esa-sceva/satcom-chunk-collection huggingface dataset:
# Surface Electromyography-Based Action Recognition and Manipulator Control
## 2 Method and Mathematical Background
### Feature Extraction
#### 2.3.2 Frequency-Domain Feature  
It was found that time-domain signals tend to be affected by time. As long as there is movement of muscle during the acquisition, the features change with it, and bring apparent non-stationary characteristics to some extent. In this case, the Fast Fourier transform (FFT) was put into use to convert the time-domain sequence to the power spectrum. Thence, the influence brought by time and quick motion could be reduced and the stability is enhanced [29].  
In practicality, the power spectrum of infinite length is estimated as follows. Assuming that the EMG signals are short-term stationary and can reflect time-varying characteristics, median frequency (MF) and the mean power frequency (MPF) are as below: [30].  
(i) MF:  
\[MF=\frac{1}{2}{\int_{0}^{\infty}PSD(f)df} \tag{5}\](ii) MPF:  
\[MPF=\frac{\int_{0}^{\infty}f\cdot PSD(f)df}{\int_{0}^{\infty}PSD(f)df} \tag{6}\]  
in which \(PSD(f)\) is the power spectral density of the EMG signals.
## 2 Related Works
### Iraqi Blood Bank System Structure  
It is essential to clarify that the Iraqi Ministry of Health is responsible for supervising, managing, and issuing instructions for the working mechanism in blood banks, blood donation centers, and sub-blood banks in all Iraqi governorates, including the National Center for Blood Transfusion in Baghdad, which is responsible for managing health care blood transfusion services. It is also considered the reference center for blood transfusion services in Iraq under the supervision of the Iraqi Ministry of Health, and it represents the first level in the structure of Iraqi blood bank work. The second level includes blood banks, blood donation centers, and the sub-blood banks of hospitals in the 18 Iraqi governorates, which also follow the policies of the Iraqi Ministry of Health. Lastly, the third level of this hierarchy, as shown in Figure 1, includes the blood banks, blood donation centers, and sub-blood banks in each governorate separately, such as those in the Salah ad-Din governorate.  
The Salah ad-Din governorate is one of the Iraqi governorates located in the center of Iraq, north of Baghdad. Its population is 1,595,235 million. Tikrit city is considered the center of this governorate and includes three blood donation entities: First, the Central Blood Bank of Tikrit, which was selected as a case study for this work. It is responsible for blood transfusions for all parts of the governorate, as well as for four primary operations (blood tests, blood draws, production, and blood supply). Second, four blood donation centers, which are responsible for three operations (blood tests, blood draws, and blood supply) distributed over four cities (Samarra, Balad, Doz, Sharqat). Third, sub-blood banks in each hospital and some medical centers, which are responsible for two operations (blood store, blood supply) in addition to the cross-matching operation.  
All these banks and centers work with the same mechanism so that the blood and its components can be exchanged, especially in critical times when a shortage occurs in a particular blood group. In addition, they cooperate with the National Center for Blood Transfusion in Baghdad. All the information above was sourced from the official guide [43].
# Drought Propagation in Brazilian Biomes Revealed by Remote Sensing
## 2 Materials and Methods
### Datasets
#### 2.2.2 Precipitation  
Precipitation (\(P\)) was obtained from the GPM mission, launched in 2014 in a cooperation between NASA and JAXA. GPM expands on the previous Tropical Rainfall Measuring Mission (TRMM), which operated from 1997 to 2015, providing improved \(P\) measurements globally. The algorithm used is the Integrated Multi-satellite retrievals for GPM (IMERG), which provides \(P\) estimates from analysis of several satellites of the GPM constellation, with microwave-calibrated infrared (IR) satellite estimates, \(P\) gauge analyses, and potentially other \(P\) estimators [64]. The IMERG algorithm provides global \(P\) estimates with half-hourly and monthly temporal resolutions of approximately \(11\) km (\(0.1^{\circ}\)).
###### Abstract  
Ice-covered transmission lines seriously affect the stable operation of the power system. Deploying a recognition network for measuring the ice thickness on transmission lines within a deicing robot, and controlling the robot to perform resonant deicing, is an effective solution. In order to solve the problem that the existing recognition network is not suitable for an edge device, an ice-thickness recognition network for transmission lines based on efficient dynamic perception (EDPNet) is proposed. Firstly, a lightweight multidimensional recombination convolution (LMRC) is designed to split the ordinary convolution for lightweight design and extract feature information of different scales for reorganization. Then, a lightweight deep fusion module (LDFM) is designed, which combines the attention mechanism with different features to enhance the information interaction between the encoder and decoder. Then, a new dynamic loss function is adopted in the training process to guide the model to perform refined detection of ice-covered boundaries. Finally, we count the ice pixels and calculate the ice thickness. The model is deployed on an OrangePi5 Plus edge computing board. Compared with the baseline model, the maximum ice-thickness detection error is 4.2%, the model parameters are reduced by 86.1%, and the detection speed is increased by 74.6%. Experimental results show that EDPNet can efficiently complete the task of identifying ice-covered transmission lines and has certain engineering application value.  
technology, machine learning, deep computing, image segmentation, lightweight network +
Footnote †: journal: Applied sciences
## 3 Results and Discussion
### Current-Voltage Characterization  
The output and transfer characteristic of the OTFTs with graphene:PEDOT:PSS as the top gate conductive layer is shown in Figure 6a,c. OTFTs exhibit a clear n-type transistor behaviour and possess a demonstrable linear and saturation region at higher gate bias voltages, along with very low gate leakage current I\({}_{\text{CS}}\). Figure 6b shows the scalability of the OTFT where the output characteristic of OTFTs with different aspect ratios were compared showing increasing output current with increasing aspect ratios. Device metrics, namely threshold voltage, carrier mobility, and ON/OFF ratio were determined from the transfer characteristic data displayed in Figure 6c. The effective per unit area dielectric capacitance was measured directly using a precision impedance analyzer and valued at 7.82 nF/cm\({}^{2}\). The threshold voltage V\({}_{\text{th}}\) = 1.2 V was determined graphically. The corresponding charge carrier mobility and ON/OFF ratio were evaluated at V\({}_{\text{DS}}\) = 50 mV and estimated as 1.2 cm\({}^{2}\)/V s and 3.75 \(\times\) 10\({}^{2}\), respectively.  
Figure 5: Charge mobility characterization: (**a**) SCLC current transient, (**b**) measured charge mobility vs. applied voltage.  
The OTFT devices exhibited good characteristics and relative environmental stability in ambient conditions. The gate leakage current was very low compared to the drain-source current, which is indicative of low surface defects of the semiconductor layer, good dielectric/semiconductor interface, and the high quality of the dielectric stack. As expected, characteristics of devices fabricated using the same process vary due to variations in ambient conditions, film thickness, and morphology. The fabricated OTFT devices experience drift and decreasing drain current with repeated measurements but remain operational over several weeks using the same device.
## 6 Conclusions  
The authors implemented a jamming event detector-based resilient MFMC software receiver that exploits frequency diversity to combat single-frequency jamming. It was demonstrated that the proposed resilient MFMC receiver offers better positioning accuracy than any single-constellation receiver in the case of single-frequency jamming. The resilient receiver can combat jamming in either the L1/E1/B1 or the L5/E5a/E5b frequency bands by properly detecting jamming on the affected signals and then excluding those signals while computing the PVT solution. Finally, the authors demonstrated the positioning performance of MFMC receiver in an operational maritime environment along with a detailed performance assessment of GPS-L5-only, Galileo-E5a-only, and Galileo-E5b-only signals that is assumed to be very novel for a research receiver. It was observed that the Galileo E5a or Galileo E5b position solutions outperform the L1/E1/B1-only position solution in nominal situation due to the improved signal characteristics in the L5/E5 frequency band. In this example dataset, the GPS L5 signal had relatively poor satellite visibility that contributed to the ultimate position accuracy. However, it is expected that GPS L5 will offer similar positioning performance to that of Galileo E5a/E5b signals when it reaches its full constellation in the future.  
The navigation results demonstrate the true potential of utilizing frequency diversity to combat jamming when it occurred in one of the GNSS frequencies. In general, following are the core contributions of this work:  
* Implementation of a jamming event detector to identify jamming on any GNSS signals.
* Implementation and validation of a novel resilient MFMC receiver to combat jamming on any single-frequency band.
* The first navigation results of single-frequency GPS L5, Galileo E5a, and Galileo E5b signals in a maritime operational environment utilizing an in-house research receiver.  
Figure 11: Instantaneous horizontal position error of different signal combinations along with jamming event detection for Scenario II.  
Future work includes impact analysis of jamming on Galileo E5 full-band AltBOC signal. To the authors' knowledge, impact analysis of jamming on Galileo E5 full-band AltBOC signal has not yet been thoroughly studied in the literature.  
Conceptualization, S.I. and M.Z.H.B.; methodology, S.I.; software, S.I. and M.Z.H.B.; validation, S.I. and M.Z.H.B.; formal analysis, S.I. and M.Z.H.B.; investigation, S.I. and M.Z.H.B.; resources, S.I., M.Z.H.B., S.K. and S.T.; data curation, S.I. and S.T.; writing--original draft preparation, S.I.; writing--review and editing, S.I., M.Z.H.B., S.K. and S.T.; visualization, S.I. and M.Z.H.B.; supervision, M.Z.H.B., S.K. and S.T.; project administration, S.I., S.T., M.Z.H.B. and S.K.; funding acquisition, M.Z.H.B., S.T. and S.K. All authors have read and agreed to the published version of the manuscript.  
This research was partially funded by \"European Space Agency (ESA), contract number ESA 4000122887/18/NL/DB.  
Not applicable.  
The authors declare no conflict of interest.
# Efficient SMC Protocol Based on Multi-Bit Fully Homomorphic Encryption
###### Abstract  
Aiming at the problems of large ciphertext size and low efficiency in the current secure multi-party computation (SMC) protocol based on fully homomorphic encryption (FHE), the paper proves that the fully homomorphic encryption scheme that supports multi-bit encryption proposed by [PERSON] et al. satisfies the key homomorphism. Based on this scheme and threshold decryption, a three-round, interactive, leveled, secure multi-party computation protocol under the Common Random String (CRS) model is designed. The protocol is proved to be safe under the semi-honest model and the semi-malicious model. From the non-interactive zero-knowledge proof, it can be concluded that the protocol is also safe under the malicious model. Its security can be attributed to the Decisional Learning With Errors (DLWE) and a variant of this problem (some-are-errorless LWE). Compared with the existing secure multi-party computation protocol based on fully homomorphic encryption under the CRS model, the ciphertext size of this protocol is smaller, the efficiency is higher, the storage overhead is smaller, and the overall performance is better than the existing protocol.  
fully homomorphic encryption; secure multi-party computation; multi-bit encryption; threshold decryption; decisional learning with errors +
Footnote †: journal: Applied  
**Keywords:** fully homomorphic encryption; secure multi-party computation; multi-bit encryption; threshold decryption; decisional learning with errors
## 3 Results
### Evaluation of HLC BSPM Elements  
In this paper, the elements m\({}_{22}\), m\({}_{33}\), and m\({}_{44}\) of the HLC BSPM were evaluated. There were 312 measurements suitable for training, which significantly complicated the task due to the small size of the experimental array. A random forest method with 100 trees was used as a reference. This allowed us to estimate the potentially optimal result. As a neural network, we took a multilayer perceptron with a hidden layer of five neurons--the simplest model for small statistics. To evaluate the quality of these approaches, we also used the value of standard deviation and cross-validation of two folds. The following results were obtained.  
Table 4 presents data for the m\({}_{22}\) BSPM element. The worst result is observed when using the neural network method, while the random forest method yields better results.  
The scatter plot in Figure 17 shows a weak relationship between the estimate of m\({}_{22}\) and its measured value. This is most likely due to the weak relationship with the input parameters. In Figure 18, it can be seen that the neural network is almost unable to detect the relationship between the compressed meteorological parameters and the element m\({}_{22}\)  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **RF (PCA)** & **NN (PCA)** & **RF (AE)** & **NN (AE)** \\ \hline Fold 1 & 0.12 & 0.14 & 0.12 & 0.17 \\ Fold 2 & 0.14 & 0.16 & 0.13 & 0.19 \\ \hline \end{tabular}
\end{table}
Table 4: The value of standard deviation in estimating the m\({}_{22}\) BSPM element for random forest method and neural network with different data compression methods.  
Figure 16: Scatter plot of HLC boundary displacement determination using neural network and PCA compression.  
Concluding the description of the results, it is worth recalling that we considered the elements of the HLC BSPM to be normalized by m\({}_{11}\); because of this, m\({}_{11}\) in the analyzed matrices is equal to one. The elements m\({}_{22}\) and m\({}_{44}\) of such BSPMs depend on meteorological parameters. Element m\({}_{33}\) requires further study and expansion of the analyzed dataset. The enrichment of the experimental dataset is continued [18; 21]. In addition, the influence of other atmospheric parameters on the HLC BSPM elements is being investigated. These parameters will be added as input data for neural network training in the future.
###### Abstract  
Automatic terrain classification in lower limb rehabilitation systems has gained worldwide attention. In this field, a simple system architecture and high classification accuracy are two desired attributes. In this article, a smart neuromuscular-mechanical fusion and machine learning-based terrain classification technique utilizing only two electromyography (EMG) sensors and two ground reaction force (GRF) sensors is reported for classifying three different terrains (downhill, level, and uphill). The EMG and GRF signals from ten healthy subjects were collected, preprocessed and segmented to obtain the EMG and GRF profiles in each stride, based on which twenty-one statistical features, including 9 GRF features and 12 EMG features, were extracted. A support vector machine (SVM) machine learning model is established and trained by the extracted EMG features, GRF features and the fusion of them, respectively. Several methods or statistical metrics were used to evaluate the goodness of the proposed technique, including a paired-t-test and Kruskal-Wallis test for correlation analysis of the selected features and ten-fold cross-validation accuracy, confusion matrix, sensitivity and specificity for the performance of the SVM model. The results show that the extracted features are highly correlated with the terrain changes and the fusion of the EMG and GRF features produces the highest accuracy of 96.8%. The presented technique allows simple system construction to achieve the precise detection of outcomes, potentially advancing the development of terrain classification techniques for rehabilitation.  
terrain classification; electromyography; ground reaction force; feature extraction; multi-sensor fusion +
Footnote †: journal: Applied Sciences
# A _uv_-Cellular Network: Design, Implementation and Experiments
## 4 Simulation Study
### Performance Metrics  
To prove the effectiveness of the proposed approach, we use three metrics as follows.  
* _Packet Latency_ indicates the time it takes for data (a packet or bits) to travel from one end of the network to the other, and typically includes transmission delay and propagation delay. It is considered to be the most important concern for users.
* _Packet Delivery Fraction_ is defined as the ratio of the number of packets successfully received by the destination to the number of packets generated by the source. This metric reflects the effectiveness of the routing strategy which proposed in this paper.
* _Effective Receive-to-Send Ratio (ERSR)_ means the ratio of the number of received packets and average number of sent packets per hop in the network. It reflects the relationship between the number of packets received and the total amount sent in the network. A larger ratio means fewer invalid transmissions and less total system energy consumption. Conversely, the higher the total system energy consumption.
# Advanced Persistent Threats and Their Defense Methods in Industrial Internet of Things: A Survey
## 3 APTs Faced by IIoT and Its Defense Methods
### Application Layer  
The application layer is the top layer of the IIoT architecture, which includes various applications and services, such as monitoring, control, diagnosis, and prediction. In the application layer, APT attacks may include intrusion into applications, tampering with application interfaces, and theft of user data, among others, as follows:  
* Access control attacks [61]: Attackers may gain unauthorized access by stealing credentials, deceiving users, or exploiting vulnerabilities. Once attackers gain access, they can manipulate equipment, tamper with data, or steal sensitive information.  
* Malware [51; 52]: Attackers may infect equipment or networks with malware to control them. Malware may include viruses, worms, Trojans, or ransomware, which can damage equipment or entire systems.
* Denial-of-service attacks (DDoS) [57]: Attackers may launch DDoS attacks to make target equipment or make systems unavailable. This type of attack typically involves a large amount of traffic or a large number of requests that exceed the target system's processing capabilities, causing it to crash or become unusable.
* Physical attacks [48; 49]: Attackers may invade equipment or systems physically, such as forcibly entering equipment or destroying physical connections. This type of attack can cause equipment or systems to stop working, lose data, or leak information.
* Social engineering attacks [50]: Attackers may use social engineering techniques to deceive users or administrators into disclosing credentials or performing inappropriate operations. This type of attack typically requires attackers to have some understanding of the target user's behavior patterns and psychological state.  
To address these threats, the industrial internet application layer needs to take a series of security measures, including access control, encrypted communication, vulnerability management, behavioral analysis, and physical security, to ensure the security and reliability of equipment and systems.
# Impact of Cooking Processes on Volatile Flavor Compounds and Free Amino Acids in Fish Sauce
## 3 Results and Discussion
### E-Nose Evaluation of Flavor Profile  
The E-nose was employed to evaluate the overall flavor profile of fish sauce samples exposed to various cooking processes (Figure 2). The E-nose radar plot revealed that the response characteristics of the 10 sensors varied among the samples. Sensors W1C, W3C, W6S, W5C, W2S, and W3S exhibited lower signal intensities across all samples, suggesting that aromatic components, including hydrogen, arom-aliph, broad-alcohol, and methane-aliph, are present at low concentrations in the fish sauce flavor. Fish sauce samples exhibited stronger responses to W5S, W1S, W1W, and W2W, corresponding to higher concentrations of broad-range compounds, broad-methane, and sulfur-organic compounds. Additionally, variations in sensor response allowed the differentiation of fish sauce samples according to cooking time. The B10 group could not be distinguished from the U group, indicating  
Figure 1: Radar plot based on sensory score of fish sauce subjected to boiling (**A**) and stir-frying (**B**). U (unheated); B10, B30, and B60 (heated at 100 \({}^{\circ}\)C for 10, 30, and 60 min, respectively); F10, F30, and F60 (heated at 220 \({}^{\circ}\)C for 10, 30, and 60 s, respectively).  
that boiling for 10 min had a minimal impact on the overall aroma profile. The other groups were distinct from the unheated group, with longer heating times resulting in greater separation along the sensor axis, especially for stir-fried samples. Sensor response intensities on radar plots demonstrated that both cooking methods and cooking times significantly affect the fish sauce flavor profile. Moreover, cooking methods have a more pronounced impact on the aroma of fish sauce. Thus, a molecular sensory science approach was employed to identify changes in the flavor of fish sauce after cooking.
# A Novel Hybrid Method for Urban Green Space Segmentation from High-Resolution Remote Sensing Images
## 3 Results
### Ablation Experiments
#### 3.2.1 Ablation Experiment on UGS-1 and UGS-2
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & \multicolumn{4}{c}{**Class IOU**} \\ \hline
**Method** & **MIOU (\%)** & **Low Vegetation (\%)** & **Tree (\%)** \\ \hline baseline & 71.11 & 48.44 & 80.64 \\ baseline+ DE & 71.44 & 48.85 & 80.76 \\ baseline+ DE+ BFF & 71.98 & 49.22 & 81.60 \\ baseline+ DE+ BFF+ Pyconv & 71.80 & 49.14 & 81.34 \\ baseline+ DE+ BFF+ GAM & 71.76 & 48.69 & 81.53 \\ baseline+ DE+ BFF+ MSPA & 72.15 & 49.53 & 81.64 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of UGS-1 ablation experiments. DE denotes decoder block, BFF denotes bilateral feature fusion module, Pyconv denotes Pyramidal Convolution, GAM denotes Global attention mechanism, MSPA denotes multiscale pooling attention.