esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Keystroke Recognition
### Experimental Results on Keys' Gap and Count  
#### 6.5.1 Effect of Resolution  
The findings from the tests above and the preliminary experiment are presented in Figure 15. A 2D line plot was generated to illustrate the ML accuracy against the average gap distance. On the \(x\)-axis, we represent the average gap distance between each typed key during the experiment, ranging from 2 cm to 8 cm. The \(y\)-axis then represents the overall accuracy of the confusion matrix as previously described. The plot demonstrates a positive correlation between accuracy and gap distance.  
The diagram starts with a gap of 2 cm, which is the distance between two adjacent keys on the keyboard. Accuracy at this point is only 16%. In this case, the system can be claimed to have poor performance due to its destructive results. Behind that point, the accuracy grows much faster, achieving 32% for 2.2 cm and 43% for 2.45 cm, respectively. Following this, the precision increases but at a lower rate when we increase the gap distance to 6.3 cm, where the accuracy reaches 83%. Eventually, when we repeat using the five keys with an average gap distance of 8 cm, the duplicated accuracy reaches 90%, which aligns with our initial findings. This outcome indicates that the effectiveness of these WiFi sensing devices relies on the gap between each spatial point being tested. Consequently, based on this illustration of spatial resolution, it can be claimed that there is a reciprocal relationship between accuracy and average gap when utilising WiFi sensing for keystroke detection while keeping the Tx-Rx distance constant. Our analysis reveals a significant drop in machine learning accuracy when the average gap distance is reduced below 5.7 cm. This issue is linked to the inherent wavelength of the 5 GHz WiFi signal, which is roughly 6 cm. As a result, when the separation between keys falls within that range, the system struggles to distinguish between them due to the limited spatial resolution.  
Figure 14: Areas of keys for different tests.  
Figure 15: Accuracy change with average gap distance.
# Application of Molecularly Imprinted Polymers in the Analysis of Waters and Wastewaters
## 5 Application of MIPs for the Detection of Different Classes of Contaminants
### Endocrine Disrupting Chemicals (EDCs)
#### 5.4.4 Bisphenol F  
In another attempt of the industry to respond for the prohibition of BPA, other chemical equivalents, such as bisphenol F (BPF) and bisphenol AF (BPAF), which were thought to be more environmentally friendly replacements, were developed. As with BPA, BPF and BPAF have a similar chemical structure, as shown in Figure 8. There are a variety of industrial uses involving BPF, including lacquers, varnishes, coatings for food packaging, oral prosthetic  
Figure 11: Schematic illustration for the preparation of MIP® photocatalyst of UiO-66-NH\({}_{2}\)@paper and associated analysis procedures on paper-based analytical device (PAD) for the detection of Tetrabromobisphenol A (TBBPA) and tetrachlorobisphenol A (TCBPA). (Reproduced under permission from Elsevier, doi:10.1016/j.bios.2021.113106).  
devices, and dental sealants. With its high-performance monomers, BPAF is utilized as a cross-linker in fluoroelastomer polymers, such as membranes and optical fibers, as well as optical fiber monomers [174]. However, the knowledge on BPA alternatives is limited when it comes to the toxicological characteristics and severe health effects [168]. Based on structural and physicochemical similarities, it is possible that BPA-like compounds might have the same or even greater toxin potential as their analogues. With regards to BPF's endocrine-disrupting potential, investigations have shown that BPF exhibits estrogenic, antiestrogenic, and antiandrogenic actions equivalent to BPA [175; 176; 177], while for BPAF, it was reported that it has even stronger estrogenic and anti-antrogenic potential than BPA [176; 178].
# Design and Evaluation of Flooding-Based Location Service in Vehicular Ad Hoc Networks+
## 2 Related Work
### Vehicle-Location Service (VLS)  
The VLS [13] is a rendezvous-based location service that uses information from digital maps to partition the network and avoid the creation of empty zones (i.e., zones with no active nodes). Each zone contains a location server. The location server of a vehicle is obtained using the vehicle identifier and a hash function. Hence, a vehicle can send its location-update message using the co-ordinates of the nearest location server. Location updates are sent to the nearest location server, and location queries are sent to the nearest location server of the queried vehicle. When leaving its zone, each server has to transfer its location information to a new server. In high-mobility configurations, the VLS protocol induces a high overhead.
# Crossband Filtering for Weighted Prediction Error-Based Speech Dereverberation
## 4 Experimental Results
### Crossband Filtering--Length Preservation
#### 4.5.3 Optimal PESQ (\(L_{\text{bb}}=20\))  
Results are presented in Figures 7a-c and are summarized in Table 2. Notably, in Figure 7a, it is observed that the optimal performance in terms of FWSegSNR is achieved when \(L_{\text{cb}}=5\), which corresponds to \(L_{\text{bb}}=10\). This finding is further supported by  
Figure 5: Performance evaluation (Pres.) for \(L_{\text{bb}}=15\) (optimal FWSegSNR) in terms of (**a**) FWSegSNR, (**b**) CD, and (**c**) PESQ.  
Figure 6: Performance evaluation (Pres.) for \(L_{\text{bb}}=14\) (optimal CD) in terms of (**a**) FWSegSNR, (**b**) CD, and (**c**) PESQ.  
Figure 1a, which indicates that the performance in terms of FWSegSNR remains relatively stable for \(L_{\text{bb}}\) in the range of 10 to 16. This suggests that the optimal performance in terms of FWSegSNR, when utilizing the crossband components, is achieved when \(L_{\text{bb}}\) is in proximity to the optimal value obtained with the conventional WPE. A similar observation can be made for the CD metric, as depicted in Figures 1b and 7b. Furthermore, it is worth mentioning that this experiment yielded the best overall performance in terms of PESQ, as shown in Table 2. These findings highlight the importance of carefully selecting the parameters and considering the specific objectives when evaluating the performance of dereverberation methods.
# BSTCA-HAR: Human Activity Recognition Model Based on Wearable Mobile Sensors
## 3 Methods
### Attention Mechanism  
The attention mechanism strategically allocates varying degrees of importance (weights) to input information, enabling the model to prioritize focus on segments that exhibit similarity to the input elements, thereby efficiently distilling the salient features within the sequence.  
The attention mechanism determines the hidden state \(y_{i}\) generated at each specific time point by taking a vector \(x_{n}\) as the weighted average of the state sequence \(y\) (extracted key features). In this paper, the output sequence of the BiLSTM is passed into the TCN. The TCN processes long sequence information through stacked one-dimensional convolutional layers and residual connections, extracting important features from the sequence. The hidden state sequence \(H=[h_{1},h_{2},\ldots,h_{T}]\) output from the TCN layer is then fed into the attention layer. The attention mechanism performs a weighted summation of these hidden states to highlight key features. The attention mechanism first calculates the weight \(\alpha_{mi}\) for each time step, which represents the importance of each hidden state for the current task. The formula can be expressed as follows.  
\[e_{ni}=\alpha(M_{n-1},y_{i})\alpha_{ni}=\frac{\exp(e_{ni})}{\sum_{i=1}^{n}\exp (e_{ni})} \tag{8}\]  
The calculated weights \(\alpha_{ni}\) are used to perform a weighted summation of the hidden states from the TCN layer, generating a weighted average \(x_{n}\). The formula can be expressed as follows:  
\[x_{n}=\sum_{i=1}^{N}\alpha_{ni}y_{i} \tag{9}\]  
where N is the total number of time steps of the input sequence and \(\alpha_{ni}\) is the weight calculated for each state \(y_{i}\). After the vectors are computed, the vectors can be used to compute a new state sequence \(M\).  
The aggregated context vector \(x_{n}\) | is passed to the output layer, where the final output \(M=\tanh(W_{x}[x_{i}:y_{i}])\) is calculated using the tanh function to generate the final prediction results. In this way, the attention mechanism plays the role of filtering and enhancing key features, allowing the model to better capture important information in the action sequences and improving its recognition accuracy.
### Calculation of Factor \(\xi\)
#### 1.3.2 Method B (According to [PERSON] and [PERSON] et al.)  
Method B is according to [PERSON] [13] and [PERSON] et al. [23; 24; 25]), the computer programs in [15; 16; 25] give similar results.  
The I-section consists of three parts: top flange-index \(T\beta\), bottom flange-index \(B\beta\)I and web-index \(w\). In Method B, the following are calculated: factor \(\xi\) to accomplish plastic resistance, axial forces \(N\) {\(N_{Tf,Ld}\); \(N_{Bf,Ld}\); \(N_{w,Ed}\)}, and bending moments \(M\) in each part {\(M_{Tf,z,Ed}\); \(M_{Bf,z,Ed}\); \(M_{w,y,Ed}\)}.  
**Step 1.** Dividing section into three parts.  
Calculation of axial and bending resistances of all the parts:  
\[\begin{split}& N_{fl,pl,Rd}=N_{Tf,pl,Rd}=N_{Bf,pl,Rd}=b\cdot t_{f} \cdot f_{y};N_{wpl,Rd}=h_{w}\cdot t_{w}\cdot f_{y}\\ & M_{fl,pl,z,Rd}=M_{Tf,pl,z,Rd}=M_{Bf,pl,z,Rd}=\frac{b^{2}\cdot t _{f}\cdot f_{y}}{4};M_{w,pl,y,Rd}=\frac{h_{w}^{2}\cdot t_{w}\cdot f_{y}}{4} \end{split} \tag{11}\]  
**Step 2.** Considering constraints:  
Plastic resistances of the top flange in the nonlinear-interaction formula:  
\[\left(\frac{N_{Tf,Ed}}{N_{Tf,pl,Rd}}\right)^{2}+\left|\frac{M_{Tf,z,Ed}}{M_{ Tf,pl,z,Rd}}\right|\leq 1 \tag{12}\]  
Plastic resistances of the bottom flange in the nonlinear-interaction formula:  
\[\left(\frac{N_{Bf,Ed}}{N_{Bf,pl,Rd}}\right)^{2}+\left|\frac{M_{Bf,z,Ed}}{M_{ Bf,pl,z,Rd}}\right|\leq 1 \tag{13}\]  
Plastic resistances of the web in the nonlinear-interaction formula:  
\[\left(\frac{N_{w,Ed}}{N_{w,pl,Rd}}\right)^{2}+\left|\frac{M_{w,y,Ed}}{M_{w, pl,y,Rd}}\right|\leq 1 \tag{14}\]  
Equilibrium equations:  
\[\begin{split}& N_{Tf,Ed}+N_{Bf,Ed}+N_{w,Ed}=\xi N_{Ed}\\ & M_{Tf,z,Ed}+M_{Bf,z,Ed}=\xi M_{zEd}\\ &\frac{h-t_{f}}{2}\Big{(}M_{Tf,z,Ed}-M_{Bf,z,Ed}\Big{)}=\xi B_{ Ed}\\ & M_{w,y,Ed}+\frac{h-t_{f}}{2}\Big{(}N_{Bf,Ed}-N_{Tf,Ed}\Big{)}= \xi M_{yEd}\end{split} \tag{15}\]  
**Step 3.** Calculation of maximum \(\xi\):  
\[\max(\xi) \tag{16}\]
# Distribution System State Estimation Using Model-Optimized Neural Networks
## 4 Methodology
### System Model and Data Preparation  
To capture the most realistic system behavior possible in the absence of actual full historical AMI data, it is assumed that the loads follow an actual day load profile as shown in Figure 7. It is also assumed that there is only one type of load in this particular microgrid system. The day load profile is sampled every five minutes and is used to scale the loads of the test system appropriately. The final system state to use for state estimation is recorded once the system reaches steady state after applying the load change which takes about five seconds in simulation time (not real time). These metered values are then subject to data augmentation.  
The test system is a modified IEEE 34-bus test system from [38] containing a 9-bus DC microgrid model developed by [36] connected together via a bidirectional interlinking converter as shown in Figure 8. The IEEE 34-bus test system is a grid-connected distribution system composed of all kinds of loads by phase connection and by bus distribution with a maximum rating of about 150 kW per phase. It also has under load tap changers and  
Figure 6: An example of weight matrix sparsity given a pruning schedule—weights are removed at each pruning step until the sparsity reaches the specified targets. Training frameworks such as TensorFlow provide the option to set the initial and target sparsity and end step.  
Figure 7: The day load profile to be followed by all loads in the system.  
a 24.9 kV/4.16 kV transformer. The interlinking converter employs the droop control strategy to manage the power sharing between the AC and DC subgrids. The DC subgrid contains 20 kW loads at the far ends of the network. There are also photovoltaic (PV) power sources coupled with battery energy storage systems (BESS) which are all capable of supporting the DC subgrid in islanded mode. The test system is realized in the PSCAD simulation software.  
To reconcile the models to use between the WLS-SE and the PSCAD, certain assumptions were made. Since the power sharing between the subgrids are outside the scope of this paper's proposal, it is assumed that the power flow within the converter is unidirectional towards the DC subgrid only. Thus, the loading conditions and renewable energy source operating points are modified such that power is not transmitted from the DC subgrid back up to the external system (IEEE 34-bus' slack bus). Second, the three-phase unbalanced modeling of the AC subgrid's lines are replaced with its positive sequence equivalent, \(Z_{1}\). The impedance values are calculated via Equation (15), with the \(A\) transformation matrix shown in Equation (16). The \(Z_{abc}\) model of all the lines are neatly presented in the documentation of the IEEE 34-bus test system [38].  
\[Z_{012}=A^{-1}\cdot Z_{abc}\cdot A \tag{15}\]  
\[A=\begin{bmatrix}1&1&1\\ 1&\angle{-120^{\circ}}&1\angle{120^{\circ}}\\ 1&1\angle{120^{\circ}}&1\angle{-120^{\circ}}\\ \end{bmatrix} \tag{16}\]
# Cell-Free Massive MIMO Power Consumption with Serial Front-Hauls
## 2 System Model and Problem Statement
### Problem Statement
It is now evident that the accuracy of the current power consumption  
Figure 2: Antenna processing unit. model, when applied to serial front-hauls, will be directly proportional to the number of links \(N\). In other words, the closer \(N\) is to \(M\), the higher the accuracy the current power consumption model will achieve, and vice versa. In the case of serial front-haul, the information is sent just \(N\) times instead of \(M\) times. This has some implications. The most evident is that the power associated with the transmission and traffic of information decreases since only \(N\) transmissions are needed instead of \(M\), being \(N<M\). Additionally, it is possible that power consumption is affected by the changes in the physical topology. However, it is challenging to quantify this variation without knowing the specific front-haul and AP technology for each case. This work focuses on the power consumed during the downlink transmission because in uplink, if no other improvement is applied in the transmission, the power consumption is equivalent in dedicated and serial links. Note that in both cases, each AP is required to perform transmission with the data received from the UEs. Novel approaches are proposed for sequential uplink processing [23], which prevents the required front-haul capacity from growing with the number of APs and, consequently, results in power savings, but this is out of the scope of this work.
## 2 Background and Related Works
### Privacy Problem in Task Offloading  
Due to the lack of cellular base stations and edge computing servers, various terminals in remote smart grids send their computing tasks to visible satellites for execution. Since satellite radio links are open and exposed, the computing task packets of each terminal can be easily captured. In this case, we are aware that a rudimentary adversary can easily obtain the usage pattern privacy information of terminals without prior knowledge.  
As shown in Figure 1, it only takes the following two steps. The first step is to capture computing task packets transmitted over open satellite links. There are many wireless sniffer tools that can be directly used by the adversary. For example, [PERSON] et al. [30] proposed NSSN, which can accurately monitor and capture data packets in wireless sensor networks. Considering the multi-hop and multi-channel characteristics, [PERSON] et al. [31] further designed an improved wireless sniffer. Despite the low overhead and high accuracy, ref. [31] struggled to maintain real-time sniffing for long periods. Then [PERSON] et al. [32] proposed a robust sniffer which can maintain long-lived connections in real time. Based on these efforts, the adversary can obtain all the computational task packets transmitted during the sniffing period. Packets will be classified according to their source (e.g., source IP address, source MAC address), and parsed as data traffic.  
The second step is to analyze the data traffic of each source using various identification algorithms. Though the data traffic is usually encrypted, some information can still be acquired according to the metadata and statistical characteristics (such as packet length and packet interval) of the encrypted traffic. For instance, ref. [17] identified which applications and operations the captured encrypted traffic originated from. Ref. [33] inferred user usage on an application according to encrypted data packets. These efforts provide solutions to learn application and terminal information about current computational tasks. After sniffing and analyzing sufficient tasks, the adversary can infer the usage pattern privacy, including the service types of each source (which indicates different terminals) in remote smart grids.  
Such usage pattern privacy leakage will lead to terminals that are responsible for core services (e.g., power supply stability monitoring, control command sending and receiving) being in danger. The adversary may focus on the core terminals to launch various attacks, like masquerade attacks and control command replay attacks. As a national infrastructure, a successful attack on smart grids would cause extremely large-scale power disruptions, which greatly impact resident livelihood. Thus, it is necessary to protect privacy during the task-offloading process, especially in more vulnerable satellite networks.
# An Overview of Modelling and Energy Management Strategies for Hybrid Electric Vehicles
## 2 Types and Characteristics of the Powertrains for HEVs  
The drive system of a hybrid vehicle usually consists of two or more sources of power energy. In general, the vast majority of hybrid vehicles have an internal combustion engine (ICE) or a fuel cell as the main source of drive energy [23]. The other source of energy, which plays a secondary role, is usually energy storage devices that recover energy, such as batteries, hydraulic pumps, super flywheels, or air compressors [24]. The most common combination of energy sources is the ICE and a battery, which is commonly known as a HEV. Based on the powertrain coupling method, there are three types of HEV powertrains: series configuration, parallel configuration, and series-parallel hybrid configuration [25; 26; 27; 28].
## 3 Experiments and Discussion  
### Frequency Response  
The frequency response of the signal transmission system based on the human body medium is an important factor that influences the quality of the receiving signal. In our investigation, the frequency response of the signal transmission based on the proposed Mach-Zehnder EO sensor was determined, while the corresponding _in-vivo_ measurements with respect to the signal transmission based on electronic sensor was also carried out under the same conditions for comparison.
# Further Investigations into the Capacitive Imaging Technique Using a Multi-Electrode Sensor
## 4 Experiments of the Multi-Electrode Sensor  
The multi-electrode sensor is an important part of a CI system that can be used for detecting the defects and the setup of our basic experimental system is shown in Figure 7. Besides the multi-electrode sensor, this CI system consisted of a function generator, a charge amplifier, a lock-in amplifier, an X-Y-Z scanning stage, PXI (National Instruments, Shanghai, China), DAQ (National Instruments) and software [22; 23; 24; 25]. The function generator was used to generate the driving voltage (10 V), the frequency of which was 10 kHz. The wavelength of the driving voltage is far larger than the size of the instrument, therefore the fringing electric fields can be considered as quasi-static fringing electric fields at this frequency. The charge signals of three different channels obtained by the different sensing electrodes (No. 2-4) were converted to the charge signals of one channel through the NI PXI-2503 multiplexer module which was installed in PXI. The charge amplifier was used to change the converted charge signals into AC voltages. The lock-in amplifier was used to change AC voltages into DC voltages which were acquired by DAQ. The acquired DC voltages were converted to the DC voltages of three different channels by software, thus the DC voltages of three different channels which were corresponding with the different electrode pairs (e.g., E21, E31 and E41) were finally storage in PXI. The X-Y-Z scanning stage was controlled by software to scan over the specimens. In this experimental system, the multi-electrode sensor was used to detect the defects: defects on the surface of the fiberglass board, defects buried in the fiberglass board, defects within the fiberglass board and aluminum plate.
## 4 Results and Discussion
### Classification of Articles by Application Domain and Field  
We further classified the 285 articles that we extracted into areas of real-life application. Notably, we classified them into medical and non-medical fields of application. The following criteria were used to classify the articles under the medical field:  
* the paper discussed a medical condition (disorder/disease), such as a psychiatric or neurological case;
* the participants of the experiment were patients or it involved two groups: one consisting of healthy people, the other of patients;
* the experiment was conducted in a clinical setting; and/or,
* the paper was directed toward the medical community and suggested a new method for assistance, enhancement, monitoring, or diagnosis using emotion-based recognition.  
After evaluating all 285 articles according to the medical classification criteria listed above, we found that 67 articles, or 23.51%, were medical articles. The remaining 218 articles, or 76.5%, were non-medical. Distributions of medical and non-medical articles according to year of publication are presented in Figure 4. We further classified the articles into areas of application within each domain. Figure 5 shows a treemap of the 285 articles, classified according to field and application domain.
# A Novel Geo-Localization Method for UAV and Satellite Images Using Cross-View Consistent Attention
## 4 Experiments
### Experiment Results
#### 4.3.2 Computation Cost  
The computation costs of different models are listed in Table 4. The pipeline in this paper is based on DeiT-S pre-trained on ImageNet. The models based on ResNet-50 and ViT-S can also be implemented in our pipeline. As shown in Table 4, the pipeline based on DeiT-S achieves the highest accuracy with the lowest computation consumption. In TransGeo [14], the GFLOPS is 11.32, the model size is 9.85 GB, and the inference time is 3.093 ms (per image). In the proposed method, the GFLOPS is 11.12, the model size is 8.75 GB, and the inference time is 0.31 ms (per image). The results show that the method proposed in this paper has improved computational efficiency.  
Figure 6: The top-\(k\) recall rate denoted as R@k has been employed, which measures the rank position of positive samples among all reference samples.