esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Observations and Recommendations
### Recommendations and Research Challenges
#### 7.3.4 Online Learning  
The lack of labelled training data in IWSNs means that supervised learning as a classification problem will not be feasible for most applications. In cases where large training sets are available, a major drawback is that the algorithm would need to be retrained every time the normal system behaviour changes. Given the dynamic nature of IWSNs this is likely to happen often within the network lifetime. Online learning would thus be a feasible solution in the IWSN context. To achieve this detection generality however comes with its own challenges. For instance, the use of complex malicious attacks, as discussed previously, could result in the system learning new erroneous behaviour if the attacker remains undetected for a long enough period of time. This means that this detection generality could come at the cost of detection accuracy. Future work into this topic will have to consider the trade-off between the two and determine how much training data and which features are required to get the best possible outcome.
## 2 SGX-Based TEE
### Basic Concept About SGX  
SGX is one of the widely used TEE technologies introduced by Intel Corporation in 2013. Through the combination of software and hardware, a special memory space called an enclave was built to protect users' sensitive data and code [20]. Entering and exiting the enclave requires special hardware instructions and involves complex security checks. SGX focuses on runtime data security, and even privileged software such as the OS or hypervisor cannot access data inside the enclave when the program is running. Isolated execution and attestation [29] are two important characteristics of SGX. Isolated execution is the guarantee that secret data will not be stolen by other software or programs and attestation is a common method for devices to prove their identities to each other in cloud computing scenarios. These two characteristics are the core part of SGX and also the security guarantee of SGX in IoT environments.
# Robot Delay-Tolerant Sensor Network for Overhead Transmission Line Monitoring
## 4 Results
### Results for the Default Parameters  
For the scenarios, the coverage area of the network is 74,500 \(\times\) 143,400. All nodes have wireless communication capabilities similar to IEEE 802.11n, and the transmission rate is 8 Mbps. The default transmission range of the WCNs is 10 km. The SNs and the robots' default transmission range are 3 km. The default buffer size of nodes is 500 M. In practice, the number of robots is limited by cost, and the simplified motion model can not only improve the efficiency, but also do not affect the results. The default number of robots is 5. The default motion model is BPRW, with a movement rate of 3-5 m/s. The initial power of both the WCN and the SN are 100 Ah, and the robots are 57 Ah. The message generation of each node follows the Poisson process. The message arrival time is 60-120 s in the robot, and 1800-3600 s in the SN, with a message size of 2 m. The messages are directly delivered to WCNs or forwarded by other nodes. The above parameters are based on actual robots and transmission lines. Each simulation's run time is 86,400 s, where the 24-h running results of actual network was simulated. 200 simulations were performed for each scenario. The default simulation parameters are shown in Table 1. All routing parameters have been optimized, see Section 4.2 for more details.  
Figure 9: RDTSN scenarios map. (**a**) Scenarios map from Open Street Map; (**b**) simulation interface of the RDTSN in the ONE (Opportunistic Network Environment) simulator.  
The statistics on the network message delay, delivery distance and hop count were gathered under the default conditions. The results and statistical analysis data are shown in Figures 10 and 11. The amount of message delivered sharply dropped with the increase in node distance, as the distance exceeded 80 km. The average hop count in the network was about 2.7, and most of the hop count in the distribution was 2. The network's average message delay was about 7600 s, and the nodes with lower delay were denser.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Default Value** \\ \hline Network area size (m \(\times\) m) & 74,500 \(\times\) 143,400 \\ Simulation run time (s) & 86,400 \\ Number of Robots & 5 nodes \\ Number of SNs & 7 nodes \\ Number of WCNs & 5 nodes \\ R\({}_{\rm C}\) (m) & 10,000 \\ R\({}_{\rm S}\) (m) & 3000 \\ Movement rate of Robot (m/s) & 3-5 \\ T\({}_{\rm pause}\) (s) & 10–30 \\ Buffer size (M) & 500 \\ Message size (Kb) & 2000 \\ Robot message interval (s) & 60, 120 \\ SNs message interval (s) & 1800, 3600 \\ Source nodes & Robot, SNs \\ Destination nodes & WCNs \\ Movement model & BPRW \\ Alpha and Beta of DTMA & 0.78, 0.22 \\ Beta of Prophet V2 & 0.36 \\ Spray and Wait Router Copies & 8 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Default parameters of the simulation.  
Figure 10: Results of the scenario of default value, delivery time and distance at message send and hop count.
# AI-Based Analysis of Policies and Images for Privacy-Conscious Content Sharing
## 3 Information Retrieval for Privacy Policies
### Feature Engineering  
Of course, the features that we use to classify paragraphs are built based on the textual content of the paragraph. The features of the paragraphs are extracted using the bag of words (BoW) model [23]. In the bag of words model, each word corresponds to a feature, and the feature value is computed based on the number of occurrences of the respective word. To build this structure, we use the _term frequency-inverse document frequency_ (TF-IDF) [23] vector representation of the document, as defined below. Note that in our case, the document corresponds to a paragraph.  
* Term frequency formula: \[\mathit{tf}(w)=\frac{\mathit{doc.count}(w)}{\mathit{doc.total}()},\] (1) where _doc.count(w)_ is the number of occurrences of the word \(w\) in the document, and _doc.total()_ is the total number of words in the document.
* TF-IDF formula: \[\mathit{idf}(w)=\log\frac{|\,\mathrm{D}\,|}{|\{d:w\in D\}|},\] (2) \[\mathit{tfidf}(w)=\mathit{tf}(w)\cdot\mathit{idf}(w),\] (3) where \(|D|\) is the total number of documents, and \(|\{d:w\in D\}|\) is the number of documents containing \(w\).  
The TF-IDF value increases proportionally to the number of times the term is contained in the document (_tf_ part), but grows in inverse proportion to the frequency of the term in the whole collection (_idf_ part). Hence, very common words (such as 'the') are not necessarily considered more important (i.e., having a larger _tfidf_ score) than less common ones such as 'email' or 'pii' (the latter stands for 'personally identifiable information').  
However, many words that appear in a paragraph are irrelevant for the purpose of classification. Hence, it is necessary to only retain the relevant ones in order to avoid problems such as overfitting and noisy text. To this aim, we investigated two solutions, explained in Sections 3.1.1 and 3.1.2.
## 7 SensorNC Applications
### Node Placement Strategies
#### Uniform Random  
We choose a uniform random placement as one of the competitors. In the uniform random placement, each of the \(n\) sensors has equal probability of being placed at any point inside a given field, as shown in Figure 13a. For example, such a placement can result from throwing sensor nodes from an airplane, helicopter or UAV.  
Popular grid layouts are a unit square, an equilateral triangle, a hexagon, etc. Among them, we investigate the square grid because of its natural placement strategy over a unit square. Figure 12(b) shows a grid deployment of \(n\) sensors in a circular field.  
The third strategy is based on tiling. A tiling is the covering of the entire plane with figures that do not overlap, nor leave any gaps. Among different tilings, we use a semi-regular tiling (which has exactly eight different tilings) where every vertex uses the same set of regular polygons. A regular polygon has the same side lengths and interior angles. We consider a semi-regular tiling that uses triangle and hexagon in the two-dimensional plane, the so-called 3-6-3-6 tri-hexagon tiling. The name comes from going around a vertex and listing the number of sides each regular polygon has, as illustrated in Figure 12(c). Here, we combine the advantages of a triangle grid and a hexagon grid.
# Impact of Gait-Synchronized Vibrotactile Sensory Feedback on Gait in Lower Limb Amputees
###### Abstract  
Background: Research on sensory feedback systems for prosthetic devices aims to enhance sensory capabilities to better meet user needs. Feedback systems for lower limb amputees (LLA) have been shown to reduce cognitive efforts, metabolic cost and phantom limb pain. This study evaluated the effect of a non-invasive, gait-synchronized, vibrotactile feedback system (VTFS) on the gait parameters of LLA. Methods: Four stimulators applied vibrotactile stimulation to the thigh of LLA during walking, corresponding to four pressure sensors located at the fore- and hindfoot embedded in a sock worn on the prosthetic foot: Standardized gait tests, such as the Timed \"Up and Go\" Test (TUG), the Four Square Step Test (FSST), the 10 Meter Walk Test (10 MVT) and the 2 Minute Walk Test (2 MWT), were performed to assess the risk of falling, coordination, walking speed and endurance before and after intervention. Results: After an average of 61.5 days using the VTFS, gait stability (TUG) improved significantly. Coordination (FSST) improved in 36% of subjects, while 45% showed a clinically relevant increase in gait speed (10 MWT). Conclusions: The results suggest an improved gait performance in the cohort. Though FSST lacked statistical significance, a \(p\)-value near 0.05 indicates a trend toward meaningful improvement. Notably, the participant with Targeted Sensory Reinnervation demonstrated the most favorable outcomes.  
amputation; lower extremity; sensory feedback; prostheses; human-machine interfaces; vibrotactile feedback +
Footnote †: journal: Applied sciences
# Low-Power LoRa Signal-Based Outdoor Positioning Using Fingerprint Algorithm
## 3 Proposed Fingerprint Algorithm
### LoRa-Based Positioning  
In this study, the LoRa-based positioning process using a fingerprint algorithm consisted of an online and an offline phase, as shown in Figure 6. In the offline phase, RSSI values of LoRa signals are measured and stored for each randomly selected sample point within the test area. In the online phase, the location of an end-device is estimated for arbitrary unknown locations.  
A LoRa end-device sends uplink packets to LoRa gateways. The gateways receive the packets and measure the RSSI values. These values are saved in a database and fingerprint maps are generated from the RSSI data for the service area.  
In the online phase, a LoRa end-device sends uplink packets that contain sequence information to LoRa gateways. When RSSI data are sent to the positioning server from gateways, gateway IDs are attached to the data for identification.
## 3 Results
### Evaluation  
To evaluate the developed serious game, two participants (see Table 1) from two different football clubs were involved in playing and testing the serious game. Both players had suffered a non-severe ankle injury three weeks previously and were already familiar with conventional rehabilitation.  
Participant 1, from a local football club, had three sessions in one week, with three repetitions per exercise (30 s, 45 s, and 60 s), for 20 min of active exercise time per session, for a total of 60 min per week. Participant 2, from another professional third-division football club, had an initial meeting with a therapist and four training sessions spread over two weeks. Three repetitions per exercise were performed (every 90 s), for a total of 27 min of active exercise per session, for a total duration of 1 h and 52 min. The evaluation results of participant 1 (P1) are shown in Figure 11 and those of participant 2 (P2) in Figure 11. The scores for the second game are abbreviated with T (Trophies). The measured ROM with the sensor is shown in the corresponding column (range) and the corresponding scores in the column score. Both participating soccer players gave positive feedback on the developed serious game. They also found the time spent playing the game to be enjoyable and entertaining. They would have continued to use the serious game and noted that they probably spent more time with the serious game than with classic rehabilitation or prevention exercises. The authors think that the participants also tried to increase their score and ROM over time. See Table 2 for an overview. On average P1 improved the ROM by 3.56\({}^{\circ}\) and scores by 7.00%, while P2 gained 6.59\({}^{\circ}\) and 9.53%, respectively.  
No variables showed significant differences to normal distribution; therefore, a paired sample \(t\)-test was performed to compare both performance indicators between the first and last session. Both the ROM (t = 5.71) and the Score (t = 3.98) showed positive, highly significant (\(p\) < 0.01) differences between the sessions.
# IoT Botnet Attack Detection Model Based on DBO-Catboost
## 4 Experimental Results
### Data Preprocessing  
Data preprocessing is an essential step in machine learning, involving cleaning, transforming, and standardizing raw data before feeding it into a model. The main purpose of data preprocessing is to improve data quality, enhance model performance, and increase generalization ability.  
Typically, data preprocessing involves several steps:  
Step1: Data cleaning. The primary objective is to remove duplicate and invalid values from the dataset. This also involves handling missing values by imputation.  
Step2: Data type conversion. This refers to the process of converting the data type of features to a different format. Machine learning algorithms typically require numerical data, so non-numeric features need to be converted into a numerical format for the algorithms to process.  
Step3: Standardization. Some features in the dataset may have varying scales or ranges of values. To simplify the learning process of the model, it is necessary to standardize the features in the dataset.  
Step4: Feature engineering. The Botnet dataset consists of 83 features, many of which are redundant. These redundant features can negatively impact the model training. In this study, principal component analysis (PCA) [36] is used for feature dimensionality reduction.  
It is worth noting that Catboost has strong capabilities in handling missing values. It can automatically detect and handle missing data, eliminating the need for additional feature engineering and reducing the time required for data preprocessing before training. In this section, to ensure consistency across all experiments, the preprocessed dataset was used.
## 1 Introduction
The materials used in the radome construction include fiber reinforced plastics (FRP), glass reinforced plastics (GRP), polypropylene (PP), acrylonitrile butadiene styrene (ABS), polystyrene unplasticized polyvinyl chloride (UPVC), and polycarbonate [29; 30]. Specifically, this research proposed a portable wideband directional antenna scheme with a radome for digital terrestrial television reception (470-890 MHz). The portable antenna scheme was an adaptation of the Yagi-Uda antenna, consisting of a folded bowtie radiator, a semicircular corrugated reflector, and a V-shaped director. The radome was of ABS material. In the study, simulations were carried out, and an antenna prototype was fabricated. In addition, experiments were undertaken to determine the antenna performance, including the impedance bandwidth (\(1\,S_{11}\,\,|\,\leq-10\) dB), gain, and unidirectionality. The outdoor and indoor testing was also carried out to assess the outdoor and indoor reception performance of the proposed antenna scheme. The organization of this research is as follows: Section 1 is the introduction. Section 2 details the antenna structure, and Section 3 deals with the parametric study. Section 4 is concerned with the fabrication of the prototype antenna and experimental results. Section 5 discusses the indoor and outdoor application testing. The concluding remarks are provided in Section 6.
###### Abstract  
We present the ionospheric disturbance responses over low-latitude regions by using total electron content from Geostationary Earth Orbit (GEO) satellites of the BeiDou Navigation Satellite System (BDS), ionosonde data and Swarm satellite data, during the geomagnetic storm in August 2018. The results show that a prominent total electron content (TEC) enhancement over low-latitude regions is observed during the main phase of the storm. There is a persistent TEC increase lasting for about 1-2 days and a moderately positive disturbance response during the recovery phase on 27-28 August, which distinguishes from the general performance of ionospheric TEC in the previous storms. We also find that this phenomenon is a unique local-area disturbance of the ionosphere during the recovery phase of the storm. The enhanced \(f_{0}F_{2}\) and \(h_{m}F_{2}\) of the ionospheric F2 layer is observed by SANYA and LEARMONTH ionosonde stations during the recovery phase. The electron density from Swarm satellites shows a strong equatorial ionization anomaly (EIA) crest over the low-latitude area during the main phase of storm, which is simultaneous with the uplift of the ionospheric F2 layer from the SANYA ionosonde. Meanwhile, the thermosphere O/N\({}_{2}\) ratio shows a local increase on 27-28 August over low-latitude regions. From the above results, this study suggests that the uplift of F layer height and the enhanced O/N\({}_{2}\) ratio are possibly main factors causing the local-area positive disturbance responses during the recovery phase of the storm in August 2018.  
ionospheric disturbance; BDS-GEO; TEC; geomagnetic storm; differential code biases Article  
Local Persistent Ionospheric Positive Responses to the Geomagnetic Storm in August 2018 Using BDS-GEO Satellites over Low-Latitude Regions in Eastern Hemisphere  
10 April 2022  
Accepted: 5 May 2022  
Submitted: 8 May 2022  
Submitted: 1
# Impact of Grounding Modeling on Lightning-Induced Voltages Evaluation in Distribution Lines
## 7 Conclusions  
Lightning induced-voltages are usually computed considering only the low-frequency grounding resistance when one considers the grounding system of the distribution tower. This work presented the impact of two different models for the grounding system of distribution line towers on the lightning-induced voltage on the phase insulators computation. The comparison between the low-frequency grounding resistance (\(R_{LF}\)) and the equivalent circuit corresponding to the wideband grounding frequency response (\(Z(\omega)\)) shows that considering only \(R_{LF}\) may lead to non-negligible underestimation of the maximum induced voltage. This aspect is more evident for subsequent strokes in the case of close stroke locations and low soil conductivities, which represents, by the way, one of the configurations when the lightning-induced voltages on a distribution line are high and potentially dangerous. On the other hand, for high soil conductivity, the differences between the two models are negligible. Future work will extend this analysis to the evaluation of a distribution line lightning performance to check whether this trend is also confirmed when dealing with statistical calculations. Additionally, in future works it is expected to include the coupling between the lightning channel and the grounding electrodes, similarly, as presented in [58].  
Conceptualization, D.M. and R.A.R.d.M.; methodology, D.M.; software, R.P. and R.A.R.d.M.; validation, D.M. and M.A.d.O.S.; formal analysis, D.M. and M.A.d.O.S.; writing--original draft preparation, D.M. and R.A.R.d.M.; writing--review and editing, R.P. and M.A.d.O.S.; All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
## 6 Results and Discussion  
In this section, we report the performance of our proposed augmented CWT feature sets while comparing it with existing feature set formulations used for indoor localization applied in deep learning models. The two existing feature set formulations used for validating our proposed feature set were those proposed in [13; 34]. In [13] joint a CWT-based feature set was proposed but without any augmentation. In [34], the feature set was composed of single RSSI images in which nine dummy values were added to each RSSI vector to generate a 2D image for CNN training and classification. We further show the effect of the Morlet and Morse wavelet transformed data on the room and specific position performance. Firstly, we present results for room identification in Section 6.1. Secondly, the performance results for specific reference point recognition are presented in Section 6.2.
## 3 Hybrid Game Theory-Based Optimization for the Energy Trading Strategy of VPP
### Master-Slave Game Model Between SES and VPP  
#### 3.1.1 Game Leader, SES  
(1) Objective function  
SES operators aim to maximize their own comprehensive benefits, and their benefits are determined by income from power purchase and sale between SES and outer power grid, income from power purchase and sale between VPP and SES, and energy storage operation and maintenance costs [45], as shown in Equations (3)-(6).  
\[\max F_{SES}=I^{es01}+I^{es02}-C^{ope}, \tag{3}\]  
\[I^{es01}=\sum_{t=1}^{24}{(c_{t}^{Ds}\cdot P_{t}^{grid,sell}-c_{t}^{Db}\cdot P_ {t}^{grid,buy})}, \tag{4}\]  
\[I^{es02}=\sum_{t=1}^{24}{(c_{t}^{Pb}\cdot P_{t}^{opp,buy}-c_{t}^{Ps}\cdot P_{t }^{opp,sell})}, \tag{5}\]  
\[C^{ope}=\sum_{t=1}^{24}{\lambda}^{ess}(P_{t}^{char}+P_{t}^{dischar}), \tag{6}\]  
where \(F_{SES}\) is the comprehensive benefit of SES operators; \(I^{es01}\), \(I^{es02}\) and \(C^{ope}\) are, respectively, the income of SES operators from purchasing and selling electricity to the outer power grid, the income of purchasing and selling electricity to VPP, and the operation and maintenance cost of energy storage system; \(c_{t}^{Ds}\) and \(c_{t}^{Db}\) are, respectively, the sale price and the purchase price between SES and outer power grid at time \(t\); \(c_{t}^{Pb}\) and \(c_{t}^{Ps}\) are, respectively, the sale price and the purchase price between SES and VPP at time \(t\), which is determined by SES operators; \(P_{t}^{grid,sell}\) and \(P_{t}^{grid,buy}\) are, respectively, the electricity sold and purchased by SES from the outer power grid at time \(t\); \(P_{t}^{opp,buy}\) and \(P_{t}^{opp,sell}\) are, respectively, the electricity purchased and sold by VPP from SES operators at time \(t\); \(P_{t}^{char}\) and \(P_{t}^{dischar}\) are, respectively, the charging power and discharging power of the energy storage system at time \(t\); \(\lambda^{ess}\) is the operation and management cost factor of the energy storage system [46], which is simplified as a ratio of the discharging and charging power of energy storage system.  
(2) Constraint conditions  
(1) The electrical power balance constraint of SES is  
\[P_{t}^{char}-P_{t}^{dischar}=P_{t}^{grid,buy}-P_{t}^{grid,sell}+P_{t}^{opp,sell} -P_{t}^{opp,buy}. \tag{7}\]  
(2) SES should meet its own performance constraints such as energy storage charge status, output power constraints, tie line constraints, and power purchase and sale price constraints of transactions between SES and VPP in the Equations (A1)-(A11) in Appendix A.