neighbor, Random Forest, Ensemble Classifier, and Artificial Neural Network. Table 1 provides a list of the features currently in use and the ML models that have been deployed.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Paper** & **AI Techniques** & **Dataset** & **Features** \\ \hline
[9] & NB, SVM, and NN & Real-time dataset from TCP traffic & Number of hosts connected per second \\
[10] & DT & Self-generated traffic & Protocol and service type, flag, TTL, and source/destination IP \\
[11] & KNN, DT, and NN & CAIDA 2007 and self-generated traffic & Number of ports per IP, the entropy of ports per IP, and number of ICAMP packets per IP \\
[12] & SVM, KNN, and RF & NSL-KDD and self-generated & Extracted features 27 and 40 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Existing algorithms to protect SDN against DDoS attacks using AI Models.
## 4 Simulation Results
### System Performance
\begin{table}
\begin{tabular}{c c c} \hline \(\mathbf{N}\;\mathcal{G}f\) & \(\left\|\mathbf{R}^{-1}\right\|\) \\ \hline \multirow{8}{*}{32} & \(f=1\) & 0.0489 \\  & \(f=2\) & 0.0119 \\  & \(f=3\) & 0.0051 \\  & \(f=5\) & 0.0016 \\  & \(f=10\) & 1.8127 \(\times\) 10\({}^{-4}\) \\  & \(f=1\) & 0.3967 \\  & \(f=2\) & 0.0986 \\
64 & \(f=3\) & 0.0434 \\  & \(f=5\) & 0.0151 \\  & \(f=10\) & 0.0032 \\  & \(f=1\) & 1.3427 \\  & \(f=2\) & 0.3348 \\
96 & \(f=3\) & 0.1482 \\  & \(f=5\) & 0.0526 \\  & \(f=10\) & 0.0123 \\  & \(f=1\) & 3.1839 \\  & \(f=2\) & 0.7948 \\
128 & \(f=3\) & 0.3524 \\  & \(f=5\) & 0.1259 \\  & \(f=10\) & 0.0903 \\ \hline \end{tabular}
\end{table} 
Table 3: Norm of the matrix \(\mathbf{R}^{-1}\) at different carrier frequencies.
# Sensor Network Environments: A Review of the Attacks and Trust Management Models for Securing Them
## 3 Trust Model Concepts
### Environments
Sensor networks often have a central node. Trust calculations for the sensor nodes are often performed in this central node or base station, which has more power and resources available to it. This type of topology is well-suited to a trust model, and some very good results can be achieved by introducing a trust model to the network. There are many sub-types of WSNs, including homogeneous and heterogeneous, underwater acoustic networks [23; 28], large scale networks [29], hierarchical networks [12] and cluster-based networks [3; 13]. Each type of network will have slightly different requirements and topologies. Specific trust models can sometimes be used in more than one specific type of WSN, if they incorporate features that allow for flexibility in their implementation (more on these features is provided in Section 6). VANETs: Vehicular ad hoc networks [30; 31; 32] are comprised of vehicles, such as cars, trucks, buses, etc., which accept and receive data from one another and from roadside units (RSUs). They are used in smart cities for applications such as traffic monitoring and optimizing, accident prevention and early warning systems. These networks are much more dynamic, with vehicle nodes moving in and out of the network freely and the nodes not having a fixed point, which, of course, brings with it potentially malicious nodes entering and leaving freely. With these dynamic networks, some unique aspects can be added to their trust models, such as distance-checking as a plausibility check on the data [30; 31], among others. Unmanned aerial systems: This area is quite similar to a VANET. A trust model is applied to one in [33]. These systems can be thought of a cross between a VANET and IoT-type network, with the nodes, in this case, being static in the network but dynamic in terms of their positions, as they are traveling. Cyber-physical manufacturing: [34, 35] This type of network can be defined as a specific use case of the IoT or WSNs and is comprised of sensors and devices used to control critical infrastructure in factories. Often, these networks are more secure from the physical attacks of nodes, with only authorized personnel allowed enter the factory/manufacturing area and no new nodes allowed to enter the network without authorization. However, the nodes are still at risk of being compromised, and trust models offer an effective lightweight solution suitable to this environment.
# A Survey on Energy-Efficient Design for Federated Learning over Wireless Networks
## 5 Improving Energy Efficiency in FL Using Emerging Technologies
### Intelligent Reflecting Surfaces  
IRS is an innovative technology that is set to revolutionize wireless communication networks by enabling intelligent and adaptive control over electromagnetic wave propagation [128; 129]. IRS utilizes a reconfigurable array of metamaterial elements to dynamically enhance signal strength, extend network coverage, mitigate interference, and enhance spectral efficiency, all while being energy-efficient and cost-effective. When integrated with FL, IRS amplifies its capabilities, creating a synergistic framework that enables secure, energy-efficient, and reliable communication in wireless networks [130; 131]. This combination has attracted significant attention from researchers, particularly in developing energy-efficient FL systems supported by IRS [132; 56; 133; 134].  
In [132], the authors introduced a HAPS communication system that utilizes rate-splitting multiple access and UAVs equipped with IRS as relay nodes to enhance the performance of integrated satellite-aerial-terrestrial relay networks. To optimize system energy efficiency, a multiobjective optimization problem was formulated, and the challenging nonconvex nature of the problem was tackled using an approach called access-free federated deep reinforcement learning. In [133], the authors proposed a scheme combining wireless power transfer, information transmission, and solar energy harvesting in IoT units, supported by UAVs and IRS. They introduce a multiagent federated reinforcement learning algorithm to optimize parameters and maximizes energy efficiency. Furthermore, in [134], the authors investigated an indoor millimeter-wave (mmWave) downlink communication system, where a wireless AP is supported by multiple IRSs. To adapt to the indoor environment, they developed an energy-efficient FL framework that enables the parallel configuration of multiple IRSs.