esa-sceva/satcom-chunk-collection huggingface dataset:
# Defect Recognition in Ballastless Track Structures Based on Distributed Acoustic Sensors
## 3 Vibration Signal Extraction  
Defects in ballastless tracks depend on different vibration characteristics that are only regular under the action of wheel-rail cycles. When no trains pass, the value of data analysis is low and data imbalances will be caused. Therefore, it is necessary to locate trains and only retain the track vibration signals when they pass by. In this article, we have identified the train movement trajectory based on a connected component-labeling algorithm [15; 16] and realized the extraction of effective signals according to the obtained fitting line of the head and tail, as shown in Figure 6. Similarly, the \(X\)-axis represents the spatial sampling points on the optical fiber, and the \(Y\)-axis represents 10,000 pulse periods, or Rayleigh backscattering traces. In this way, given the displacement and time, it is easy to identify the trajectory, such as head, tail and direction.  
Figure 5: Smoothing results of different times.  
Figure 6: Train movement trajectory.  
This task consisted of the following steps:  
Step 1: Extract the phase from the original signal to acquire a matrix of the size 10,000 \(\times\) 4095;  
Step 2: Denoise the matrix. Perform the moving average and moving differential method (\(M=10\)) based on columns and median filtering based on rows;  
Step 3: Transform the matrix into a binary image with an adaptive threshold calculated based on the OSTU method [17];  
Step 4: Label the target pixels in the binary image so that each individual connected region forms an identified block.  
Assuming that the binary image is \"data.bmp\", then the pseudocode of its connected component labeling is shown in Algorithm 1.  
```
0: data.bmp
0: Structure labels of the same connected component
1: algorithm TwoPass(data)
2: linked = []
3: labels = structure with dimensions of data, initialized with the value of Background (less focused content)
4: First pass
5: for row in data:
6: for column in row:
7: if data[row][column] is not Background
8: neighbors = connected elements with the current element's value
9: if neighbors is empty
10: linked[NextLabel] = set containing NextLabel
11: labels[row][column] = NextLabel
12: NextLabel += 1
13: else
14: Find the smallest label
15: L = neighbors labels
16: labels[row][column] = min(L)
17: for label in L
18: linksed[label] = union(linked[label], L)
19: Second pass
20: for row in data
21: for column in row
22: if data[row][column] is not Background
23: labels[row][column] = find(labels[row][column])
24: return label
25:endif
```  
**Algorithm 1**Two-Pass Algorithm
## 2 State of the Art
### Existing Technologies for Keystroke Detection  
WiFi sensing technology for keystroke detection raises significant privacy concerns due to its ability to infer sensitive information without direct interaction. [PERSON] et al. [25] introduced \"Widar\", a system that used commodity WiFi devices to detect human activities, including keystrokes, by analysing CSI. However, Widar's performance was highly sensitive to environmental changes such as furniture rearrangement or user orientation, which could degrade accuracy significantly in real-world scenarios. Building upon this, [PERSON] et al. developed \"WiKey\", which achieved over 97.5% detection accuracy for keystrokes using a TP-Link router and a Lenovo laptop [11]. Despite its high accuracy, WiKey required strict line-of-sight placement and stable signal environments, making it less practical in dynamic or cluttered settings. Later, [PERSON]'s team proposed \"WiKI-Eve\", a system that eavesdropped on keystrokes using beamforming feedback information from smartphones, achieving up to 65.8% accuracy in password inference [26]. The drawback of WiKI-Eve lay in its relatively low accuracy for complex keystroke sequences and its reliance on specific hardware configurations to access beamforming data, which may limit scalability. Besides these applications, a recent system called \"Wi-Crack\" was introduced [27], which combined amplitude, phase, and their differences in CSI to enhance keystroke recognition accuracy on smartphones. While Wi-Crack improved signal feature extraction, it remained vulnerable to cross-device variability and still struggled in environments with multiple users or movement noise. Similarly, in 2024, [PERSON] et al. proposed \"WiPass\" [28], a 1D-CNN-based system for numerical keypad inputs on smartphones, demonstrating the feasibility of keystroke inference via WiFi signals. Although WiPass showed promise, its deep learning model was heavily dependent on training data collected in static environments, raising concerns about robustness across different settings and users. Additionally, the model training process required considerable computation and may not be feasible for real-time applications without specialised hardware. Although all of these technologies have been shown to have high accuracy, the literature shows a lack of research on the spatial resolution of keystroke detection.
## 7 Implementation and Evaluation
### Times Taken for Key Enrollment and Authentication
#### 7.2.1 Key Enrollment Overhead  
To evaluate the latency caused by the key enrollment procedure in our system, we measured the execution time of the entire procedure described in Figure 2 including the key generation time. We also performed the same measurements on the conventional user authentication system described in Section 3 to compare the overheads. For the key enrollment procedure in the conventional system, the user's sign key was generated and simply encrypted with the user's password. For both systems, we repeated the same experiments 100 times. Table 1 summarizes the results.  
For the proposed system, the average execution times were 0.658 s for RSA-3072 and 0.279 s for ECDSA-256 with respective standard deviations of 0.295 s and 0.038 s. For the conventional system, on the other hand, the average execution times were 0.326 s for RSA-3072 and 0.014 s for ECDSA-256 with respective standard deviations of 0.209 s and 0.002 s. Even though the execution times of our system were about 2 and 20 times longer than the conventional system, the absolute values of the performance gap were as small as 0.333 s and 0.266 s on average. Furthermore, the enrollment procedure was performed only once and can be amortized over the subsequent authentication procedures.  
To find a more suitable digital signature algorithm for our system, we implemented both RSA-3072 and ECDSA-256 and compared the execution times of both implementations. As shown in Table 1, the execution time for the key enrollment procedure of the implementation using ECDSA-256 was 0.379 s shorter than the implementation using RSA-3072.
# Proactive Caching at the Edge Leveraging Influential User Detection in Cellular D2D Networks
###### Abstract  
Caching close to users in a radio access network (RAN) has been identified as a promising method to reduce a backhaul traffic load and minimize latency in 5G and beyond. In this paper, we investigate a novel community detection inspired by a proactive caching scheme for device-to-device (D2D) enabled networks. The proposed scheme builds on the idea that content generated/accessed by influential users is more probable to become popular and thus can be exploited for pro-caching. We use a Clustering Coefficient based Genetic Algorithm (CC-GA) for community detection to discover a group of cellular users present in close vicinity. We then use an Eigenvector Centrality measure to identify the influential users with respect to the community structure, and the content associated to it is then used for pro-active caching using D2D communications. The numerical results show that, compared to reactive caching, where historically popular content is cached, depending on cache size, load and number of requests, up to 30% more users can be satisfied using a proposed scheme while achieving significant reduction in backhaul traffic load.  
D2D communication; proactive caching; community detection; centrality measure; influential users; social network +
Footnote †: journal:  
_Future Internet_**2018**, _10_, 93; doi:10.3390/610100093
## 3 Experimental Data and Results
### Usefulness of Camera-Based Optical Communications in the Detection Process
#### 3.1.1 Techniques for Extracting Information from an LED Using Optical Communications  
The current cameras contain CMOS-type sensors; they include automatic running mechanisms. The technique dedicated to sequential reading becomes the main feature of the cameras that benefit from the shutter, more precisely when the frames are not processed at the same time. Hypostases are analyzed by a quick scan, both horizontally and vertically, however different from a camera with a global shutter, in which case the scene is analyzed completely at the same time. A camera that contains a global shutter facilitates the sensor to be exposed only once to the light, being able to support the closing/lowering state of an LED on a single frame. If a camera has a shutter, the pixels in each row are exposed simultaneously at time T.  
Therefore, an image captured using the shutter allows multiple exposures. It has the quality of obtaining several states for LEDs in a single frame, through the prism of each row gradually exposed to light. If an LED flashes, opens and closes depending on the bitrate modulated in binary code, the image contains a multitude of black-and-white gradients. In these circumstances, the bandwidths depend on certain modulated frequencies; however, the number of bands also depends on the distance between the camera and the LED.  
To be able to adjust and improve the ability to communicate over long distances, a photoreceptor is used. Figure 4 shows a model and an imaging technique of a room. In the example shown, the status of the LED is defined by blocks with black-and-white graphic layers, and the image captured from the LED is displayed only after going through a rotation process at 280\({}^{\circ}\). It should be noted that the region is marked in the figure to understand the exposure time for each row denoted by \(n\) and for the upper rows denoted by \(n+1\). In another particular case, a transition region is obtained to the ON/OFF moment for the LED; this aspect iterates the state of change during the exposure.
## 3 Result and Discussion  
### Percentage Yield and Chemical Composition of C. operculatus Essential Oil  
The hydrolytic distillation of fresh leaves of _C. operculatus_ resulted in the isolation of the pale yellow oil. The percentage of essential oil obtained was 0.18 \(\pm\) 0.03% (\(v/v\)), much higher than that obtained from the _C. operculatus_ leaf collected in Nghe An, Vietnam (0.10%, fresh leaves) of [PERSON] et al. (1994) [PERSON] et al. (1994). Different geographical circumstances and extraction techniques can be blamed for variations in the amount of essential oil. The GC-MS experiment showed that the _C. operculatus_ leaf essential oil contained 30 volatile compounds, representing 98.3% of the oil content. The essential oil of leaf _C. operculatus_ showed the presence of main groups, including monoterepene hydrocarbons (88.3%), sesquiterpene hydrocarbons (8.3%), oxygenated sesquiterpen (0.9%), and oxygenated monoterepenes (0.6%) (Table 1). Out of 30 compounds, allo-ocimene (31.6%), (Z)-\(\beta\)-ocimene (30.4%), (E)-\(\beta\)-ocimene (14.0%), \(\alpha\)-pinene (5.6%), \(\beta\)-caryophyllene (4.6%), and limonene (2.7%) were found to be major components. These results appear to differ from other locations' oil chemical compositions when compared to the literature. Indeed, the major components of essential oils from Phong Dien, Thua Thien Hue (Vietnam) were (_E_)-\(\beta\)-ocimene (52.9%), (Z)-\(\beta\)-ocimene (10.9%), cryophyllene (8.1%), humulene (2.5%), \(\gamma\)-muurolene (2.0%), and epiglobould (2.1%); those of Vinh, Nghe An (Vietnam) were (_Z_)-\(\beta\)-ocimene (32.1%), (_E_)-\(\beta\)-ocimene (9.4%), myrcene (24.6%), and \(\beta\)-caryophyllene (14.5%); and those of Nepal were myrcene (69.70%), (_E_)-\(\beta\)-ocimene (12.24%), (_Z_)-\(\beta\)-ocimene (4.79%), linalool (4.08%), \(p\)-cymene (2.71%), (_E_)-caryophyllene (1.11%), and (_E_)-Nerolidol (1.57%) [PERSON] et al. (1994); [PERSON] et al. (1994). The observed dissimilarity in the quality and content of these essential oils might be attributed to the distribution of species, as well as differences in analysis and identification methods. However, compounds (_Z_)-\(\beta\)-ocimene and (_E_)-\(\beta\)-ocimene were always present in the major components of this essential oil. Therefore, compounds (_Z_)-\(\beta\)-ocimene and (_E_)-\(\beta\)-ocimene could be the marker compounds of the essential oil. Figure 1: Interaction between co-crystal ligand and \(\alpha\)-glucosidase.
# A Millimeter-Wave 4 th-Harmonic Schottky Diodes Mixer with Integrated Local Oscillator
## 2 Mixer Design
### 4 th-Harmonic Mixer Passive Circuits  
The fourth-harmonic mixer is based on an anti-parallel double pair of Schottky diodes (ideality factor \(\eta=1.2\), saturation current \(I_{sat}=1.2\times 10^{-16}\)\(A\), estimated resistance \(R_{s}=7.3\)\(\Omega\) and junction capacitance \(C_{j}=5.3\)\(fF\)). The circuit is printed on 100 \(\upmu\)m thickness cyclic olefin copolymer (COC) substrate (\(\epsilon_{r}=2.3,\tan\delta=0.0009\)) [14] and mounted in a 100 \(\upmu\)m waveguide channel. Using four diodes instead of two avoids overheating and malfunctions that may occur when working at high temperatures [15].  
From Figure 1 we can observe that the radiofrequency signal is coupled from a rectangular waveguide to the microstrip line where the diodes are placed. The matching lines that adjust the diode embedding impedance and the corresponding filters for IF, LO and RF port isolation have been optimized. A top view of the passive elements and their simulated response using the commercial software HFSS are shown in Figure 2.  
The first filter corresponds to a low-pass filter for IF frequency. In this case, a hammer-head configuration has been selected, see Figure 2a, because of its compactness and good rejection. In Figure 2b the RF low-pass filter is shown. This filter rejects the RF signal and only lets the LO signal pass; a stepped impedance configuration has been chosen in this case. This kind of filter, which consists of quarter-wavelength-long alternating low- and high-impedance sections, presents good rejection and wide bandwidth with a narrower footprint than other filter configurations. The last necessary filter is a high-pass filter, in this case a DC-Block, which lets the LO signal pass to the circuit and rejects the other frequencies, Figure 2c.  
A transition that couples the RF signal received in the antenna to the microstrip line in which the planar filters are implemented is also necessary; see Figure 2d. This transition consists of an E-probe optimized to maximize the power coupled to the microstrip line at the RF frequency. Furthermore, this transition also works as a high-pass filter for the IF and the LO signals, so that the isolation at the RF port is high. Finally, one end is connected  
Figure 2: Top view and predicted response of the mixer passive circuits: (**a**) IF Hammer-head filter; (**b**) RF stepped impedance low-pass filter; (**c**) LO DC-Block and (**d**) RF waveguide to microstrip E-Probe transition.  
to the antiparallel Schottky diode pair (APDP) and the other one is connected to the metal housing block to provide the required DC grounding for the APDP.  
From the load-pull analysis of the fourth-harmonic mixer, the optimum embedding impedance at LO and RF frequencies have been calculated. These impedances are \(Z_{LO}=135-j108\)\(\Omega\) and \(Z_{RF}=99-j72\)\(\Omega\) for LO and RF frequencies, respectively. The dimensions of the matching lines were calculated in order to implement these optimum impedances so that the mixer conversion losses were minimized.
## 4 Results and Discussion  
The following section presents the structure of NN used in the experiment and explains the results of the model and data quantization and selection methods described in the previous section. Please note that although the presented results were obtained from simulated data (which can be considered a limitation of the current study), due to the physics fidelity of the data generated by the simulator (as discussed in Section 3.1), we believe that the conclusions drawn can be transferred to real data with no or minimal NN re-training and adjustment.
# Toward a Comprehensive Understanding and Evaluation of the Sustainability of E-Health Solutions
## 9 Limitations  
Despite the perceived effectiveness of the proposed sustainability model and its assessment method, there are some threats that are worth mentioning. In terms of the SLR construct validity, the SLR protocol, especially the search string, might be biased or might not cover all relevant studies. However, to mitigate this threat, we had the SLR reviewed by an expert and the search string's terms were extracted from relevant studies. Another threat was that some of the evaluation tools, surveys and checklists of the aspects were gathered from studies that discuss the evaluation of the intended aspects without relating it to the sustainability of digital solutions. To mitigate those risks, the model and the assessment method were validated by IT and domain experts in the healthcare field. Lastly, as there is no clear evaluation scale for the sustainability of e-solutions in healthcare in the literature, we proposed an evaluation scale, which might impact its validity. However, to mitigate this risk, the proposed scale was inspired from other studies that were published to assess the maturity level of the digitalization of companies, which is close to the subject of this study. For the internal validity, data extraction might be biased, or some relevant data might be missed, as the data was extracted by the researchers. However, having the template that includes what data to extract, and documentation of extracted data might reduce the risk if this issue (see Table 5). Additionally, some of the questions in the quality assessment are not decisive, as they depend on how the researchers interpreted the studies. Hence, quality assessment criteria were adopted from a published study. In terms of building the sustainability model, grouping the aspects and elements under the dimensions might be biased towards the authors' understanding. To mitigate this risk, we used thematic and coding analysis where dimensions were given clear definitions and criteria, which were used to code and group the aspects and dimensions. In addition, three IT experts in the healthcare domain wholly validated the constructed model.
# Continuous Emotion Recognition with Spatiotemporal Convolutional Neural Networks
## 3 Spatiotemporal Models for Continuous Emotion Recognition
### Post-Processing  
The post-processing aims to improve the quality of the prediction by using some statistical information of the target dataset to reduce variance among datasets [72]. However, due to data imbalance in the training set, some valence and arousal values are difficult to reach. For instance, in the target dataset, neutral emotions, which imply valence and arousal levels close to zero, are much more frequent in the training set than extreme valence and arousal values (close to 1). Therefore, three post-processing steps are used to reduce the variance: scale normalization, mean filtering, and time delay.  
Scale normalization consists in normalizing the predictions according to the distribution of the labels in the training set. Valence and arousal predictions \((y^{\prime})\) are normalized by the mean \((\overline{y}_{l_{tr}})\) and the standard deviation \((\sigma_{l_{tr}})\) of the labels of the training set as:  
\[y_{sn}=\frac{y^{\prime}-\overline{y}_{l_{tr}}}{\sigma_{l_{tr}}} \tag{1}\]  
Mean filtering consists of centering predictions around mean values, increasing the linear relationship, and labeling correspondence. Valence and arousal predictions \((y^{\prime})\) are centered by subtracting the mean value of the labels \((\overline{y}_{l_{tr}})\) of the training set and by adding the mean value of the predictions \((\overline{y}^{\prime}_{tr})\) on the training set as:  
\[y_{mf}=y^{\prime}-\overline{y}_{l_{tr}}+\overline{y}^{\prime}_{tr} \tag{2}\]  
Finally, a time delay is used to compensate for some offset between labels and predictions due to the reaction lag of annotators. Valence and arousal predictions \((y^{\prime}(f))\) at frame \(f\) are shifted over \(t\) frames (precedent or subsequent) in order to align predictions and labels temporally as:  
\[y_{td}=y^{\prime}(f+t) \tag{3}\]  
where \(t\) is an integer in \([-10,10]\).
# Secure Authentication and Credential Establishment in Narrowband IoT and 5G
## 4 The Use Case of Precise Agriculture
When the bootstrapping has finished, the IoT device and the IoT controller will obtain the required credential material, as described in Section 5. After that, the IoT device can perform a secure data communication with the IoT controller, employing the credentials generated during bootstrapping. In Stage 2.A, the IoT device can use the best suited EAP lower layer to exchange data with the IoT controller in a secure and trustworthy manner. Finally, in Stage 2.B, the IoT controller without computing limitations can exchange data with the smart agriculture platform in a secure way using the HTTPS protocol integrating HTTP with Transport Layer Security (TLS) [41].
# Traffic Aware Scheduler for Time-Slotted Channel-Hopping-Based IPv6 Wireless Sensor Networks
## 3 Related Work
### Adaptive Approaches
#### 3.2.6 Msf  
The minimal scheduling function (MSF) [14] is designed to work on top of the 6 TiSCH operation sublayer (6 top) protocol [15]. It allocates two types of cells, autonomous and negotiated. The autonomous cells, one Rx and one Tx cell, are allocated to ensure the minimum amount of resources for bidirectional communication between neighbors. The Tx-negotiated cells are added when bursts of traffic are detected in the network. The negotiation implies the exchange of messages between the nodes, making this solution a distributed one.