esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluation of Modern Internet Transport Protocols over GEO Satellite Links
## 4 Evaluation Testbed  
For a realistic evaluation of modern Internet protocols, we built a testbed running on dedicated computers and hosting the full stack software implementation of HTTP/2, HTTP/3 with Cubic and BBR congestion control algorithms. The testbed was realized using the OpenSAND Emulation platform which is an open-source project developed by Thales Alenia Space to provide demonstration, validation and performance evaluation of satellite network applications [27]. The emulator implements most of the new satellite transmission standards such as DVB-RCS2 and DVB-S2 including the protocol stacks as well as the DVB physical layer blocks for each uplink and downlink. It also supports the Adaptive Modulation and Coding (AMC) loop which is used by DVB-RCS2 transmitters to match the transmission rate with the channel quality. In addition, the emulator provides multiple attenuation profiles that depends on the orbit characteristics of the satellite [27].  
The testbed consists of five computers deployed as the network depicted in Figure 5. Three machines host the three primary components of OpenSAND: terminal, satellite and gateway. We added a machine at the terminal side to run the client software and control the experiments, and another machine at the gateway side to host the web server. The specifications of all software and hardware are summarized in Table 1.  
There are two direct point-to-point links and a switched subnet in this setup. The first direct link is from client to terminal and the other is from server to gateway. The switched subnet consists of a bridge that connects the terminal, the gateway and the satellite via IP tunnels when the emulation is enabled. All traffic arriving at the terminal will be received and processed by the satellite emulator node. Then, the traffic will be transferred and processed by the corresponding blocks of the gateway. Therefore, we can produce a realistic satellite channel by adjusting the emulator link characteristics as needed in each experiment. The satellite link parameters used in the testbed are listed in Table 2.  
For the server machine, we deployed the Open LiteSpeed v1.7 which is a free web server provided by LiteSpeed technologies [28]. We chose this web server because it implements different HTTP protocols (HTTP/1.1, HTTP/2 and HTTP/3) and supports TCP and QUIC with various congestion control algorithms such as Cubic and BBR. On the client machine, we used Firefox Nightly v90.0 which was, at the time of evaluation, the most recent development version of Firefox because it supports HTTP/3 as well as HTTP/2 [29]. Also, it has been chosen among other browsers such as Opera, Chromium, etc. since it allows controlling the number of parallel TCP connections to the server. This is necessary in our testbed to ensure fair comparisons to QUIC in the experiments.
## 3 Classifier Training and Results
### Results for Datasets Taken Directly from IMU Signal Samples  
Subsequently, we evaluated the classifier performances for the testing datasets that were drawn directly from the samples of IMU signals. The achieved classifiers' accuracies for the test datasets are shown in Figure 6.  
Thus, we can note that for the classifiers trained on the IMU signal samples, the best accuracies exceeding 95% were achieved for the SVM classifiers. Also, an important observation is that their performance did not depend on the test data type (T_53_set or T_12_set).
# A Mechanism for Reliable Mobility Management for Internet of Things Using CoAP
###### Abstract  
Under unreliable constrained wireless networks for Internet of Things (IoT) environments, the loss of the signaling message may frequently occur. Mobile Internet Protocol version 6 (MIPv6) and its variants do not consider this situation. Consequently, as a constrained device moves around different wireless networks, its Internet Protocol (IP) connectivity may be frequently disrupted and power can be drained rapidly. This can result in the loss of important sensing data or a large delay for time-critical IoT services such as healthcare monitoring and disaster management. This paper presents a reliable mobility management mechanism in Internet of Things environments with lossy low-power constrained device and network characteristics. The idea is to use the Internet Engineering Task Force (IETF) Constrained Application Protocol (CoAP) retransmission mechanism to achieve both reliability and simplicity for reliable IoT mobility management. Detailed architecture, algorithms, and message extensions for reliable mobility management are presented. Finally, performance is evaluated using both mathematical analysis and simulation.  
reliable mobility management; IoT networks; CoAP 1
# Color-Based Image Retrieval Using Proximity Space Theory
## 2 Proposed Method
### Feature Extraction  
RGB color space and HSV color space are often used in digital image processing, and RGB color space is the basic color space that can be represented by a cube model. Red, green and blue are called the three primary colors and any point corresponds to a color in the RGB color model. HSV color space is established based on human visual perception characteristics and it can be represented by cone model. \"Hue\" means different color, \"saturation\" means the intensity of the color, whose value is in the unit interval [0,1], and \"value\" means the degree of shade of color. Circumferential color of the top of cone is pure color, and the top of the cone corresponds to \(V=1\), which contains three surfaces (R = 1, G = 1, and B = 1) in RGB color space. The conversion formula from RGB color space to HSV color space is given by:  
\[v=\max(r,g,b),\;\;s=\frac{v-\min(r,g,b)}{v}, \tag{2}\]  
\[r^{\prime}=\frac{v-r+\sigma}{v-\min(r,g,b)+\sigma},\;g^{\prime}=\frac{v-g+ \sigma}{v-\min(r,g,b)+\sigma},\;b^{\prime}=\frac{v-b+\sigma}{v-\min(r,g,b)+ \sigma}, \tag{3}\]  
where \(\sigma\to 0\), \(v=v/255\) and \(r\), \(g\), and \(b\) are the red, green and blue coordinates of a color, respectively, in RGB color space.  
\[h^{\prime}=\left\{\begin{array}{llll}(5+b^{\prime}),&r=\max(r,g,b)&and&g= \min(r,g,b)\\ (1-g^{\prime}),&r=\max(r,g,b)&and&g\
eq\min(r,g,b)\\ (1+r^{\prime}),&g=\max(r,g,b)&and&b=\min(r,g,b)\\ (3-b^{\prime}),&g=\max(r,g,b)&and&b\
eq\min(r,g,b)\\ (3+g^{\prime}),&b=\max(r,g,b)&and&r=\min(r,g,b)\\ (5-r^{\prime}),&b=\max(r,g,b)&and&r\
eq\min(r,g,b),\end{array}\right. \tag{4}\]  
\[h=60\times h^{\prime},\]  
where \(h\in[0,\cdots,360]\) and \(s,v\in[0,\cdots,1]\) are the hue, saturation and value coordinates of a color, respectively, in HSV color space. Then, according to the property of the HSV color space, we can obtain 72 kinds of colors by the non-uniform quantization method, which can reduce the dimension of the color feature vectors and the amount of calculation. The quantization formula is described as follows:  
\[H=\left\{\begin{array}{ll}0&316\leq h\leq 360\;\;or\;\;0\leq h\leq 20\\ 1&21\leq h\leq 40\\ 2&41\leq h\leq 75\\ 3&76\leq h\leq 155\\ 4&156\leq h\leq 190\\ 5&191\leq h\leq 270\\ 6&271\leq h\leq 295\\ 7&296\leq h\leq 315,\end{array}\right. \tag{5}\]\[S=\left\{\begin{array}{ll}0,&0\leq s\leq 0.2\\ 1,&0.2<s\leq 0.7\\ 2,&0.7<s\leq 1,\end{array}\right.\quad V=\left\{\begin{array}{ll}0,&0\leq v\leq 0.2 \\ 1,&0.2<v\leq 0.7\\ 2,&0.7<v\leq 1,\end{array}\right. \tag{6}\]  
Based on the above quantization method, the three-dimensional feature vector is mapped into a one-dimensional feature vector by using the equation: \(l=hQ_{s}Q_{v}+sQ_{v}+v\), where \(Q_{s}\) and \(Q_{v}\) are set to 3 because saturation and value are divided into three levels, respectively. Thus, the above equation can be rewritten as  
\[l=9h+3s+v. \tag{7}\]  
According to the value range of \(h\), \(s\), and \(v\), we can obtain the range of \(l=[0,1,\cdots,71]\), and then we can get 72 kinds of color features.
## 3 Design Concept and Characteristics
### Simulation of the System
#### 4.2.1 Results in Domain of the Frequency
When the excitation with a specific frequency induced the controlled structure, the required optimal inertance was computed as portrayed in Figure 8. It indicates an inverse relationship between the optimum inertance and the relevant frequency. In higher excitation frequencies, the frequency of the ATMID increases when CVT ratio is decreased (low inertance). Although, the ATMID's frequency is reduced in lower frequencies (high inertance). Figure 6: The FRC of the adaptive controlled and uncontrolled SDOF structures. ATMID, the damping properties are enhanced, that is, higher structural DAF reduction is obtained, as displayed in Figure 9c. It should be pointed out that by introducing a control system, the detection of the force frequency can be eased. Subsequently, the controller sends a signal to the motor, which causes the planet's axles to slant together, and the angle of the planetary arrangement is adjusted. Later on, the required CVT ratio can be calibrated and the optimal inertance is provided. Afterward, the frequency of the ATMID is tuned to that of the current frequency of excitation, leading to notable damping properties along a vast frequency range.
## 3 Basic Concepts
### Location Attacks
#### 3.4.1 Location Sequence Attack  
In the location sequence attack, introduced in [9; 23], the an adversary is aware of a subset of the locations that the individual has visited and the temporal ordering of the visits.  
In the context of trajectory data privacy, a location sequence attack involves an adversary possessing knowledge of a subset of the locations visited by an individual, as well as the temporal ordering of these visits. For an individual \(s\), the sequence of visited locations \(L(T_{s})\) is represented by the sequence of locations \(l_{i}\) within \(T_{s}\).  
The background knowledge category for a location sequence attack is formally defined as follows.  
**Definition 9** (Location Sequence Background Knowledge).: _Let \(k\) be the number of locations \(l_{i}\) known by the adversary for an individual \(s\). The location sequence background knowledge comprises configurations based on \(k\) locations, denoted as \(B_{k}=L(T_{s})[k]\). Here, \(L(T_{s})[k]\) represents the set of all possible \(k\)-subsequences of the elements in set \(L(T_{s})\)._  
In this context, the notation \(a\preccurlyeq b\) indicates that \(a\) is a subsequence of \(b\). Each instance \(b\in B_{k}\) is thus a subsequence of location \(X_{s}\preccurlyeq L(T_{s})\) of length \(k\). Given a record \(d\) in the dataset \(D\) and the corresponding individual \(u\), the matching function is defined to determine the presence of a location sequence:  
\[evaluate(T,b)=\begin{cases}1,&b\preccurlyeq_{l}L(T_{u})^{k}\\ 0,&otherwise\end{cases} \tag{1}\]  
In this attack, the attacker knows that a person went, first, to a supermarket and then to work, but they do not know when, only the sequence of places.
## 5 Challenges and Future Research Directions
### Mobility Management  
In order to achieve a sufficient link budget when operating at mm waves to sustain high-capacity connections, directionality is essential. Fine beam alignment has serious consequences for the design of control procedures such as user tracking, handover and radio link failure recovery in this situation. These issues are especially pressing in the non-terrestrial sector, where the fast speed of UAV aerial/space platforms may cause beam alignment to be lost before a data transfer is completed. The higher Doppler experienced at high speeds may also cause the channel to become non-reciprocal, reducing the feedback over a broadcast channel in 6G enabled UAV networks [126].
# Application of a MEMS-Based TRNG in a Chaotic Stream Cipher
## 3 Seed Generator  
### Noise Signal Analysis  
In order to generate values for the initial parameters of the STM, \(\gamma\) and \(x_{0}\), as well as the initial state of the LFSR, \(y_{0}\), the feasibility of a TRNG based on the capture of noise produced by an accelerometer at rest was studied. Simulations have been done to test the proposed method. To generate random numbers, the noise signal produced by means of the evaluation board EVAL-ADXL335Z has been used.  
Figure 4: Block diagram of the proposed 64-bit Skew Tent Map generator. Values \(1/\gamma\) and \(1/(1-\gamma)\) are precalculated when initializing the chaotic cipher and they are constants for the whole communication session until a new key is requested.  
Figure 3: Block diagram of the proposed cipher system.  
This board contains a small low-energy three-axis accelerometer ADXL335 [9]. This accelerometer is composed by a polysilicon surface-micromachined capacitive sensor and a conditioning electronic stage that implements an open-loop measurement architecture. Acceleration deflects the moving mass and unbalances the differential capacitor resulting in a sensor output whose amplitude is proportional to acceleration. The phase-sensitive demodulation technique was then used to determine the magnitude and direction of the acceleration. Some of its typical applications, such as mobile systems or sports and health devices, are strongly related with IoT. The evaluation board was supplied with batteries to avoid coupling 50 Hz or 60 Hz harmonics from the power supply network. AC signal of the measurements of the acceleration in the X axis have been acquired by an oscilloscope at several sampling rates, 250, 25, 2.5, 0.25, and 0.1 kSps using a dynamic range of \(\pm\)8 mV and 8-bit resolution. An example of noise signal, obtained using a sample rate of 25 kSps is shown in Figure 5. To minimize the correlated common noise, signals generated by the X and Y sensors have been subtracted.  
The Fast Fourier Transform (FFT) of this signal has been calculated to analyze the characteristics of the noise (Figure 6). As can be seen, the obtained power spectral density decreases with the frequency for low frequencies but it stays approximately constant (white noise) for high frequencies.  
Finally, it was tested if the noise generated by the accelerometer follows a Gaussian distribution, which would be suitable for our application. Figure 7a shows a histogram of the data where it can be seen that the distribution is approximately Gaussian. A normal distribution fit gives a mean of \(\mu=-0.42\) mV and a standard deviation of \(\sigma=0.43\) mV. On the other hand, Figure 7b compares the sensor data cumulative distribution function (CDF) with the standard normal CDF. As can be seen, both cumulative distribution functions are very similar, which indicates that the sensor data is very close to Gaussian noise.  
Figure 5: Sample random data generated by the accelerometer at rest, measured using a sample rate of 25 kSps.  
Figure 6: Fast Fourier Transform of the sampled data.
## 5 Simulation and Results
### Simulations  
_Training Phase_: Training of the data is an important part of the ANN, where input data and concerned output data are given to the ANN, and it then trains the brain of the ANN by forward and backward propagation. ANN finds the optimal weights between the layers by adjusting the weights between the layers to the minimum level. The training performance can be measured in two parameters, one is Mean Square Error (mse), and the second is the number of Epochs.  
During the training phase, the training performance is shown by the blue solid line, the validation performance is shown by the green dash line and the testing performance is shown by the red dotted line. Figure 5 represents the convergence graphs of the Levenberg-Marquardt (LM) training algorithm of ANN, which is attaining a convergence level at approx. 550 number of Epochs with \(6.3\times 10^{-03}\) (mse).  
Figure 6 represents the performance of the Bayesian regularization (BR) training algorithm of ANN and it is converging at 900 Epochs approx and achieved the \(5.5\times 10^{-03}\) (mse). The validation and the testing during this algorithm training is overlapping, however, the training is higher as compared to the validation and testing, resulting in no over-fitting of the model.  
Figure 7 shows the convergence performance of the Scaled Conjugate Gradient (SCG) training algorithm of ANN, which is attaining \(8.8\times 10^{-03}\) (mse) at 300 Epoch approx. The validation and testing in the SCG training algorithm are also overlapping at some points, however, during the training phase, no over-fitting is happening, as the training performance is higher than the others.  
Figure 5: Training performance of LM algorithm.  
Validation Phase: Validation is an important phase of any machine learning algorithm, which gives a clear sight of the performance of the proposed model. Since the proposed model is based on the local clients and the central federated server, it's necessary to evaluate the validation performance of each entity. For the aforementioned purpose, 45,000 instances were used of the dataset, which was initially divided into Validation data, in which 12,616 records show the normal traffic records, and 32,381 are the attack traffic records. The validation results can be elaborated using the confusion matrix, which consists of true positive, false negative, false positive, and true negative values in a square matrix form.
## 4 Experiments and Analyses
### Model Training  
To demonstrate the training situation and computational cost of our model, we reimplement BERT-BiLSTM-CRF because it is the mainstream base model in the NER task, and we take it as the baseline. Figure 4 shows the variation tendency of evaluation metrics and loss value on the development set with the training epoch. As can be seen from Figure 4, on the Weibo dataset, the evaluation metrics and loss value exhibit significant fluctuations during the early stages of training. This is because the Weibo dataset has a relatively small amount of data, approximately one twelfth of the OntoNotes 4 dataset, so the model requires more training time to learn and adjust before it can stably demonstrate good performance. Meanwhile, compared with BERT-BiLSTM-CRF, the loss curve of CGR-NER is lower and smoother, indicating the better fit of the CGR-NER model. Moreover, to demonstrate the computational cost of CGR-NER, we also report the total number of parameters and the average time per epoch during training for both BERT-BiLSTM-CRF and CGR-NER in Table 4.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Hyperparameter** & **Value** \\ \hline CBHNN\_kernel\_size & 3, 5, 7 \\ CBHNN\_hidden\_size & 128, 256, 512 \\ CMTE\_layer & 1, 2, 3 \\ CMTE\_head & 4, 8, 16 \\ Dropout\_rate & 0.2, 0.3, 0.4, 0.5 \\ Learning\_rate & 0.00001, 0.0001, 0.0004, 0.001 \\ Semantic\_Encoding\_Layer\_hidden\_size & 128, 256, 512 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The selection range of hyperparameters.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Hyperparameter**} & \multicolumn{4}{c}{**Weibo**} & \multicolumn{4}{c}{**OntoNotes 4**} \\ \cline{2-7}  & **Precision** & **Recall** & **F1 Score** & **Precision** & **Recall** & **F1 Score** \\ \hline \multirow{3}{*}{Kernel Size} & 3 & 73.92 & 75.45 & **74.68** & **82.52** & 79.88 & **81.18** \\  & 5 & **74.48** & 74.68 & 74.58 & 80.8 & **80.13** & 80.47 \\  & 7 & 72.59 & **75.97** & 74.24 & 81.04 & 78.91 & 79.96 \\ \hline \multirow{3}{*}{Hidden Size} & 128 & 72.24 & **75.97** & 74.06 & 78.31 & **80.4** & 79.34 \\  & 256 & 73.59 & 74.16 & 73.87 & 80.33 & 80.37 & 80.35 \\ \cline{1-1}  & 512 & **73.62** & 75.71 & **74.65** & **80.9** & 79.84 & **80.37** \\ \hline \hline \end{tabular}
\end{table}
Table 3: The evaluation results of different hyperparameters on the development set. Among them, kernel size represents the size of the context window in CBHNN; hidden size represents the dimensions of the hidden state in the semantic encoding layer.
# Analysis of Four Years of Global XCO\({}_{2}\) Anomalies as
## 2 Datasets
### Tropomii/s5p No\({}_{2}\) Tropospheric Columns  
In order to study local scale emission sources in South Africa, we use the TROPOMI/SSP NO\({}_{2}\) tropospheric columns [29; 30] collocated with the XCO\({}_{2}\) anomalies, as indicators of anthropogenic fossil fuel emissions. Launched in October 2017, TROPOMI on board S5P flies on a sun-synchronous polar orbit satellite with overpass time 13:30 LT. TROPOMI NO\({}_{2}\) data are publicly available since July 2018 with a spatial resolution of 3.5 km \(\times\) 7 km at nadir and daily global coverage.
## 2 CC&OV-PT Controlled Two-Stage WPT System
### The Proposed Double-Side Feedback PT Control Strategy  
#### 2.2.1 WPT System Description  
The overall topology of the proposed voltage-regulating WPT system is shown in Figure 3. The power circuit is composed of a Buck converter and a magnetic coupling resonant WPT circuit. The output voltage can be tuned by regulating the front-stage Buck converter connected to the H-bridge inverter. When the PT controller is applied to the Buck converter (\(S_{\mathrm{e2}}\) is off), the controller generates a high and low power pulse \(P\) with duty cycle \(D_{\mathrm{v}}\) to drive the switch \(S_{1}\) of the Buck converter. We have:  
\[D_{\mathrm{v}}=\left\{\begin{array}{l}D_{\mathrm{H}}\ P=P_{\mathrm{H}}\\ D_{\mathrm{L}}\ P=P_{\mathrm{L}}\end{array}\right. \tag{6}\]  
The output voltage \(V_{\mathrm{o}}\) is sampled and sent to the primary side through the wireless communication module, which is then compared with reference voltage \(V_{\mathrm{ref}}\). When \(V_{\mathrm{o}}\) is higher than \(V_{\mathrm{ref}}\), a low power pulse is generated to drive the Buck converter to reduce the  
Figure 3: Schematic of the proposed WPT system.  
output voltage; when \(V_{\rm o}\) is lower than \(V_{\rm ref}\), a high power pulse is produced to increase the output voltage. We have:  
\[P=\left\{\begin{array}{l}P_{\rm H}\ V_{\rm o}<V_{\rm ref}\\ P_{\rm L}\ V_{\rm o}>V_{\rm ref}\end{array}\right. \tag{7}\]  
When the WPT system operates in a steady state, the control pulse train will be generated with constant cycles of high and low pulses.