esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Seismic Signal Analysis Based on Machine Learning
## 3 Methodology
### Evaluation Indicators  
In order to evaluate the clustering accuracy of our collaborative learning models in detail, this paper adopted standard neural network evaluation indexes to display and calculated the recall rate, accuracy rate, and F1 score. TP is the positive sample result of the prediction pair, FP is the positive sample result of the prediction error, TN is the negative sample result of the prediction pair, and FN is the negative sample result of the prediction error.  
The recall rate refers to the proportion of positive samples predicted to be positive among actual positive samples:  
\[Recall=\frac{TP}{TP+FN} \tag{4}\]  
In addition to the recall rate, the precision rate of the two methods was also calculated (the proportion of positive samples predicted as positive samples, that is, the proportion of positive samples predicted correctly) as well as the F1 score (a measure of classification problems, which considers both accuracy and recall rate and is the harmonic mean of the two), and the calculation formulas are as follows:  
\[Precision=\frac{TP}{TP+FP} \tag{5}\]  
\[F1=\frac{2\times TP}{2\times TP+FP+FN} \tag{6}\]  
Based on the characteristics of small data in this paper, leave-one-out cross-validation was used to verify the accuracy and stability of the model. Since each test (sub-test) has only one event in the retention cross-validation test, the event-based recognition rate is given only after all sub-tests are completed, so only the final recognition rate can be calculated, not the average recognition rate, the highest recognition rate, and the lowest recognition rate.  
In view of the inconsistency of the number of different types of events in the data used in this article, the weighted calculation indicator in sklearn.metrics was used in the final output of the above three parameters. Considering the imbalance of data in each category, the weight of each category when calculating each indicator was no longer the reciprocal of the number of categories but the proportion of each category in the real label.
# Optimized Gateway Placement for Interference Cancellation in Transmit-Only LPWA Networks
## 2 System Model  
We consider a scenario with \(N\) SNs with the same transmit power and modulation scheme. The SNs are randomly located in a monitoring region with \(M\) deployed GWs that collect the data from the SNs.  
In this paper, we model the channel with only path loss. Let the power received by GW \(g\) from the SN \(m\) be expressed as,  
\[P_{r}(d_{m})=P_{o}*(\frac{d_{0}}{d_{m}})^{n}, \tag{1}\]where \(d_{m}\) is the distance between the SN \(m\) and the GW \(g\), \(n\) is the path loss exponent, and \(P_{o}\) is the received power at a reference distance \(d_{0}\) from the GW.  
Packet collisions are unavoidable in a high density TO LPWAN. When \(L\) SNs transmit packets that collide at one GW, the overall signal at the GW will hence be the superposition of the \(L\) overlapping radio signals transmitted by the SNs, plus noise power \(N_{0}\), with total power  
\[\Lambda=\sum_{m=1}^{L}P_{r}(d_{m})+N_{0}. \tag{2}\]
# Radiation-Balanced Lasers: History, Status, Potential
## 4 Development of Radiation-Balanced Lasers  
The thermodynamics of radiation-balanced lasers has been discussed both qualitatively and quantitatively from the point of view of the first and second laws by [PERSON] [28]. The Carnot efficiency has been derived for optical amplification from consideration of the radiative transport of energy and entropy. It has been shown that the highest Carnot efficiencies can be reached only when the system is pumped into saturation. Indeed, the energy of a collection of photons of fixed mean frequency and angular divergence scales linearly with the number of photons. The entropy of the set depends on the number and on the energy distribution of the photons. As the number of photons in the pump beam increases, the entropy that the beam carries per photon decreases since it becomes more sharply peaked. As the fluorescence intensity increases, the entropy decreases, but it does not decrease as rapidly as does the pump beam, because the spontaneously emitted light is less intense, broader in bandwidth, and distributed over a larger range of solid angles than is the pump radiation.  
As one can see from the theory developed in the previous part of the paper, a detailed balance of the stimulated and spontaneous emission at each point of the laser medium can provide a solid-state laser that generates no internal heat. Unfortunately, there are two serious problems associated with the practical development of radiation-balanced amplifiers and lasers: the precise control of the pump power and almost linear growth of the amplified signal (Figure 5). The linear growth of the power of the amplified signal requires an enormous increase in the length of the active medium for very high output power. The athermal bulk or fibre laser requires precision control of the pump power at each point along the length laser medium. This is not a simple problem, especially in the case of a fibre amplifier or laser. The sensitivity and stability of a radiation-balanced laser to perturbations in the field parameters and temperature have been analyzed in [29]. As one can see in [29], fluctuations in the gain set limits on the variability of the pump wavelength. A pump stability of \(\pm 1\) nm was suggested for an Yb:KGW laser. An active wavelength stabilization scheme based on techniques for spatial mode matching was presented in [29]. A dynamic control to the laser system based on the pump wavelength shift was proposed. It permits to minimize the sensitivity of the athermal laser to ambient temperature fluctuations. The idea to use tapered reflectance mirrors to flatten the transverse laser profile and to improve optical efficiency was considered in [29].
## 5 Testing and Experimental Configuration
### Experimental Configuration  
Radio planning, which evaluates the performance of the LoRa network operating at 915 MHz and 904 MHz, is presented.  
Based on the number of LoRa parameters that can be configured, a configuration notation is defined for the experimental tests: frequency (Fr), bandwidth (BW), spreading factor (SF) and coding rate (CR).  
Nodes 1 and 2 transmit information of environmental conditions (temperature, humidity and UV radiation) to the gateway at 2000 ms intervals. The data received by the gateway were stored on a PC. Node 3 receives the payload packets from the gateway and these were sent via Bluetooth to a mobile application for storage. The study focuses on nodes sending/receiving packets with a payload length of 16 bytes, 8 bytes and 4 bytes.  
The link checks at each location were performed by configuring the end node and the gateway with identical transmit/receive parameters going through various combinations for three values of spreading factor SF (7, 9 and 12), keeping a constant value in the coding rate CR (4/5) and 125 kHz in the bandwidth parameter (BW). The isotropic transmit power is set at 14 dBm, the maximum allowed [43], using antennas with 2 dBi gain. The main parameters are shown in Table 3.  
The LoRa wireless communication parameters in Table 2, were customized in an optimized algorithm in C language in the Arduino IDE. The optimized algorithm addresses different parameters [44; 45] pre-installed libraries such as LoRa, OLED, etc.; Fr, BW, SF, CR; variable acquisitions, reception/transmission and data flow occurring within a period of 2000 ms. The MCUs used in the research had their own optimized algorithm.  
During the experiments, each end node was configured to transmit 100 packets to the LoRa gateway; as well as downstream communication node 3 received 100 payload packets.  
The flowchart of the optimized gateway algorithm is illustrated in Figure 9a.  
A summary of the simplified logic of the end-node operation is presented in Figure 9b.
## 3 Proposed Method
### Fsnd-Enn Principle
#### 3.1.1 Initialization Process  
To encode the optimization problem, the optimizable object is transformed to a binary genetic code, and a unique binary code exists for any solution to the problem. The genetic information encoding of a subject includes feature selection, the number of fully connected network layers, and the number of nodes in each layer (Figure 3). The genetic information encoding of an evolutionary neural network consists of a string of binary codes of length \(d+M_{1}+M_{2}+M_{3}\). \(M_{1}\) and \(M_{2}\) are parameters to be set, which are related to the maximum number of layers, \(2^{M_{1}+1}-1\), and the maximum number of nodes, \(2^{M_{2}+1}-1\) per layer  
Figure 2: FSND-ENN principle diagram. (**a**) Flowchart of genetic algorithm. (**b**) Relationship between populations, individuals, and genotypes. (**c**) Relationship between parent generation, parent, child generation, and child.  
of the fully connected neural network. These parameters depend on the complexity of the training data of the fully connected neural network; the higher the complexity, the higher the maximum number of layers and maximum number of nodes per layer of the evolutionary neural network. \(M_{3}\) is the number of layers of the fully connected network.  
The first through \(d\)-th codes indicate the feature selection result, where \(d\) is the number of features to be selected. Each value in the feature selection code corresponds to a feature, where a value of 1 indicates that a feature is selected, and 0 indicates rejection. The \((d+1)\)-th through \((M_{1}+d)\)-th encoding denote the number of network layers \(\sum_{i=1}^{M_{1}}2^{i}\times x_{i}\) of the fully connected neural network, where \(x_{i}\) is the \(i\)-th value in this binary encoding.  
Codes \(d+M_{1}+(n-1)\times M_{2}\) to \(d+M_{1}+n\times M_{2}\) represent the number of nodes \(\sum_{i=1}^{M_{1}}2^{i}\times x_{i}\) in the \(n\)-th fully connected layer, where \(x_{i}\) is the \(i\)-th value in this binary code. For example, if the parameters are set as \(d=5\), \(M_{1}=4\), \(M_{2}=4\) and the total length of the encoding is 69, the genetic information is converted as shown in Figure 4. The first to fifth encodings are for feature selection, the sixth to the ninth are the network layer encodings, and the \((10+4(n-1))\)-th to \((10+4n)\)-th are the node encodings of the \(n\)-th layer network. Feature selection code 01001 indicates that the second and fifth features are selected, and the rest are discarded. The network layer code 0011 can be translated to a three-layer network. A first layer network node encoding of 1011 indicates that the first layer network has 11 nodes.
# An Overview of Machine Learning and 5G for People with Disabilities
## 2 Machine Learning for People with Disabilities
### Visually Impaired
#### 2.1.1 Navigation Systems
The arrival of a bus is detected using Histogram of Oriented Gradients (HOG)-based feature extraction and a Cascaded SVM classifier; the information of the bus route number is extracted from the top part of the bus facade using a scene text extraction algorithm. Bus detection, as well as route number recognition, is transmitted to the visually impaired via audio.
# Vendor-Agnostic Reconfiguration of Kubernetes Clusters in Cloud Federations
## 8 Discussion of Limitations
### Limitations of the Study  
The current prototype of the autonomic controller has not been evaluated from scalability or reliability perspective. Scalability requirements entails reconfiguring large number of features in a large cluster federations without exponential growth of reconfiguration time. From a reliability perspective, it is not clear what is the probability that the vendor-agnostic reconfigurations lead to broken control plane components or Kubelets that cannot be repaired anymore by rollback or forward rolling. It does not yet implement the configuration of multiple member clusters in parallel.
## 3 Experimental Results and Discussion
### Test of Drone Tracing and Longitude and Latitude Coordinates  
Figure 17 shows the tracing process through a full-color image. A target drone was successfully hit with laser rangers in this study. Conversion of the sensed values of the nine-axis attitude meter and the known longitude and latitude coordinates (\(W_{\rm A},J_{\rm A}\)) as (120.328766, 22.648851) of the GPS of the dual-axis device were achieved through the longitude and latitude sphere coordinate algorithm. It can be calculated that when the drone is locked by the tracing system, its current longitude and latitude coordinates (\(W_{\rm B},J_{\rm B}\)) (based on Equations (12) and (13)) are (120.328766, 22.648394), and the flying altitude is 58.7 m.  
Figure 18 shows the tracing process through a thermal image at the same place. A target drone was successfully hit with the laser rangers of the dual-axis device. Conversion of the sensed values of the nine-axis attitude meter and the known longitude and latitude coordinates (\(W_{\rm A},J_{\rm A}\)), where a longitude and latitude coordinate (120.328766, 22.648851) of the GPS of the device were achieved through the longitude and latitude sphere coordinate algorithm. It can be calculated that when the drone is locked by the tracing system, its current longitude and latitude coordinates (\(W_{\rm B},J_{\rm B}\)) (based on Equations (12) and (13)) are (120.328697, 22.648524) and the flying altitude is 51.9 m.  
Figure 16: Results of the tracing test on rainy days (drone flight altitude is 80 m).  
Figure 15: Results of the tracing test on cloudy days (drone flight altitude is 130 m).
# Precision Agriculture: A Remote Sensing Monitoring System Architecture +
## 4 The Network Layer
### The Link Layer
#### 4.1.2 Precision Agriculture Routing Protocols  
Data routing algorithms play an important role in WSNs by establishing the path of communication for data exchange between sensor nodes and base stations on a network. A variety of routing techniques have been proposed until now, aiming to achieve higher performance with minimal power consumption. IoT and WSN routing protocols can be categorized according to network structure and the way information will be disseminated through the network. A routing protocol can belong to more than one category, aiming to satisfy as many performance metrics as possible.  
According to the way by which routing decisions are made proactive and reactive routing techniques can be used. Proactive routing (or table-driven) supports the periodic renewal and updating of the routes and destinations that are formed between the nodes throughout the network. On the other hand, reactive routing (or on demand) includes discovering routes on demand based on the transmission of route request packets. The downside to reactive protocols is their latency, since transmissions over unknown or expired routes face delays, for which either the application or the routing protocol must account by buffering or dropping data.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Communication Technology** & **Data Rate** & **Frequency Band** & **Range** & **References** \\ \hline IEEE 802.15.4 & 20–250 Kbps & 2400/915/868 MHz & 10 m & [7] \\ IEEE 802.15.4-ZigBee & 20–250 Kbps & 2400/915/868 MHz & 10–100 m & [19] \\ Wi-Fi-IEEE 802.11 & 450 Mbps & 24 GHz–25 GHz & 100 m & [2,20] \\ GPRS-2G GSM & 64 Kbps & 900 MHz–1800 MHz & 100 m & [21] \\
3G & 14.4 Kbps–2 Mbps & 1.6–2 GHz & 100 m & [21] \\
4G-LTE & 100 Mbps–1 Gps & 2–8 GHz & 100 m & [14] \\ LoRa & 0.3–50 Kbps & 433,868,780,915 MHz & 2–5 km & [1,22] \\ Bluetooth LE & 1 Mbps & 2.4 GHz–2.485 GHz & \textgreater{}100 m & [5] \\ RFID & 400 Kbps & 125 KHz–915 MHz & 3 m & [23] \\ \hline \hline \end{tabular}
\end{table}
Table 3: Smart farming networking technologies.  
Furthermore, according to WSN structure, routing algorithms can depend on neighbor nodes to broadcast the collected information. Another popular technique is based on dividing the network into clusters. Each cluster depends on a cluster head node to manage the routing of information between other clusters or base stations. Hierarchical routing is the most popular routing method in smart farming monitoring systems and soil parameter monitoring [23; 24]. Multi-path routing protocols can also be used to implement a smart farm monitoring system to balance the data transfer load and conserved energy. Table 4 presents characteristic examples of routing protocols adopted in smart farming systems according to literature.
## 3 Results and Discussion
### Optimizing the Physical Performance of TPCB/GF-70  
The results above show that TPCB can decrease the low-temperature performance of GF-70; therefore, AHO at four different dosages (0.3%, 0.6%, 0.9%, and 1.2%) was tested to investigate whether it can improve the low-temperature performance of TPCB/GF-70. The penetration, ductility, softening point, PI, T800, and T1,2 results are shown in Table 6.  
From Table 6, with the addition of AHO, low-temperature indictors such as ductility and T1,2 show a good tendency. In detail, when the dosages of AHO are 0.3%, 0.6%, 0.9%, and 1.2%, the ductility of TPCB/GF-70 increases by 3.2%, 15.5%, 27.3%, and 41.2%, respectively, while the T1,2 decreases by 0.6%, 1.3%, 2.1%, and 2.9%, respectively. Thus, AHO can significantly improve the low-temperature anti-cracking performance of TPCB-modified asphalt, and the improvement effect is more pronounced with the increase in AHO dosage. AHO can also affect the high-temperature performance of TPCB-modified asphalt: the softening point and T800 decrease with the addition of AHO, and the penetration increases. However, the softening point and T800 of TPCB/GF-70 only decrease by 1.5 \({}^{\circ}\)C and 1.3 \({}^{\circ}\)C with a 1.2% dosage of AHO, respectively, and the high-temperature performance of TPCB/GF-70 does not obviously decrease; the softening point and T800 of TPCB/GF-70 are still 1.4 \({}^{\circ}\)C and 1.5 \({}^{\circ}\)C higher than that of GF-70, respectively. The PI of TPCB/GF-70 increases with the addition of AHO, which shows that the temperature sensitivity of TPCB/GF-70 decreases. In other words, AHO can obviously improve the low-temperature performance of TPCB/GF-70, but does not significantly decrease the high-temperature performance of the asphalt binder.
## 5 Multiobjective Optimization Approach
### Multiobjective Optimization Modeling
#### 5.1.1 Mission Path Model
Figure 7: Space–time state network diagram considering FPTLIR’s flight velocity curve. consumption model, the position trajectories of different flight state intervals in VPON, HPON, HPOF, and VPOF can be represented by Equations (22)-(25), respectively. \[\mathbf{s_{\text{wn1}}}(t)=[0,0,\frac{1}{2}a_{\text{wn1}}t_{\text{wn1}}^{2}],\mathbf{s_{ \text{wn2}}}(t)=[0,0,v_{\text{wn1}}t_{\text{wn2}}],\mathbf{s_{\text{wn3}}}(t)=[0,0,v_ {\text{wn1}}t_{\text{wn3}}-\frac{1}{2}a_{\text{wn2}}t_{\text{wn3}}^{2}] \tag{22}\]  
\[\mathbf{s_{\text{hn1}}}(t)=[\frac{1}{2}a_{\text{hn1}}t_{\text{hn1}}^{2},0,0],\mathbf{s_{ \text{hn2}}}(t)=[v_{\text{hn}}t_{\text{hn2}},0,0],\mathbf{s_{\text{hn3}}}(t)=[v_{ \text{hn}}t_{\text{hn3}}-\frac{1}{2}a_{\text{hn2}}t_{\text{hn3}}^{2},0,0] \tag{23}\]  
\[\mathbf{s_{\text{hf1}}}(t)=[\frac{1}{2}a_{\text{hf1}}t_{\text{hf1}}^{2},0,0],\mathbf{s_ {\text{hf2}}}(t)=[v_{\text{hf}}t_{\text{hf2}},0,0],\mathbf{s_{\text{hf3}}}(t)=[v_{ \text{hf}}t_{\text{hf3}}-\frac{1}{2}a_{\text{hf2}}t_{\text{hf3}}^{2},0,0] \tag{24}\]  
\[\mathbf{s_{\text{wf1}}}(t)=[0,0,\frac{1}{2}a_{\text{wf1}}t_{\text{wf1}}^{2}],\mathbf{s _{\text{wf2}}}(t)=[0,0,v_{\text{wf}}t_{\text{wf2}}],\mathbf{s_{\text{wf3}}}(t)=[0, 0,v_{\text{wf}}t_{\text{wf3}}-\frac{1}{2}a_{\text{vf2}}t_{\text{wf3}}^{2}] \tag{25}\]  
where \(\mathbf{s_{\text{sym}}}\), \(\mathbf{t_{\text{sym}}}\), \(\mathbf{a_{\text{syk}}}\), and \(\mathbf{v_{xy}}\) are the parameters in the four path intervals VPON, HPON, HPOF, and VPOF. Here, the term \(\mathbf{s}\) is the path of the interval position, \(t\) is the duration of the flight state interval, \(a\) is the acceleration of the uniformly variable velocity, and \(v\) indicates uniform velocity. The subscript \(x=\text{v},\text{h}\) denotes vertical and horizontal paths, respectively; \(y=\text{n},t\) denotes on-line and off-line paths, respectively; \(m=1,2,3\) denotes acceleration, uniform velocity, and deceleration intervals, respectively; while \(k=1,2\) denotes acceleration and deceleration intervals, respectively.
# Integrating Elliptic Curve Cryptography with the Modbus TCP SCADA Communication Protocol
## 4 Architecture and Implementation
### Android Implementation  
To demonstrate the use and functionalities of the proposed library on a mobile device, which can be part of a SCADA network as an IIoT device, the modified Elliptic Curve based Modbus library was fully compiled for the Android operational system. Our extensions on the Modbus library require the ECCLIB and GMP libraries. The ECCLIB was compiled from C source code, but we used the precompiled GMP version for the Android Operating System [34]. Using the compiled libraries, a simple Modbus server was developed running on the resource limited Android device. The final generated library was named libsmodbus.so.  
For the main server application, which uses libsmodbus.so, \"gen-libs\" was used as a foundation. A simple C file provided the functionality of a Modbus server. The file contained a JNI function, that could be called from the Main Activity of the application. Finally, the Main Activity consists of a main view, generated when the application is created and the JNI function, which is called when the application starts running.  
In our implementation, we encrypt the whole Modbus ADU. The size of the ADU is typically 260 bytes. When a request/response is, at first, created, it is stored in a uint8 array, however, to process it, it has to be converted to a multiple precision integer. The resulting number must be encrypted using the ECCLIB. To make this possible, we increased the size of the field on which the elliptic curve is defined. Consequently, larger messages can be processed by the library.