esa-sceva/satcom-chunk-collection huggingface dataset:
# Post-Quantum Migration of the Tor Application
## 2 Literature Review
### Schemes  
Based on research papers and Tor's official source code, this section outlines the used schemes and their potential replacement candidates, each addressing specific security requirements. NIST defined the security level of PQC from 1 to 5. The first level is equivalent to the 128-bit traditional security level (strength of AES-128).  
TAP [9] implemented a standard of at most 80-bit security [25], through the use of RSA with a 1024-bit modulus and DH 1024 in its key exchange. Since the NTOR [12] paper, Tor cryptography publications [11; 13] have agreed on the necessity of at least a 128-bit security standard. Tor has started to adopt Curve25519 (also written as x25519) in most key-enabled cryptography processes; nonetheless, RSA is still used as an identity key in the relay layer (see Table 11) and in TLS 1.2.  
[PERSON] et al. [24] compiled a comparative inventory of the vulnerable protocols and evaluated the corresponding mitigation strategies. The paper presents a risk assessment framework that includes multiple known attack strategies based on the STRIDE (Spoofing, Tampering, Repudiation, Information Disclosure, Denial of Service, and Elevation of Privilege) model. They differentiate the risk into two categories: the algorithmic level, where a mathematical assumption is not strong enough, and the protocol level, where the implementation presents a vulnerability, such as side-channel or fault-injection attacks. Hybrid implementation, aiming to protect the new cryptography migration from these risks, is highlighted in the context of a crypto-agility approach. However, due to Tor's infrastructure, the computational load, packet size, and bandwidth requirements need to be considered.  
Table 1 breaks up Tor's layers by schemes (currently used), their security level, key size in bytes, and role. When looking at the replacements, the benchmarking of PQC schemes tends to highlight the overall performance of lattice-based algorithms: Saber and Kyber as KEMs, and Dilithium as a signature scheme, in constrained environments, such as running on a Raspberry Pi [26]. Nevertheless, Saber was judged to be less efficient than Kyber in a broader range of environments [27].  
The following first covers quantum-safe key exchange schemes, followed by a review of PQC signature algorithms.
# Machine Learning-Based 3D Soil Layer Reconstruction in Foundation Pit Engineering
## 3 Data Enhancement Algorithm of Soil Layers
### Generation Algorithm of Virtual Boreholes  
Based on the soil layer data, we designed a borehole generation algorithm. The borehole locations are randomly selected from the uppermost grid points of the soil layer. First, the positions of the boreholes are determined based on these grid points. Subsequently, a set of boreholes is randomly generated according to the soil layer characteristics, and the thickness of each layer at each borehole location is calculated using the interface coordinates between the layers. The schematic diagram of the algorithm is illustrated in Figure 7.  
The borehole generation algorithm consists of three steps:  
Step 1. Select the grid point coordinates. The positions of the boreholes are all taken on the grid points.  
Step 2. Acquire the borehole elevation and plane coordinates. The borehole elevation is the height coordinate of the grid point. We use the plane coordinate of the selected grid point as the borehole coordinate.  
Step 3. Parse the soil layer that the borehole passes through from the soil layer data and extract the label of the soil layer. Then, calculate the thickness of each identified soil layer using the formula provided in Formula (2).  
\[thickness_{i}=z_{i}-z_{i+1}, \tag{2}\]  
In the above formula, \(thickness_{i}\) is the thickness of the \(i^{th}\) soil layer at the borehole location; \(z_{i}\) is the height coordinate value of the grid point through which the borehole passes at the \(i^{th}\) interface; and \(z_{i+1}\) is the height coordinate value of the grid point through which the borehole passes at the \((i+1)^{th}\) interface.  
The generated borehole data are represented by the set \(Z\), where \(Z=\{z_{1},z_{2},\ldots,z_{n}\}\). This set \(Z\) contains \(n\) boreholes. For each \(z_{i}\in Z\), there exists a set \(z_{i}=\left\{e_{pi},l_{pi},t_{pi},c_{pi}\right\}\), where \(e_{pi}\) is the elevation value of the \(i^{th}\) borehole, \(l_{pi}\) is the soil layer through which the \(i^{th}\) borehole passes, \(t_{pi}\) is the thickness of each soil layer through which the \(i^{th}\) borehole passes, and \(c_{pi}\) is the plane coordinate of the \(i^{th}\) borehole.
# Research on the Remaining Life Prediction Method of Rolling Bearings Based on Multi-Feature Fusion
## 4 Experimental Verification
### Case Study 2: Predicting the RUL of Bearings Using the XJTU-SY Dataset  
#### 4.3.1 Introduction to the Dataset  
Data set for the XJTU-SY rolling bearing accelerated life test [30]. The test rig for this collected dataset is shown in Figure 12. The platform is used to perform accelerated degradation tests on bearings by generating radial force from a hydraulic loading system, and the AC motor's speed is controlled by its speed controller. The gathered vibration signals were split into horizontal and vertical directions, and the test's sample frequency was set to 25.6 kHz, the sampling interval was set at 1 min, and the sampling time was 1.28 s. Figure 13 shows the physical diagrams of bearing inner ring wear, cage fracture, outer ring wear, and outer ring cracking. Different deterioration patterns under various operating situations are caused by these bearing degradation trends. Two different operating conditions were selected for the XJTU-SY test, as shown in Table 5. For the first scenario, the motor speed is 2100 r/min, and the load is 12 kN; for the second scenario, the motor speed is 2250 r/min, and the load is 11 kN; both conditions contain five different bearings each.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Method 1** & **Method 2** & **Method 3** & **Proposed Method** \\ \hline _MAE_ & 13.4 & 8.71 & 6.32 & 5.74 \\ _RMSE_ & 14.5 & 9.43 & 8.51 & 7.31 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of ablation experiments with different comparison methods under the PHM2012 dataset.  
Figure 12: Bearing test bench of the XJTU-SY dataset.
# A Framework to Model Bursty Electronic Data Interchange Messages for Queueing Systems +
## 3 Results
### Model Non-Batch By Split Count  
_Service Time And Inter-Arrival Time Modelling_  
\"Non-Batch By Split Count\" is the complement of \"Batch By Split Count\"; it considers the messages where exactly one XML document is associated with the message. We modelled the service and the inter-arrival times by applying different techniques of  
Figure 16: Batch by split count, log-normal model results.  
Figure 15: Batch by split count, classification model.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**AD Test** & **Normal** & **Log(n+1)** & **Sqrt(n)** & **Exp(n)** & **Exp(log(n+1))** & **Sqrt(log(n+1))** & **Log(log(n+1)+1)** & **Sqrt(exp(n))** \\ \hline log-normal & 39 & 51 & 39 & 1500 & 1300 & 51 & 63 & 1500 \\ Burr & 41 & 45 & 41 & 640 & 470 & 45 & 49 & 640 \\ \hline \hline \end{tabular}  
In the interests of space, only AD scores are shown. The majority of \(p\)-values are rounded to 0.00.  
\end{table}
Table 12: Batch by split count, ST \(\leq\) 1 second, filter \(>\)2, zero removed.  
the classification model. The results of our analysis indicate that we could only fit a Burr distribution to the tail of the data, as per Figure 17.  
The results of the Anderson-Darling test in Table 13 confirm that we can fit the tail of the service times to a Burr distribution. Note that we applied a constant offset of 1 to the dataset when it passed the AD GoF test. The CDF plot in Figure 18 shows both the empirical distribution and the fitted Burr distribution. From the P-P plot, we note that no observations appear to deviate significantly from the line.  
For the head of the service times data (service times \(\leq 1\) s), we could not parametrically fit the data to a parametric distribution, irrespective of transformation or implementing partitioning methods.  
Now we consider inter-arrival times partitioned into the head and a tail with a boundary of 1 s. For the tail (\(>\)1 s), the results of the AD tests conclude (Table 14) that these filtered data do not fit a parametric distribution. However, we observe that a no-transform and a square root transform (highlighted in bold) are a relatively close fit to a Burr distribution but do not pass the AD test. Table 14 shows only the closest model to a parametric distribution. When modelling the head of the inter-arrival times, we found evidence of correlation, which is included in our conference paper [11].  
\begin{table}
\begin{tabular}{c c c} \hline \hline AD Score & \(p\)-Value & Test \\ \hline
1.2 & 0.3 & Pass \\ \hline \hline \end{tabular}
\end{table}
Table 13: AD test normal period, ST, tail of data.  
Figure 17: Non-batch by split count classification.  
Figure 18: Normal Period, Split = ’1’, Burr Fitting, Service Times - \(>1\) s.
## 4 Physical Features and Response Characteristics
### Defining the Features
Increasing the average channel quality across the network will increase PRR dramatically affecting the evaluation metrics. Taking an average of the channel quality of all links to a node may not fully represent the quality of the channels that actually get used within the network and as a result may not fully represent performance. This can occur when many \"fringe\" neighbours appear to a node, none of which are \begin{table}
\begin{tabular}{l c c} \hline \hline
**Variable Pair** & \(\rho\) & \(p\)**-Value** \\ \hline Density-Scale & \(-0.5493\) & \(0.0009\) \\ Size-Density & \(8.9518\times 10^{-4}\) & \(0.9832\) \\ Scale-Size & \(0.5843\) & \(0.0001\) \\ \hline \hline \end{tabular} Source of data from experiments described in Section 7.  
\end{table} 
Table 1: Pearson correlation between dependent variables. Figure 4: Relationship between physical features in simulations. chosen for communication. A more accurate description of the used channel can be achieved by also using the best quality channel available to each node. A number of measurements are used to assess channel quality including Link Quality Indicator (LQI) and Received Signal Strength Indication (RSSI). Such measurements are available using the CC2420 [29] chipset typically used for IoT communications.
# Online Detection and Fuzzy Clustering of Anomalies in Non-Stationary Time Series +
## 3 Anomaly Detection and Clustering
### ECDF-Based Fuzzy c-Means Clustering  
Once multiple anomalies are detected, we may follow up with clustering. We propose a method that represents anomalous segments (of potentially different lengths) by empirical cumulative distribution functions (ECDFs). Our ECDFs are discretized bins since fuzzy c-means clustering works well with vectors. The number of bins is of course adaptable, and for the subsequent experiments, 20 bins was chosen after exploratory analysis as a value which balanced the ability to capture the structure of anomalies with the ability to make reliable inference. The result is a ECDF vector \(A_{i}\) associated with each anomaly \(\mathbf{a}_{i}\), where \(A_{ij}=|\mathbf{a}_{i}|^{-1}\sum_{a\in\mathbf{a}_{i}}\mathbb{I}\{a\leq-1+0.1j\}\) for \(j=1,\dots,20\). Since we standardise the residuals to \([-1,1]\), we always have \(A_{i,20}=1\).  
Fuzzy c-means clustering is a soft partitioning method whose output assigns each vector \(A_{i}\), \(i=1,\dots,N\) a membership degree \(\mu_{ij}\geq 0\) for each cluster \(j=1,\dots,M\) such that \(\sum_{j=1}^{M}\mu_{ij}=1\)[15].  
The clustering is chosen as a solution to the optimisation problem:  
\[\min_{\mu}\mathcal{J}_{m}=\sum_{i=1}^{D}\sum_{j=1}^{N}\mu_{ij}^{m}||A_{i}-C_{j }||^{2}, \tag{5}\]  
where \(D\) and \(N\) are the numbers of ECDF vectors and clusters, respectively. The parameter \(m\) is a fuzzy partition exponent for the degree of fuzzy cluster overlap and is set to be 2, based on the default setting of common fuzzy clustering. The \(A_{i}\) and \(C_{j}\) represent the \(i\)th ECDF vector for anomalies and the \(j\)th cluster centre vector.  
The following steps explain the fuzzy clustering mechanism [15]:  
1. Randomly initialise the degree of membership \(\mu_{ij}\).
2. Calculate the cluster centre vectors with: \[Cj=\frac{\sum_{i=1}^{D}\mu_{ij}A_{i}}{\sum_{i=1}^{D}\mu_{ij}},\quad j=1,\dots,N.\] (6)
3. Update the degree of membership using:  
Figure 2: Structure of implemented NARX-SCAPA framework.  
\[\mu_{ij}=\left(\sum_{l=1}^{N}\left(\frac{||A_{i}-C_{j}||}{||A_{i}-C_{k}||}\right)^ {2}\right)^{-1}\ \ \ i=1,\ldots,D,\ j=1,\ldots,N. \tag{7}\]
4. Compute \(\mathcal{J}_{m}\) with updated centres and membership degrees.
5. Repeat steps 2 to 4 until the distance between the old and new objective function is smaller than the minimum improvement threshold.
# A New Internet of Things Hybrid VLC/RF System for m-Health in an Underground Mining Industry
## 2 Wireless Communications Technologies for m-Health
### Optical Wireless Communications  
* **IR:** IrDA provides specifications for a complete set of protocols for wireless infrared communications. The main characteristics of this kind of wireless optical communication are physically secure data transfer, line-of-sight (LOS), and a very low bit error rate (BER) that makes it very efficient. Works in the frequency band of 100 to 200 THz, has data rates supported at 1 Gbps and a maximum range of 5 m [12].
* **VLC:**[13] The operation of Li-Fi is quite simple, since the data stream is sent by an LED bulb for transmission, which is simply embedded in the beam of light when we see it illuminate the surrounding environment. The 0s and 1s representing the information being sent are presented by the very fast dimming of the LED bulb. The use of visible light means that the Li-Fi signal does not pass through walls, so it is only suitable for display within a room [14; 15; 16; 17; 18].
Sensors_**2014**, _14_, 2595-2618; doi:10.3390/s140202595  
_Sensors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
**Techniques for Clutter Suppression in the Presence of Body Movements during the Detection of Respiratory Activity through UWB Radars**  
**[PERSON] *, [PERSON] and [PERSON]**  
Department of Electronic, Electric and Automatic Control Engineering, Universitat Rovira i Virgili (URV), Av. Pa \(\bar{\text{s}}\)os Catalans 26, Campus Sescelades, Tarragona 43007, Spain;  
E-Mails: [EMAIL_ADDRESS] (D.G.); [PERSON] (R.V.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +34-977-558-668; Fax: +34-977-559-605.  
_Received: 20 December 2013; in revised form: 16 January 2014 / Accepted: 24 January 2014/ Published: 7 February 2014_  
**Abstract:** This paper focuses on the feasibility of tracking the chest wall movement of a human subject during respiration from the waveforms recorded using an impulse-radio (IR) ultra-wideband radar. The paper describes the signal processing to estimate sleep apnea detection and breathing rate. Some techniques to solve several problems in these types of measurements, such as the clutter suppression, body movement and body orientation detection are described. Clutter suppression is achieved using a moving averaging filter to dynamically estimate it. The artifacts caused by body movements are removed using a threshold method before analyzing the breathing signal. The motion is detected using the time delay that maximizes the received signal after a clutter removing algorithm is applied. The periods in which the standard deviations of the time delay exceed a threshold are considered macro-movements and they are neglected. The sleep apnea intervals are detected when the breathing signal is below a threshold. The breathing rate is determined from the robust spectrum estimation based on Lomb periodogram algorithm. On the other hand the breathing signal amplitude depends on the body orientation respect to the antennas, and this could be a problem. In this case, in order to maximize the signal-to-noise ratio, multiple sensors are proposed to ensure that the backscattered signal can be detected by at least one sensor, regardless of the direction the human subject is facing. The feasibility of the system is compared with signals recorded by a microphone.  
keywords: breathing monitoring; sleep apnea; vital signs; ultra-wideband radar +
Footnote †: journal: Journal of Sound and Vibration
## 1 Introduction  
Very recently, two-dimensional (2D) materials have become the focus of attention in the academic community for potential applications in nanoelectronic devices, gas sensing, photo-catalysis, energy storage, and optical devices [1; 2; 3; 4], because of their excellent physicochemical properties, including high electron mobility, high specific surface area, super-capacitors, as well as desirable optical properties. With the success of graphene in 2004 [5], scholars began to pursue other graphene-like nanomaterials with similar structures and properties [6; 7], which largely facilitated the development of materials science. Among the novel members, the group III-V nitrides [8; 9; 10; 11], including BN, AlN, and InN nanosheets, raise considerable attention for many applications due to their supreme semiconducting property.  
Particularly, the InN monolayer has demonstrated superior thermal stability, high carrier mobility, and a sizable band gap [12; 13; 14]. Moreover, its large specific surface area and favorable chemical reactivity confers the potential application of the InN monolayer to be a gas sensor for typical gases. For example, the adsorption behavior of a pristine InN monolayer to several small gas molecules was implemented in a theoretical study [15], which implies its strong potential for sensing applications given the comparable performance with graphene. [PERSON] [16] et al. investigated the Cu-doped InN monolayer for sensing decomposed SF\({}_{6}\) species and found that it possesses enhanced behavior in comparison with the intrinsic counterpart. [PERSON] [17] et al. proposed Pd, Pt, Ag, and Au decorated InN monolayers for NO\({}_{2}\) sensing applications in order to exploit the potential of transition metal (TM) doped InN monolayers as toxic gas sensors. These findings suggest the InN monolayer is a promising sensing material and the TM-doped surface could be an even more extraordinary candidate for gas detection, as verified in the carbon-based surfaces [18; 19] and transition metal dichalcogenides [20].  
CO and NO are widely recognized as toxic gases that exert great threat to our living environment and to human health. Therefore, detection of these gases is significant for guaranteeing the living condition around us. Nanomaterials based on carbon nanotubes [21] and MoS\({}_{2}\) monolayers [22] were proposed to realize this purpose and obtain desirable results, on which TM atoms largely contribute to enhancing the sensitivity of nano surfaces. To the best of our knowledge, there has been no report regarding a TM-doped InN monolayer for adsorption and sensing such two toxic species. In this regard, we studied the adsorption and sensing behaviors of the Pd-doped InN (Pd-InN) monolayer upon CO and NO gases by the first-principles theory, in order to shed light on the potential of the Pd-InN monolayer for gas sensing exploration. Based on our calculations, it could be concluded that Pd-InN is a promising candidate to detect NO and scavenger CO, which may facilitate the development of resistance-type sensors or gas adsorbents for applications in environment monitoring and industrial manufacturing.
# A Reliable Handoff Mechanism for Mobile Industrial Wireless Sensor Networks
## 1 Introduction
This project is a collaboration with a company which specializes in industrial machine production. All the welding machines in the production site should be monitored and controlled. Since the parameters, e.g., electric current, voltage, output power and other states, need to be reported to a server in time, the network communication should be reliable and real-time. However, the welding machines always operated to the huge metallic devices by workers and it is not necessary to equip lots of machines due to the cost of installation. Since a welding machine need to weld any part of the device or the other metallic devices at any time, the mobility support in the network communication becomes an urgent requirement. Moreover, the multi-hop wireless sensor network needs to maintain the delivery of monitored data. The inefficient handoff may break the delivery and even increase the transmission delay. The common problem for mobility supporting is the ping-pong effect that a node frequently triggers the handoff by some factors like interference or mobility. Once the node registers a new link, it may trigger the handoff again, which consumes much energy and increases the end-to-end delay. Therefore, this paper proposes an effective handoff mechanism to solve the problem of ping-pong effect during handoff and the registration after handoff. We start by integrating multiple metrics to hold the switching handoff and guarantee the accurate trigger by using the theory of fuzzy logic. Then, to avoid redundant registration, we divide the scenario of handoff into two cases caused by static link failure and actual movement, respectively. In our mechanism, the mobile nodes identify which cases to process by evaluating their mobile state instead of registering a new link immediately. This means that unnecessary handoff and registration are avoided by augmenting the independence of mobile nodes. Another contribution of this paper is that the promotion of packet delivery and the decrease of energy consumption are proved by not only conducting an experiment on our testbed but implementing it in practice. The rest of this paper is organized as follows: Section 2 presents related work and Section 3 describes the prototype of the system and the network models. The condition of triggering and our proposed mechanism are presented in Sections 4 and 5 respectively. Section 6 provides the experimental evaluation of both testbed and factory. Finally, the paper is concluded in Section 7.
# An Improved Target Network Model for Rail Surface Defect Detection
## 3 Experimental Design  
### Dataset of Rail Surface Defects  
In this study, the dataset used is a publicly available dataset [28], and the railway surface images were captured by the Railways Research and Technology Center (DATEM) using a measurement train on the Ankara-Konya and Ankara-Eskisehir lines in Turkey. The dataset categorizes images into four different classes based on the types of defects found on the track surface, namely Healthy, Joint, Squats, and Severe Squats (Ssquats), with 492, 408, 608, and 330 samples in each category, respectively. The pixel size of the rail defect images in the dataset is 224 \(\times\) 224. Figure 6 represents a random selection of images from the dataset. Techniques such as rotation and mirroring were used to enhance the robustness of the dataset. We also expanded the existing dataset by rotating and mirroring images, performing enhancement operations on four types of images. Image mirroring, rotation, and other operations have been proven to be effective in training object detection algorithms [39]. During the data preparation phase, training images were annotated using LabelImg (version: 1.8.6). After dataset preparation, 80% of the data was used for training and validation, while the remaining 20% was used for testing. Moreover, all experimental studies utilized the same training, validation, and test sets to ensure fairness in comparisons.  
Figure 5: The bounding box sizes clustering using the k-means algorithm.