esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Feature Extraction and Application to Floor Classification and Landmark Detection
### Semisupervised Discriminant Analysis
#### 3.2.4 Semisupervised Combination of FDA and PCA  
To extend to a semisupervised version, it modifies the original FDA in a way that utilizes the unlabeled data. Let \(\{(y_{i},w_{i})\}_{i=1}^{l+u}\) be the set of both the labeled and unlabeled data points, where \(l\) is the number of the labeled data points and \(u\) is the number of the unlabeled data points. If \(\{(y_{i},w_{i})\}\) is the labeled data, \(w_{i}\in\{1,2,\ldots,C\}\) denotes the class labels; for the unlabeled data, \(w_{j}=0\). Analogously to traditional semisupervised discriminant analysis, we define the new weight matrices, modified from Equations (27) and (28):  
\[W_{ij}^{bS} = \left\{\begin{array}{rl}\frac{1}{l}-\frac{1}{l}&\text{if $w_{i}=w_{j}=c$}\\ \frac{1}{l}&\text{if $w_{i}\
eq w_{j}$}\\ \frac{1}{u}&\text{if $w_{i}=0$ or $w_{j}=0$}\end{array}\right. \tag{29}\] \[W_{ij}^{mS} = \left\{\begin{array}{rl}\frac{1}{c}&\text{if $w_{i}=w_{j}=c$}\\ 0&\text{if $w_{i}\
eq w_{j}$}\\ 0&\text{if $w_{i}=0$ or $w_{j}=0$}\end{array}\right. \tag{30}\]  
The corresponding matrices \(S^{bS}\) and \(S^{wS}\) are  
\[S^{bS} = \frac{1}{2}\sum_{i,j=1}^{l+u}W_{ij}^{bS}(y_{i}-y_{j})(y_{i}-y_{j} )^{\text{T}}, \tag{31}\] \[S^{wS} = \frac{1}{2}\sum_{i,j=1}^{l+u}W_{ij}^{wS}(y_{i}-y_{j})(y_{i}-y_{j} )^{\text{T}}. \tag{32}\]  
The generalized eigenvalue problem for the semisupervised version is  
\[T^{\text{semi}} = \operatorname*{argmax}_{T}\left[\text{tr}\left(T^{\text{T}}S^{bs }T(T^{\text{T}}S^{us}T)^{-1}\right)\right], \tag{33}\] \[S^{bs} = \alpha_{bal}S^{bS}+(1-\alpha_{bal})S^{p},\] (34) \[S^{ws} = \alpha_{bal}S^{wS}+(1-\alpha_{bal})I, \tag{35}\]  
where \(I\) is the identity matrix and \(\alpha_{bal}\) is a parameter to adjust the balance between PCA and FDA.
# Cutting Speed and Feed Influence on Surface Microhardness of Dry-Turned UNS A97075-T6 Alloy
###### Abstract  
In this work, an analysis of the cutting speed and feed influence on surface roughness and microhardness of UNS A97075-T6 alloy, turned under dry conditions, was carried out. The results were compared before and after a corrosion process. The influence of these cutting parameters on each of these variables was analyzed, as well as the possible interrelation between them. The microgeometrical deviations showed a general trend to increase with feed. However, no significant modifications were observed as a function of the cutting speed. This trend was softer after the corrosion process, due to the surface alterations produced by pitting corrosion, which resulted in higher dispersion of the experimental data. In addition, a surface microhardness increment was observed in all samples, after machining and before corrosion, regardless of the cutting parameter values. The experimental results revealed that the mechanical effects, produced by the feed, should not be neglected against the thermal effects, produced by the cutting speed, within the range of the tested cutting speed. Finally, the corrosion process negatively affected the microhardness, but it was not possible to establish a direct relationship between the cutting parameters, surface roughness, and microhardness after a corrosion process.  
dry turning; surface roughness; microhardness; UNS A97075; aluminum alloys Article
# A Novel Ranging Technique Based on Optical Camera Communications and Time Difference of Arrival
## 3 System Description
This is the main difference with the former system, in which the optical signal consists of a short pulse when the sound signal is detected. Therefore, the BS camera gets an optical signal whose duration is equal to the time difference of arrival at the MS.  
To allow a camera-based detection, the MS does not emit a single pulse, but a pulse train. The period of this signal significantly affects the estimation error performance, as discussed below. Once the camera retrieves enough frames, the system will proceed to the distance estimation using image processing. This is described in the next section. Figure 2: Block diagram of the proposed scheme.
# A LoRaWAN Architecture for Communications in Areas without Coverage: Design and Pilot Trials
## 3 LoRaWAN Wireless Technology
### LoRaWAN Architecture  
The typical architecture of a LoRaWAN network [30] is based on a star topology consisting of motes or end nodes, gateways and the network and application servers (see Figure 4):  
* End node (mote): low-power communication device that collects data from sensors.
* Gateway: receives transmissions from the motes and forwards the information to the network servers and vice versa. Its functionality is similar to that of a bridge.
* Network server: routes the information packets between the motes and the application servers and vice versa, depending on the predefined topic or application.
* Application server: manages the information packets transmitted from the end nodes. Then, it is possible to process them depending on the type of information received.  
In addition, LoRaWAN provides certain pre-established security functionalities in the network [31], as shown in Figure 4:  
* Integrity: establishes an MIC code using the NwkSSKey to provide the integrity of the information transmitted between the fine node and the network server.
* Confidentiality: uses information encryption with AES-128 and the AppSKey key to provide confidentiality of the messages between the end nodes and the application servers.  
In addition, LoRaWAN allows the activation of devices by Activation By Personalization (ABP), in which the keys are pre-recorded in the mote, or by Over-the-Air Activation (OTAA), in which the keys are generated after a signal is sent.
## 4 Mechanical Property Evolution Analysis  
### Failure Shear Stiffness Kf  
Shear stiffness is an important parameter characterizing soil shear deformation properties. According to [33], the shear stiffness corresponding to 15% of the strain in the soil samples can be defined as the failure shear stiffness. In this study, the failure shear stiffness during the first push shear on the soil samples with different coarse grain contents (Figure 5) is considered the failure shear stiffness of each coarse grain content group, denoted by \(K_{f}\). The test results are summarized in Table 4.  
The relationship between the failure shear stiffness, \(K_{f}\), and coarse particle content, \(P_{5}\), was fitted and analyzed, and the results are shown in Table 5.  
It can be seen from Table 5 that the linear fitting of the relationship between the failure shear stiffness, \(K_{f}\), and the coarse particle content, \(P_{5}\), is the best, as shown in Figure 11.  
According to Figure 11, the relationship between the failure shear stiffness, \(K_{f}\), and the coarse grain content, \(P_{5}\), of the soil samples can be expressed as follows:  
\[K_{f}=0.0217P_{5}+1.4316 \tag{5}\]  
In Equation (5), the linear fit correlation coefficient is 0.9655, and the soil sample's failure shear stiffness, \(K_{f}\), has a good linear correlation with the coarse grain content, \(P_{5}\).  
\begin{table}
\begin{tabular}{c c c c} \hline
**Coarse Grain** & **Shear Stress \(\tau\)/kPa** & **Strain** & **Failure Shear Stiffness** \\
**Content \(P_{5}\)/\%** & & \(\epsilon\)/\% & \(K_{f}\) (MPa/mm) \\ \hline
27.56 & 300 & 15 & 2.000 \\
38.02 & 351 & 15 & 2.340 \\
61.34 & 398 & 15 & 2.653 \\
75.43 & 470 & 15 & 3.133 \\ \hline \end{tabular}
\end{table}
Table 4: Failure shear stiffness, \(K_{f}\), of argillaceous silststone rockfill materials with different coarse grain contents.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Model** & **Fitting Model Expression** & **Degree of Freedom** & **Residual Sum of Squares** & **Mean Square** & **Fitting Coefficient** \\ \hline Exponential model & \(K_{f}=1.6165^{0.0086P_{5}}\) & 2 & \(2.51\times 10^{-2}\) & 13.15 & 0.9343 \\ Linear model & \(K_{f}=0.0217P_{5}+1.4316\) & 2 & \(2.40\times 10^{-2}\) & 0.67 & 0.9482 \\ Logarithmic model & \(K_{f}=1.0267^{1}\ln N-1.4196\) & 1 & \(2.45\times 10^{-2}\) & 0.34 & 0.9462 \\ Power model & \(K_{f}=0.5022P_{5}\)\({}^{0.4168}\) & 2 & \(3.01\times 10^{-2}\) & 13.15 & 0.9250 \\ \hline \end{tabular}
\end{table}
Table 5: The fitting relationship between failure shear stiffness, \(K_{f}\), and coarse particle content, \(P_{5}\).
## 3 Results  
### Carrier Concentration Difference between Maximum ZT and Maximum Power Density  
In previous research, it was shown that low thermal conductivity is advantageous for energy harvesting on the human body while the ZT remains at the same value [20]. However, in the practical engineering of a material, such as doping level control, ZT also changes while the thermal conductivity is adjusted [39]. Therefore, in order to understand the optimal engineering direction of the thermal conductivity, a device simulation to find the optimum point of the power density should be performed concurrently.  
The material ZT of P-type BiSbTe\({}_{3}\) and the power density of the device according to carrier concentration are shown in Figure 3. Figure 3 shows that the maximum point of ZT and the power density are misaligned. When the material is engineered at the concentration that maximizes ZT as the conventional methods do, the device made of the material generates 28.8% less power density than the maximum power density.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Variables** & **Values** \\ \hline Fill factor (FF) & 10\% \\ TE leg height & 2.5 mm \\ TE leg width & 2.0 mm \\ Contact resistance & \(10^{-6}\)\(\Omega\)-cm\({}^{2}\) \\ Copper pad width & 1.4 \(\times\) Leg width \\ Copper pad thickness & 35 \(\upmu\)m \\ Thermal conductivity of filler & 0.03 W/m\(\cdot\)K \\ \hline \hline \end{tabular}
\end{table}
Table 1: Variables related to the device structure. All variables were applied to values that could be used in a human body application.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Variables** & **Values** \\ \hline Body core temperature & 37 \({}^{\circ}\)C \\ Ambient temperature & 25 \({}^{\circ}\)C \\ Heat transfer coefficient (skin) & 39.2 W/m\({}^{2}\cdot\)K \\ Heat transfer coefficient (air) & 10.5 W/m\({}^{2}\cdot\)K \\ \hline \hline \end{tabular}
\end{table}
Table 2: Variables related to human body application. Heat transfer coefficients are the calculated values from experimental data of the human body application.  
As shown in Figure 3, the optimal point of the power density is shifted to the left from the optimal point of the ZT. As shown in Equation (2), among the variables that affect the power density, the three key properties of the ZT are the only factors that are controlled by the carrier concentration. Among the three properties, it can be inferred that the thermal conductivity has a stronger influence on the power density than the ZT. Since the left side of the graph means lower thermal conductivity of the material, this interpretation is consistent with the existing theory that low thermal conductivity is more advantageous for energy harvesting on the human body [26].
## 2 Security Considerations for Marine Network Protocols
### Authorization  
Many protocols incorporate a form of an ID tag to identify the communication nodes, along with their privileges, as well as access rights, inside a network. However, without a proper method of inspection, a rogue device can generate its own ID with high level of authorization to gain access to sensitive data [15].
## 3 Results  
There were 1658 publications identified by the search strategy, and 38 studies were included in the synthesis of the data summary table. The selection process is outlined in the PRISMA flowchart (Figure 3). Notably, [PERSON] and colleagues from the Hebrew University (Israel) have published 27 studies in a broad range of disease settings and interventions from 1992 to 2018 [33, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62]. Two in vitro studies [63, 64] and five retrospective studies reviews [28, 29, 30, 35, 65] from this group are excluded from the synthesis of data summary table because they did not meet the inclusion criteria and to avoid duplication of information. [PERSON] and colleagues from the Centre of Advanced Studies, University of Defence, (Hradec Kralove, Czech Republic) published five studies from 2009 to 2011 [66, 67, 68, 69, 70]. This group have also published three studies utilizing Square Wave Voltammetry (SWV) and Differential Pulse Voltammetry (DPV) [71, 72, 73]. These techniques have not been included in this review, as they represent significant departures from the standard CV [74]. [PERSON] and colleagues from the University of Auckland, New Zealand published two studies examining CV in the context of acute pancreatitis and shock [31, 32]. Four other research groups published the remaining four studies from Italy, the Czech Republic, France and India, respectively [34, 75, 76, 77].  
Figure 3: PRISMA flowchart for article identification & selection.
## 3 Results
### Discussion  
In this section, we engage in a comparative analysis of our ensemble soft voting approach against existing models in the literature across three distinct domains: driving style, road surface quality, and traffic conditions. Our ensemble method, which leverages a combination of machine learning algorithms, has shown competitive and promising results. For driving style classification, our ensemble model slightly outperformed the CNN-LSTM model referenced in [50], with an accuracy of 93.89% compared to 93.81%. This small improvement suggests that even though deep learning models like CNN-LSTM are very good, ensemble methods might be able to find more patterns by combining different algorithmic points of view. This could lead to a more accurate classification in some situations.  
Using a CNN model, our work in road surface classification achieved an accuracy of 97.26%, slightly below the 97.50% reported in [51]. This small difference shows that CNNs are good at finding spatial hierarchies and patterns in da-ta, but ensemble methods are  
Figure 12: Algorithm improving driving style in connected vehicles (IDSCV).  
still very useful for making more complex decisions, especially when model variance and interpretability are important factors.  
With an accuracy of 97.14%, our model significantly improved traffic prediction, surpassing the 81% accuracy of the standalone random forest model in [52]. Compared to the accuracy of 98.7%, there is a slight difference [41]. This huge improvement shows how well the ensemble soft voting method works for dealing with the complexity and unpredictability of traffic conditions. Combining different models may help us understand these changing environments better.  
The comparison results show that our ensemble soft voting method is very good. It not only works as well as specialized deep learning models, but it also does a much better job of predicting traffic than traditional single-model methods. It suggests a robust generalization capability and highlights the potential of ensemble methods for adapting to a variety of complex real-world conditions. Show Table 5.
## 2 Signal and System  
### Signal Introduction  
This section briefly introduces the target signals (Bluetooth, Wi-Fi, ZigBee) in the paper and includes basic information such as channel parameters, transmission power, and access method.  
* Bluetooth [21]  
Bluetooth is a low-power communication technology, generally used for short-distance wireless communications. The transmission power can generally be divided into three classes, namely 100 mW (class 1), 2.5 mW (class 2), and 1 mW (class 3). The modulation method of Bluetooth is Gaussian Frequency Shift Keying (GFSK), the transmission rate can reach 1 Mb/s. Bluetooth can transmit on 40 channels in the range of 2402-2480 MHz, the width of each channel is 2 MHz. When communicate with other nodes, frequency-hopping spread spectrum (FHSS) is used on 40 channels. In a non-connected broadcast mode, Bluetooth signals are broadcast on three fixed channels 37, 38, and 39. The receiver can identify the signal source by access code in broadcast data packets. The Bluetooth channel distribution is shown in the Figure 4.  
* WiFi [22]  
The IEEE 802.11b/g/n protocol, commonly known as WiFi uses two modulation schemes. Direct sequence spread spectrum (DSSS) is used for lower bit rates transmission, and orthogonal frequency division multiplexing (OFDM) is used for higher bit rates transmission. There are 13 channels defined in the 2.4 GHz ISM frequency band (different regions may vary), the width of each channel is 20/22 MHz. The total frequency band width is 83.5 MHz so there is overlap between the channels, as shown in Figure 5. The maximum allowable power is 10 mW/MHz. The wireless access point (AP) periodically sends broadcast frames about 10 times per second to inform the existence of the WiFi network, which contains Service Set Identifier (SSID) information.  
* ZigBee [23]  
ZigBee is a low-rate wireless network, following the IEEE 802.15.4, using DSSS modulation. A total of 16 channels are defined in 2.4 GHz band and do not overlap; each channel has 2 MHz bandwidth with a 3 MHz guard interval, which is shown in Figure 6. The communication range is about 10 m, transmission rate can reach 250 kb/s. When a  
Figure 4: Bluetooth channel distribution.  
Figure 5: WiFi channel distribution.  
new ZigBee network is established, the energy detection function is used to determine the operating channel. However, dynamic channel selection is not supported.  
The Figure 7 shows the relationship between Bluetooth, WiFi, ZigBee on the 2.4 GHz ISM frequency band. It can be seen that they completely overlap in the frequency domain [24].
# TETES: Trust Based Efficient Task Execution Scheme for Fog Enabled Smart Cities
###### Abstract  
Quality lifestyle leads to increasing trends in smart cities by offering modern communication and information technologies. Smart cities offer multiple applications with smart management of resources such as smart agriculture, Intelligent transportation systems, waste management and energy management. These applications are based on IoTs that are composed of sensor networks with limited processing and computing capabilities and are connected with different types of networks. Due to limited computational capability, IoT sensor nodes require more time to compute different tasks and are required to offload some tasks to remotely placed cloud servers for task execution. Fog nodes are preferred over the cloud as they are placed in close access to IoT nodes distributed in different networks. Different types of networks make it more vulnerable to malicious attacks. Malicious nodes offload complex and high computing tasks to fog nodes to compromise their performance and create delays in the computing tasks of legitimate nodes. In addition, fog nodes even after removing the malicious nodes are unable to process all the legitimate tasks within a specific time frame. In this work, a Trust-based Efficient Task Execution Scheme (TETES) is proposed for fog node that scrutinizes the offloaded tasks sent by the malicious nodes and efficiently execute most of the trusted tasks within a stipulated time cycle. The simulated results show that TETES execute more offloaded tasks as compared to well-known First Come First Serve (FCFS), Longest Task First (LTF), and Shortest Task First (STF) algorithms.  
trust management; fog computing; task offloading; smart cities +
Footnote †: journal: Applied sciences
## 3 Perturbation Analyses
\begin{split}&\omega_{0}^{2}\mathrm{D}_{0}^{2}\mathrm{s}_{1}+\big{(} \omega_{0}^{2}-\omega_{0}^{2}\pi^{2}\tau^{2}\big{)}\mathrm{s}_{1}=\mathrm{B} ^{3}\mathrm{e}^{3\mathrm{i}\sqrt{1-\pi^{2}\tau^{2}}\mathrm{T}_{0}}+\\ &\Big{(}3\mathrm{B}^{2}\overline{\mathrm{B}}-2\mathrm{i}\omega_ {0}^{2}\sqrt{1-\pi^{2}\tau^{2}}\mathrm{D}_{1}\mathrm{B}-\mathrm{i}\kappa \sqrt{1-\pi^{2}\tau^{2}}\mathrm{B}+\tfrac{1}{2}\mathrm{a}_{\mathrm{m}} \mathrm{e}^{\mathrm{i}\mathrm{T}_{1}\upsilon}\Big{)}\mathrm{e}^{\mathrm{i} \sqrt{1-\pi^{2}\tau^{2}}\mathrm{T}_{0}}+\mathrm{cc}\end{split}  \tag{20}\]  
where a zero-order approximation \(\omega_{0}=\sqrt{1-\pi^{2}\tau^{2}}\) is adopted from Equation (19). Additionally, secular terms in Equation (20) must vanish in order to obtain a periodic solution for \(\mathrm{s}_{1}\). As a result, the following relation and solution can be derived by eliminating the terms which produce secular terms as  
\[3\mathrm{B}^{2}\overline{\mathrm{B}}-2\mathrm{i}\omega_{0}^{2}\sqrt{1-\pi^{2} \tau^{2}}\mathrm{D}_{1}\mathrm{B}-\mathrm{i}\kappa\sqrt{1-\pi^{2}\tau^{2}} \mathrm{B}+\frac{1}{2\mathrm{i}}\mathrm{a}_{\mathrm{m}}\mathrm{e}^{\mathrm{i} \mathrm{T}_{1}\upsilon}=0 \tag{21}\]  
\[\mathrm{s}_{1}=-\frac{1}{8(1-\pi^{2}\tau^{2})\omega_{0}^{2}}\mathrm{B}^{3} \mathrm{e}^{3\mathrm{i}\sqrt{1-\pi^{2}\tau^{2}}\mathrm{T}_{0}}+\mathrm{cc} \tag{22}\]  
Assuming \(\mathrm{B}=\tfrac{1}{2}\mathrm{b}\mathrm{e}^{\mathrm{i}\beta}\), where \(\mathrm{b}\) is the amplitude of excited vibration and \(\beta\) is a real function with respect to \(\mathrm{T}_{1}\) and substituting it into Equation (21) then separating the real-parts and imaginary-parts in the results yields  
\[ \begin{split}&\mathrm{b}\beta^{\prime}=-\frac{3}{8\omega_{0}^{2} \sqrt{1-\pi^{2}\tau^{2}}\mathrm{b}^{3}-\tfrac{\mathrm{a}_{\mathrm{m}}}{2\omega_ {0}^{2}\sqrt{1-\pi^{2}\tau^{2}}}\sin(\upsilon\mathrm{T}_{1}-\beta)}\\ &\mathrm{b}^{\prime}=-\frac{\mathrm{b}\kappa}{2\omega_{0}^{2} \sqrt{1-\pi^{2}\tau^{2}}\cos(\upsilon\mathrm{T}_{1}-\beta)}\end{split}  \tag{23}\]Introduce a variable \(\gamma=\upsilon T_{1}-\beta\) and so \(\gamma^{\prime}=\upsilon-\beta^{\prime}\), Equation (23) then becomes  
\[ \begin{split}& b\gamma^{\prime}=b\upsilon+\frac{3}{8\omega_{0}^{2} \sqrt{1-\pi^{2}\tau^{2}}}b^{3}+\frac{a_{m}}{2\omega_{0}^{2}\sqrt{1-\pi^{2}\tau^ {2}}}\sin\gamma\\ & b^{\prime}=-\frac{b\kappa}{2\omega_{0}^{2}}-\frac{a_{m}}{2 \omega_{0}^{2}\sqrt{1-\pi^{2}\tau^{2}}}\cos\gamma\end{split}  \tag{24}\]  
We seek a stable periodic solution in this study, so b and \(\gamma\) should approach stability, hence  
\[\left(b\upsilon+\frac{3}{8\omega_{0}^{2}\sqrt{1-\pi^{2}\tau^{2}}}b^{3}\right)^ {2}+\left(\frac{b\kappa}{2\omega_{0}^{2}}\right)^{2}=\left(\frac{a_{m}}{2 \omega_{0}^{2}\sqrt{1-\pi^{2}\tau^{2}}}\right)^{2} \tag{25}\]  
Equation (25) is the frequency response equation, and, in fact, it contains the implicit function relations among amplitude b, detuning parameter \(\upsilon\) and amplitude of disturbing force \(A_{m}\).
# Distributed Rateless Codes with Unequal Error Protection Property for Space Information Networks
## 5 Conclusions  
We have investigated the design and optimization of the DURC over a dynamic energy-limited satellite relay network with multiple sources in SIN. The decoding performance has been analyzed by the And-Or tree technique, and the optimizations of parameters are solved by the MOP. The DURC can adapt the degree distribution, symbol selection weight and network coding rule to various erasure probabilities in different links. Simulation results show that, in the DURC, the information of any source can be divided into different arbitrary importance levels. The DURC can give great protection for the MIB without a large sacrifice of LIB. Furthermore, we can set different UEP levels for different sources to satisfy the requirement of system, which improves the flexibility of screening information for ground stations and makes the system more practical.  
This work was supported in part by the National Natural Sciences Foundation of China (NSFC) under Grant 61525103, Grant 61371102 and Grant 61201144, the Natural Scientific Research Innovation Foundation in the Harbin Institute of Technology under Grant HIT. NSRIF. 2017051, and the Shenzhen Fundamental Research Project under Grant JCYJ20160328163327348 and Grant JCYJ20150930150304185.  
[PERSON] and [PERSON] put forward the main idea and designed the distributed unequal error protection rateless coding scheme. [PERSON] and [PERSON] did the analysis and prepared the manuscript. All authors have participated in writing the manuscript. All authors have read and approved the final manuscript.  
The authors declare no conflict of interest.