esa-sceva/satcom-chunk-collection huggingface dataset:
# Novel Feature Extraction Method for Detecting Malicious MQTT Traffic Using Seq2 Seq
## 3 Message Queue Telemetry Transport (MQTT)  
The MQTT protocol [21] was developed for IoT environments. As shown in Figure 2, it comprises publishers such as sensors, brokers, and subscribers. The publishers include temperature, light intensity, humidity, gas, and motion sensors. The sensors publish their measured data to the broker. The broker then forwards the data to the subscribers. When subscribers subscribe to a broker, they provide the broker with interesting topics. According to the topic, the broker forwards the data to the subscribers.  
MQTT packet types are as follows. A total of 16 types were defined as shown in Table 1.  
This section introduces the four important MQTT packet structures. First, a connect packet is provided. The structure of the connect packets is shown in Figure 3, and it includes a message type, keep alive, client ID, username, and password.  
Second, a connect ACK packet is provided. The structure of the connect ACK packet is shown in Figure 4; it has a message type and return code. The return code determines whether a connection request has been accepted.  
Third, a subscription request packet is provided. The structure of a subscribe request packet is shown in Figure 5; it includes a message type and topic.  
Fourth, a publish packet is provided. The structure of a publish packet is shown in Figure 6, and it includes a message, message type, and topic.  
Figure 2: MQTT system structure.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type** & **Value** & **Direction** & **Description** \\ \hline Reserved & 0 & - & - \\ CONNECT & 1 & C \texttextgreater{} S & Connection Req \\ CONACK & 2 & S \textgreater{} C & Ack for Req \\ PUBLISH & 3 & C \textless{}\textgreater{} S & Publish \\ PUBACK & 4 & C \textless{}\textgreater{} S & ACK (QoS 1) \\ PUBREC & 5 & C \textless{}\textgreater{} S & QoS 2 \\ PUBREL & 6 & C \textless{}\textgreater{} S & QoS 2 \\ PUBCOMP & 7 & C \textless{}\textgreater{} S & QoS 2 \\ SUBSCRIBE & 8 & C \textgreater{} S & Subscribe \\ SUBACK & 9 & S \textgreater{} C & Ack for Sub \\ UNSUBSCRIBE & 10 & C \textgreater{} S & Unsubscribe \\ UNSUBACK & 11 & S \textgreater{} C & Ack for Unsub \\ PINGREQ & 12 & C \textgreater{} S & Ping Req \\ PINGRESP & 13 & S \textgreater{} C & Ack for Ping \\ DISCONNECT & 14 & C \textgreater{} S & Disconnect \\ Reserved & 15 & - & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: MQTT packet types.  
Figure 4: Connect ACK packet structure.  
Figure 3: Connect packet structure.
## 3 Results
### Impact of Varying Single Tip Mass on Frequency Response of Slitted Beam  
Optimizing the energy harvesting efficiency of slitted beams equipped with PZT requires, amongst other things, examining the impact of varying a single tip mass on the frequency response of the beams. Here, the mass on one free end is changed, while there is no tip mass on the other free end. Using this method, we can better understand how variations in tip mass affect resonance peaks and anti-resonance troughs, which in turn impact the dynamic response and energy capture capacities of the system. Designing piezoelectric energy harvesters that are more efficient requires an understanding of these effects. We analyzed the frequency response function under various single-tip mass situations to determine the best configurations for improved performance at higher frequencies.  
Figure 7 depicts the FRF of a slitted beam with a PZT layer for three tip masses. Resonance peaks at around 27 Hz and 43 Hz are observed for all three configurations, with the magnitudes of these peaks increasing with increases in tip mass. This indicates that the addition of tip mass increases the beam's potential to harvest energy. Also evident is anti-resonance between the main resonance peaks, which has a minimum value for the 5 g tip mass. In general, the inclusion of tip mass enhances the dynamic response, especially at higher frequencies, suggesting that optimizing tip mass can increase the performance of piezoelectric energy harvesters. Comprehending the effects of resonance and anti-resonance is essential for the design of energy harvesting devices that are highly efficient. Also, the value of the tip mass can make the harvester tuneable for different ambient vibrations.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Property** & **Value** \\ \hline Beam Length & 142 mm \\ Substructure Thickness h\({}_{\text{s}}\) & 0.7 mm \\ Piezo Thickness & 0.66 mm \\ Beam Width & 28 mm \\ Slit Length & 92 mm \\ Slit Width & 2 mm \\ Load Resistance & \(1\times 10^{4}\)\(\Omega\) \\ Tip Mass (m\({}_{1}\)) & 5 g \\ Tip Mass (m\({}_{2}\)) & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Baseline parameters used for the analysis of the slitted beam.  
Figure 6: Schematic of slitted beam with PZT layer showing slit parameters and tip mass configurations.
# Content Adaptation and Depth Perception
## 5 User Studies
### Results
#### 5.5.1 Task Completion Time  
Task completion times are displayed in Figure 10 split by condition and subtask. In the Order subtask, completion time was 18.33 s (SD = 11.03) for SliceView display and 17.04 s (SD = 8.94) for LookingGlass; no significant difference was found (t(19) = 0.668, \(p\) = 0.512). In the Range subtask, it was 15.18 s (SD = 3.88) for SliceView and 15.79 s (SD = 4.81) for LookingGlass; the difference was not significant (t(19) = \(-\)0.569, \(p\) = 0.576). In the Comparison subtask, it was 11.44 s (SD = 3.06) for SliceView and 11.44 s (SD = 3.65) for LookingGlass; no significant difference was found (t(19) = 0.007, \(p\) = 0.994). In the Readability task, completion time was 22.66 s (SD = 12.06) for SliceView and 32.11 s (SD = 2.45) for LookingGlass; there was a significant difference (t(19) = \(-\)3.768, \(p\) = 0.001).  
Tasks completion times were similar in both conditions when performing chart analysis subtasks, but significant differences were observed in the readability tasks. This difference could be explained by the different depth-placing capabilities of both devices: SliceView has a larger depth span and LookingGlass suffers from severe loss of resolution when items are placed next to its front or back display area.
## 4 Detection Performance and Discussion
### Initial Detection Performance  
The detection performance with the steps before the FFT as discussed previously were obtained using the outdoor experimental data for a female subject behind the wall facing the antenna at a distance of 600 cm from the antenna. The results after removing the clutter and linear trend are shown in Figure 12a. This shows that the respiration signal is very weak and difficult to see. The results after filtering in fast and slow time are given in Figure 12b,c, respectively. This shows a further reduction in the clutter resulting in an improvement in the respiration signal. Figure 12d shows that the human respiration signal is enhanced by employing the advanced automatic gain method. While the interference introduced by moving objects can have a significant effect on the received signal, this can be avoided by prohibiting people from moving in the environment.
# Multi-Satellite Observation-Relay Transmission-Downloading Coupling Scheduling Method
## 2 Problem Description
### Rule-Based Dijkstra Data Transmission Path Scheduling Method
#### 3.4.3 Node Distance Calculation Method
\begin{matrix}t_{dslium_{min}}/(t_{e}-t_{s})&Flag_{gilu}=1, Flag_{fillu}=1\\ t_{e}-t_{s}+t_{rOutsl_{n}}/(t_{e}-t_{s})&Flag_{gilu}=0, Flag_{fillu}=1\\ \inf&Flag_{fillu}=0\end{matrix} \right. \tag{27}\]  
For two strategies, the calculation of inter-node distance \(a_{l,lu}\) is expressed as  
\[a_{l,lu}=\left\{ \begin{matrix}d_{lu}-d_{l}&Flag_{fillu}=1\\ \inf&Flag_{fillu}=0\end{matrix} \right. \tag{28}\]  
After calculating the total distance \(d\) and the inter-node distance \(a\), the path selection can be performed according to the Dijkstra algorithm.
# Maintaining Secure Level on Symmetric Encryption under Quantum Attack
## 4 Programming and Testing
### Hardware Description Language Simulation  
This section will show the AES-384 and AES-512 hardware description language [19; 20; 21; 22] simulation process, mainly AES-384/512 for explanation, only with AES-384/512 attached to each process of the demonstration diagram [23; 24; 25], and not attached versions of AES-128, AES-192, or AES-384. The test content is \"Learn to walk before you run\".  
Figure 3 shows the complete information and results of the AES-384 encryption and decryption, including the parameters used in AES, standard S-box [26; 27; 28; 29], inverse S-box, Mixcolumns and InvMixColumns, as well as the input plaintext, encrypted ciphertext, key expansion, and ETC.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c} \hline \hline  & \multicolumn{10}{c}{**Plaintext 1**} & \multicolumn{10}{c}{**Plaintext 2**} \\ \hline \multicolumn{2}{c}{**Text**} & \multicolumn{10}{c}{**Thatisagoodidea8**} & \multicolumn{10}{c}{**Thatisagoodidea5**} & \\ \hline \multirow{3}{*}{ASCII} & 54 & 68 & 61 & 74 & 69 & 73 & 61 & 67 & 54 & 68 & 61 & 74 & 69 & 73 & 61 & 67 \\ \cline{2-13}  & 6f & 6f & 64 & 69 & 64 & 65 & 61 & 38 & 6f & 6f & 64 & 69 & 64 & 65 & 61 & 35 \\ \hline \multirow{6}{*}{Original key (384 bits)} & 2b & 7e & 15 & 16 & 28 & ae & d2 & a6 & ab & f7 & 15 & 88 \\ \cline{2-13}  & 09 & cf & 4f & 3c & 7e & 15 & 16 & 28 & ae & d2 & a6 & ab \\ \cline{2-13}  & 6f & 15 & 88 & 09 & cf & 4f & 3c & 2b & 2b & 7e & 15 & 16 \\ \cline{2-13}  & 28 & ae & d2 & a6 & ab & f7 & 15 & 88 & 09 & cf & 4f & 3c \\ \cline{2-13}  & 7e & 15 & 16 & 28 & ae & d2 & a6 & ab & f7 & 15 & 88 & 09 \\ \cline{2-13}  & 2b & 7e & 15 & 16 & 28 & ae & d2 & a6 & ab & f7 & 15 & 88 \\ \cline{2-13}  & 09 & cf & 4f & 3c & 7e & 15 & 16 & 28 & ae & d2 & a6 & ab \\ \cline{2-13}  & 6f & 4f & 3c & 2b & & & & & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 11: AES-512 input plaintext.  
Figure 3: AES-384 encrypted and decrypted simulation.  
Figure 4 shows the results of each round of AES-384 key expansion, and it can be seen that the value of each round key and the simulated time delay are step by step. Each round needs to wait for the execution result provided by the previous round, and it proceeds to the next round immediately after some results are outputted.  
Figure 5 shows a series of AES-512 encryption and decryption simulations and results. The most obvious difference between AES-512 and AES-384 is that AES-512 performs more rounds, and Figure 6 shows the results of each round of the AES-512 key expansion.  
Although this section does not attach the simulation process of AES-128, AES-192 and AES-256, Figure 7 illustrates the execution results of all versions from AES-128 to AES-512, which proves that we have integrated the five keys of the hardware description of AES (the language of the length version).  
Figure 4: AES-384 key expansion simulation.  
Figure 5: AES-512 encrypted and decrypted simulation.
## 2 Materials and Methods
### Transduction of Primary AML Cells with the shRNA Library  
The customized focused shRNA library was described previously [21]. The packaging of the shRNA library into lentiviral particles was performed by Sirion-Biotech GmbH (Planegg, Germany). The viral preparation had a functional titer of 4.2 \(\times\) 10\({}^{9}\) IU/mL as detected by the manufacturer in HEK293 cells. B and T cell-depleted primary AML cells were counted and 1 \(\times\) 10\({}^{7}\) (or the maximum available number) were infected with the shRNA library according to the spinoculation protocol (see Materials and Methods Section 2.8) at the multiplicity of infection (MOI) ranging from 2 to 10, depending on the available cell number. Note that because primary AML cells are difficult to transduce, this MOI corresponds to an infection rate of 10-50%, depending on the primary sample, not exceeding the integration rate of one copy of the expression plasmid per cell. After 20 h of incubation at 37 \({}^{\circ}\)C and 5% CO\({}_{2}\) the cells were washed twice in PBS, resuspended in the culture medium and returned to the cell culture incubator. At 96 h post transduction, the infection rate was analyzed by assessing the percentage of RFP-positive cells using BDCanto II (Becton Dickinson, Franklin Lakes, NJ, USA) and the FlowJo software. The cells were counted, and half of the cells were collected as a baseline sample and frozen away at \(-20\)\({}^{\circ}\)C until genomic DNA isolation. The remaining cells were cultured for 20 more days and then collected as a final timepoint sample.
## 2 Materials and Methods
### Genome Size and Endopolvploid Estimation by Flow Cytometry  
For the estimation of genome size, leaves of plants collected in the field, adventitious shoots, rooted plants on \(\frac{1}{2}\) MS, and regenerated plants in hydroponic and soil cultures were used. Samples were prepared as previously described [40], using Galbraith's buffer [41] supplemented with propidium iodide (PI; 50 \(\mu\)g mL\({}^{-1}\)) and ribonuclease A (50 \(\mu\)g mL \({}^{-1}\)). _Solanum Lyopersicum_ cv. Stupicke (2C = 1.96 pg; [42]) was used as an internal standard. For each sample, PI fluorescence in 3000-5000 nuclei was measured using a CyFlow SL Green (Partec GmbH, Munster, Germany) flow cytometer. Analyses were performed using linear amplification. Histograms were evaluated using the FloMax program (Partec GmbH, Munster, Germany). The coefficient of variation (CV) of the G\({}_{0}\)/G\({}_{1}\) peak of _A. arenasa_ ranged between 2.59 and 6.92%. Nuclear DNA content was calculated using the linear relationship between the ratio of the 2C peak positions of _Arabidopsis_/_Solanum_ on a histogram of fluorescence intensities.  
For endopolvploid estimation, callus derived from cotyledon + hypocotyl explants cultured on the \(\frac{1}{2}\) MS medium supplemented with 2 mg L\({}^{-1}\) 2,4-D and 2 mg L\({}^{-1}\) BAPand callus kept on 1 mg L\({}^{-1}\) TDZ for 3 weeks and leaves of initial 3-6-week-old seedlings were used. The samples were prepared and analyzed similarly to those of genome size analysis, however, PI fluorescence was measured in 5000-10,000 nuclei, and logarithmic amplification of the signal was used. After histogram evaluation, the percentages of the nuclei with different DNA content (i.e., of different ploidy) and the (\(\Sigma>2\)C)/2C ratio [43] were calculated.  
In this study, nuclei with at least 8C DNA were considered to be endopolyploid since it was not possible to distinguish by flow cytometry the 4C nuclei originating from the cells that have just entered endoreduplication from those originating from the cells in the G\({}_{2}\) phase of the mitotic cycle.
# LaRaWAN Metering Infrastructure Planning in Smart Cities
###### Abstract  
The planning of metering network infrastructure based on the concept of the Internet of Things primarily involves the choice of available radio technology. Then, regardless of the type and availability of power sources, energy conservation should be one of the main optimization criteria. For this reason, LPWANs operating in unlicensed ISM bands appear to be a suitable solution in urban environments due to their sub 1 GHz propagation properties. High signal penetration and coverage make them applicable in urban areas with buildings and various obstacles. Therefore, this article presents solutions developed to support the planning process of implementing a LoRaWAN network infrastructure aimed at monitoring and collecting electricity meter data in smart cities. To this end, an algorithm has been proposed to support the selection of the number of LoRaWAN gateways and their deployment, as well as the selection of transmission parameters at the measurement nodes with a particular focus on geographic data from real maps.  
LoRaWAN; IoT; clustering; network optimization; smart city; AMI 1
# Nonlinear Tank-Level Control Using Dahlin Algorithm Design and PID Control
## 3 Results  
### Single Tank Control Results  
#### 3.1.1 Single Tank Model Linearization  
The linearization of the ST system shown in Figure 3 is based on the recording of its step response in Figure 6, where the adopted system parameter values are presented in Table 2.  
As can be seen in Figure 6, the static gain of the system \(K\) and the time constant \(T_{I}\), which is inversely proportional to the steepest tangent of the system step response and the time delay \(\tau\), can be determined. Therefore, we can obtain the linearized tank transfer function in the following form:  
\[G(s)=\frac{K}{T_{I}s+1}e^{-\tau s}=\frac{51.6}{9.9s+1}e^{-4.3s}. \tag{29}\]  
In addition, it can be seen that the rise time, defined as the time for the response signal to increase from 0.1 to 0.9 of its final value, is \(T_{r}\) = 25.91 s.  
To confirm the model linearization procedure, the responses of the nonlinear tank system shown in Figure 3 and the resulting linearized model provided with relation (29) are compared in Figure 7.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Symbol** & **Value** & **Unit** \\ \hline \(a\) & 0.0134 & m\({}^{2}\) \\ \hline \(A\) & 0.1257 & m\({}^{2}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: ST parameters.  
Figure 6: The step response of the ST system.  
Figure 7: The Simulink model of the tank system and the linearized tank model.  
The responses of the tank system and the linearized tank model, which largely coincide, confirm the validity of the applied procedure as shown in Figure 8.
# A Novel Data Forwarding Strategy for a Drone Delay Tolerant Network with Range Extension
## 5 Evaluation
### Discussion  
The above simulation studies reveal that HFPP outperforms other compared methods. HFPP, due to the inter-drone communication, can route the data packets through multiple hops whereas [16]  
Figure 3: (**a**) delivery ratio of comparable schemes, (**b**) average data delivery delay of comparable schemes, when number of drones varies.  
is a single hop approach. Moreover, [16] does not cover long-distance waypoints as drones are not assumed to re-charge their battery. HFPP can be considered as a routing protocol in the same way as EPIDEMIC and EBR and exchanges data packets upon opportunistic contacts. However, with HFPP only single a copy of each data packet exists. The results show that when the drones' density is low, HFPP outperforms these routing protocols and this is because the probability of contacts is low. However, by increasing the drones' density, Epidemic performance is similar to HFPP though the overhead is massive. Hence, HFPP can be very useful in such logistics networks as the drones' density cannot be high in a real-world scenario. In addition, HFPP is the only approach that considers inter-node communications and modifying the mobility pattern.  
Figure 4: Overhead ratio of comparable schemes when number of drones varies.  
Figure 5: _Cont._
# A Robust and Efficient Method for Effective Facial Keypoint Detection
## 4 Experimental Results
### Main Results
#### 4.2.4 Regression and Heatmap Loss Weight Ratio Results  
To evaluate the influence of different regression and heatmap loss weight combinations on model performance, we adjusted the weight ratio of these two losses to find the optimal balance between regression accuracy and heatmap quality. The weight ratio of regression to heatmap is denoted as \(\lambda_{rh}\).  
\[\lambda_{rh}\in\{0.2,0.5,1,2,5\} \tag{9}\]  
The results of the comparative experiments are shown in Figure 4, where we found that, when the weight ratio is 1, the minimum MSE value is 0.0462. When the weight ratio deviates from the optimal range, the mean squared error (MSE) value increases, leading to worse performance. This is because, when the weight ratio is close to 1, the model can effectively balance between predicting global keypoint coordinates and expressing local heatmap details. This equilibrium enables accurate keypoint prediction and detailed feature refinement, while also preventing significant differences during gradient updates, thus avoiding issues like gradient explosion or vanishing. If the weight ratio exceeds 1, the model prioritizes global coordinate prediction but lacks in capturing local details, which hampers its ability to refine keypoint positions, especially under conditions of severe occlusion. On the other hand, if the weight ratio is below 1, the model overly focuses on local heatmap features at the expense of global keypoint accuracy. This results in the poor capture of global structures and overfitting to local details.
## 2 Methods
### Isokinetic Strength Testing  
The isokinetic strength of the hamstring and quadriceps muscles was measured using a Cyberx II isokinetic dynamometer (Lumex Inc., Ronkonkoma, New York, NY, USA). For each angular velocity, peak isokinetic torque was recorded simultaneously, and the torque produced by the limb weight and the dynamometer arm was extracted from the collected data. Each participant then sat on the dynamometer in an adjustable chair, with their upper body secured by straps placed diagonally across the chest and around the hips and thighs to minimize any unwanted joint movement. The knee being tested was positioned at 90\({}^{\circ}\) of flexion (with 0\({}^{\circ}\) representing full knee extension) to align with the axis of the dynamometer lever arm at the distal point of the lateral femoral condyle. The length of the lever arm was adjusted individually for each participant, and the resistance pad was positioned just above the medial malleolus. The other leg remained relaxed, hanging freely without any applied force. Knee movements followed a specific sequence: extension was performed from the 90\({}^{\circ}\) flexed position, and flexion was performed from full extension (0\({}^{\circ}\)). Participants were instructed to kick their tested leg as hard and fast as possible through the full range of motion (ROM) while keeping their arms crossed over their chest. Three repetitions were performed at each angular velocity, with the peak torque (PT) value being recorded. A 30 s rest period was allowed between repetitions, and a 60 s rest interval was given between assessments at different angular velocities. During the session, participants were encouraged to perform maximal voluntary contractions with verbal positive reinforcement. Data were collected at angular velocities of 60, 180, and 300\({}^{\circ}\)/s, and maximal isokinetic strength was identified as the torque produced by the hamstring and quadriceps muscles during movements through their full ROM [26; 27]. The ratio of peak torques at each angular velocity reflects the concentric strength ratio between the knee flexors and extensors. The traditional H:Q ratio was calculated by dividing each participant's highest concentric peak torque for leg flexion by their highest concentric peak torque for leg extension.