esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed TFR Approach
### Operations
#### 3.2.3 Fault Recovery Acknowledgement  
The last step of the TFR approach is to identify the communication paths between the slave ports and the MC port, and to start the standard synchronization mechanism between the clocks because the MC still does not precisely identify the communication paths with the slave ports. Therefore,  
Figure 5: The structures of the Link-Alive and Sync-Request messages. IP: Internet Protocol; UDP: User Datagram Protocol.  
the slave ports unicast the synchronization Acknowledgement message (Sync-Request message), which identifies the Active slave ports that have been used in the synchronization hierarchy. Thus, the slave ports exclusively target the best MC ports that have been used in the synchronization hierarchy. The content of the Sync-Request message includes specific information to notify the MC port that receives this particular message and the slave nodes that are requesting to start the synchronization mechanism immediately.  
The fields of the Sync-Request message are addressed as follows:  
* Clock flag: identifies the port state of the transmitting node as either a slave port (flag = 1) or a master port (flag = 0).
* Sync flag: identifies the port of the transmitting node that is requesting to start the clock synchronization mechanism immediately (flag = 1).
* PortID: identifies the port identifier of the transmitting node (slave port).
* MPortID: identifies the port identifier of the receipt node (master port).  
In this case, the slave ports will identify the Clock flag as (flag = 1) and the Sync flag as (flag = 1), and they use the PortID as their specific port identifier and the MPortID as the specific port identifier of the MC in the PTP system. This specific message uses the PTP message header and signaling message type, as shown in Figure 5b.
# Three-Dimensional Assessment of Upper Limb Proprioception via a Wearable Exoskeleton
## 2 Materials and Methods
### Task and Procedure  
During the experiment, participants sat on the workstation chair wearing one of the robotic exoskeletons, according to the tested body side. They were requested to grasp a pressure-sensitive handle of the contralateral exoskeleton (non-tested side). Arms and forearms were firmly strapped to ensure arm positioning's repeatability and limit inter-trial variability and undesired relative movements during the experiment.  
The proprioceptive test consisted of an ipsilateral JPM task [9], involving a single trial with two main phases (Figure 1B):  
1. Phase 1 or \"Stimuli Presentation\" in which the blindfolded user 'arm was passively moved by the exoskeleton from an \"Initial Position\" (AA\({}_{\text{Shoulder}}\): 10\({}^{\circ}\), FE\({}_{\text{Shoulder}}\): 5\({}^{\circ}\) and FE\({}_{\text{Elbow}}\): \(-40^{\circ}\)) to a \"Proprioceptive Target\" (AA\({}_{\text{Shoulder}}\): 60\({}^{\circ}\), FE\({}_{\text{Shoulder}}\): 80\({}^{\circ}\) and FE\({}_{\text{Elbow}}\): \(-10^{\circ}\)). An auditory cue (high-frequency beep) was provided when the robot reached the Proprioceptive Target. Successively, after the target presentation, the robot moves back the users' arm to the initial posture (Block 3).
2. Phase 2 or \"Active Matching\" started with a sound and required the subjects to match, as accurately as possible, the previously experienced stimuli by actively moving her/his arm: this was possible because the exoskeleton was set in a transparent modality, i.e., without applying any force. Participants could stop the trial when matched position by squeezing the handle held in the contralateral hand (Block 4). After each trial's completion, the robot drove back the subject's arm to the initial position before initiating the next trial (Block 5), Figure 1B.  
We tested the proprioceptive acuity in two different modalities:  
* \"Single Joint\" (SJ): only one of the three examined degrees of freedom was independently tested for JPM task (AA\({}_{\text{Shoulder}}\) or FE\({}_{\text{Shoulder}}\) or FE\({}_{\text{Elbow}}\)) (Figure 1C top).
* \"Multiple Joints\" (MJ): proprioceptive targets were presented by moving all the three degrees of freedom in a multi-joint fashion (AA\({}_{\text{Shoulder}}\) + FE\({}_{\text{Shoulder}}\) + FE\({}_{\text{Elbow}}\)) (Figure 1C bottom).  
The whole experimental session included four target sets (3 SJ and 1 MJ) for both left and right arms, which were pseudo-randomly distributed across participants in order to avoid possible target sequence effects. Each target sets counted 10 proprioceptive target presentations and the relative matching tasks. A total of 80 trials (30 SJ +10 MJ distributed on the two arms) were administered to each subject, with a 5-min break between each target set, for a total duration of about 1 h for the whole experiment.
## 3 Link Quality Estimation Routing Protocol Design
### Minimum Hop Field Establishment  
MHFR can provide optimal path to send data to the sink. In WSNs, let the hop count of the sink node be 0. For other nodes, the minimum hop count is defined as the number of intermediate nodes from that node to the sink on the optimal path plus 1. We use the flooding algorithm to establish the hop count field as shown in Table 1. Initially, each node sets its hop count to, for example 1000, more than the maximum hop count reachable in the network. After the sink broadcast an ADV (advertisement) message containing its own hop count (0 initially), the message propagates through the network. Upon hearing the an ADV message from node \(M\), node \(N\) has a path with hop count \(H_{M}+1\), where \(H_{M}\) is the hop count of node \(M\). Node \(N\) then compares its current hop count \(H_{N}\) with \(H_{M}+1\). If the new hop count is smaller, it sets \(H_{N}\) to \(H_{M}+1\) and broadcasts an ADV message with its new hop count. If the new hop count equals to its current one, it adds node \(M\) to its forwarding node set but does not broadcast any ADV message. If the new hop count is larger, it just rejects the message data. Eventually, every node may calculate the minimum hop to the sink through flooding and get each own forwarding node set.  
Suppose the message forwarding time of each node is the same \(T\), it can be proved that however long the node is apart from the sink, it will hear a message with minimum hop only once [1]. However, in above design, some practical issues have been ignored such as delays from transmission, propagation, processing and channel error. For example, for a message with 30 bytes, if the transmission rate is 9600 bps, the transmission time is \(\frac{30\times 8}{9600}=0.025\)s. If the delays are near to or lager than transmission time, some nodes may receive more than one message containing minimum hop count. To reduce the total number of messages in the network, we introduce a random delay \(T_{w}\). When the node hears a message containing smaller hop count, it defers the broadcast to the time \(T_{w}\). In that case, it may broadcast only once, carrying its minimum hop count.
## 3 On-the-Fly KeyGen and Encryption  
It is crucial to recognize that, although the protocol shown in Figure 1 adheres to the principles of a secure multiparty sum involving three parties, as outlined in [14], we diverge from this method by ensuring that the dummy third party remains unaware of the final (secure) sum. Furthermore, the performance of the actual protocol is improved if the sequence (ordering) in which transactions are executed is taken into account.  
Clearly, since any consideration about the messages to be exchanged includes parsimony and duplication avoidance, the protocol can be modified as follows (Algorithm 1):  
```
0: Users \(U_{1},U_{2}\), which will be called Client and Server, respectively
0: Peer to Peer Key sharing and Encryption. Initialization: - \(U_{1}\) generates a dummy third party \(U_{3}\) and shares the connection details with the server \(U_{2}\). - \(U_{i}\) reads three (true) random numbers from the devices attached to it: \(r_{i,1},r_{i,2}\) and \(r_{i,3}\), \(i=1,2,3\). Procedure: 1. First Round: - \(U_{1}\xrightarrow{r_{1,2}}U_{2}\xrightarrow{r_{2,3}}U_{3}\xrightarrow{r_{3,2 }}U_{2}\xrightarrow{r_{2,1}||s_{2}}U_{1}\), with \(s_{2}:=r_{1,2}+r_{2,2}+r_{3,2}\). - \(U_{1}\xrightarrow{r_{1,3}}U_{3}\xrightarrow{s_{3}}U_{2}\), with \(s_{3}:=r_{1,3}+r_{2,3}+r_{3,3}\). - \(U_{3}\xrightarrow{r_{3,1}||s_{3}}U_{1}\). 2. Key determination: - \(U_{1}\) computes \(K_{1}:=s_{1}+s_{2}+s_{3}\), with \(s_{1}:=r_{1,1}+r_{2,1}+r_{3,1}\). - \(U_{2}\) computes \(K_{2}:=s_{2}+s_{3}\) Encryption and decryption: - \(U_{1}\xrightarrow{s_{1}||c:=K_{1}\oplus m}U_{2}\) - \(U_{2}\) decrypts the message as \(m=(s_{1}+K_{2})\oplus c\)
```  
**Algorithm 1** SMP-OTF4 IOT: On-the-Fly KeyGen and Encryption  
We implement the SMP-OTF4 IOT protocol described above to establish a common key \(K\) between any two IoT devices that can be used as an OTP to secure communication between two parties \(P_{1}\) and \(P_{2}\) that requires a third (dummy) server \(P_{3}\), whose job is to receive inputs and broadcast the sum of the inputs.  
The procedure described above is visualized in Figure 2.
# Floating Step-Down Converter with a Novel Lossless Snubber
## 4 Experimental Results  
In this section, the experimental results are displayed as follows:  
From Figures 10-14 it can be seen that the rising slope of the voltage \(v_{ds}\) on the switch \(Q_{1}\) can be slowed down during the switch cutoff period, so the ZVS cutoff can be achieved. Furthermore, the lighter the load, the more obvious this phenomenon. The reason is that when the load is lighter, the current flowing through the resonant capacitor \(C_{r}\), \(i_{Cr}\), is also smaller, and therefore the resonant capacitor \(C_{r}\) discharges for a longer period of time, thereby making the rise time of the voltage \(v_{ds}\) on the switch \(Q_{1}\) longer. In addition, some small high-frequency oscillation current can be seen during the switch cutoff period, which can be attributed to parasitic components.  
From Figures 11 and 15, the current spike in the resonant current \(i_{L}\) is due to the reverse recovery current of the diode at 100% and 10% loads.  
In Figures 12 and 16, the voltage-second balance of the output inductor \(V_{Lo}\) at 100% and 10% loads can be seen.  
From Figures 13 and 17, it can be seen that the output voltage \(V_{o}\) can be stabilized at a certain value under closed-loop control, while the output current \(I_{o}\) has a more obvious ripple at light load. Incidentally, the power switch \(Q_{1}\) on the \(v_{gs}\) at 100% load and 20% load under the duty cycle \(D\) is about 45% and 40.2%, respectively. Therefore, the larger the load, the larger the duty cycle due to the parasitic components.  
Figure 14: Measured waveforms at 20% load: (1) \(v_{gs}\); (2) \(v_{ds}\); (3) \(i_{ds}\).
## 2 System Model  
At the transmitter side, the source information \(\textbf{s}=\left[\textbf{s}_{1},\cdots,\textbf{s}_{i},\cdots\textbf{s}_{N_{t} }\right]^{T}\) where each symbol \(s\) is mapped to constellation symbols by taking symbols from a set of the constellation alphabet \(\Omega\). The transmitted signal \(\textbf{x}=\left[\textbf{x}_{1},\cdots,\textbf{x}_{i},\cdots\textbf{x}_{N_{t} }\right]^{T}\) is constructed by the modulated symbol **s**, where \(\textbf{x}_{i}\) denotes a signal transmitted by the \(i^{\text{th}}\) UE device. The vector \(\textbf{y}=\left[\textbf{y}_{1},\cdots,\textbf{y}_{i},\cdots\textbf{y}_{N_{t} }\right]^{T}\) represents the receiving signal at the BS, where \(\textbf{y}_{i}\) denotes a signal received by the \(i^{\text{th}}\) receive antenna, and:  
\[\textbf{y}=\textbf{H}\textbf{x}+\textbf{w} \tag{1}\]  
where **w** is the \(N_{r}\)-by-one additive white Gaussian noise (AWGN) vector following \(CN\left(0,\sigma^{2}\right)\) with \(\sigma^{2}\) representing the average power of the noise. In Equation (1), the matrix **H** denotes the \(N_{r}\)-by-\(N_{t}\) flat fading channel gain, and **H** can be expressed as:  
\[\textbf{H}=\left[\begin{array}{cccc}h_{11}&h_{12}&\cdots&h_{1N_{t}}\\ h_{21}&h_{22}&\cdots&h_{2N_{t}}\\ \vdots&\vdots&\ddots&\vdots\\ h_{N_{t}1}&h_{N_{t}2}&\cdots&h_{N_{t}N_{t}}\\ \end{array}\right] \tag{2}\]  
where the element \(h_{i,j}\), \(i=\{1,2,\cdots,N_{r}\},j=\{1,2,\cdots,N_{t}\}\) denotes the channel impulse response between the receiving antenna \(j\) and the user \(i\). It is noted that \(h_{ij}\) follows an i.i.d. Gaussian distribution with zero mean and unit variance. In addition, the channel matrix **H** is assumed to be known perfectly at the BS [28; 29].  
Figure 1: MIMO system signal detection structure diagram.
## 3 The Results of Experiments
### Prediction of Welding Pool Boundary via U-Net
The prediction image area is around the boundary area when observed by humans. In the normal welding process, the weld boundary should be symmetrical or vertical, as shown in Figure 11a. On the other hand, if the welding track deviates from the weld line, the weld boundary is not symmetrical, and the weld line found using image processing techniques will also deviate from the reference weld line, as shown in Figure 11b. Thus, from the results from the prediction images, the authors can find the \begin{table}
\begin{tabular}{c c c} \hline \hline
**Training Data** & **Test Data** & **Total Number** \\ \hline
1378 & 154 & 1532 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: U-Net dataset. Figure 11: Data variation during U-Net model training. (**a**) Train accuracy and test accuracy behavior; (**b**) Train loss and test loss behavior. -held line position from the boundary area by using image processing. It is well-established that the maximization of the training data can yield superior parameter estimation. The utilization of weld pool boundaries as feature points for effective prediction via U-Net is a salient aspect of the discussion. Subsequent to this, the optimization of the model becomes a pertinent subject for deliberation.
# Multivariate Statistical Process Control Using Enhanced Bottleneck Neural Network
## 3 Case Study : Wastewater Treatment Plant (WWTP) Monitoring
### Simulated Process Case  
This benchmark plant is composed of a five compartment activated sludge reactor consisting of two anoxic tanks followed by three aerobic tanks (see Figure 4). The plant thus combines nitrification with pre-denitrification in a configuration that is commonly used for achieving biological nitrogen removal in full-scale plants. The activated sludge reactor is followed by a secondary settler. The simulated wastewater treatment process BSM1 (Figure 5) is used under dry weather during 7 days under normal operating conditions. The sampling period is 15 min, where the training dataset is composed of 673 observations and 672 observations are used for the test phase. The plant is designed for an average influent dry-weather flow rate of 18,446 m\({}^{3}\).d\({}^{-1}\) and an average biodegradable chemical oxygen demand (\(COD\)) in the influent of 300 g\(\cdot\)m\({}^{-3}\). For more details, the simulated wastewater treatment process BSM1 is available on [41].  
Figure 3: Reconstruction principle.  
The monitored sensors of the BSM1 used in this work are given in Table 1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**N** & **Monitored Sensors** & **Notation** \\ \hline
1 & Dissolved oxygen in \(Tank_{1}\) & \(S_{O}\) \\
2 & Nitrate and nitrite nitrogen in \(Tank_{1}\) & \(S_{NO}\) \\
3 & \(NH_{4^{+}}+NH_{3}\) nitrogen in \(Tank_{2}\) & \(S_{NH}\) \\
4 & Soluble biodegradable organic nitrogen in \(Tank_{2}\) & \(S_{ND}\) \\
5 & Particulate biodegradable organic nitrogen in \(Tank_{2}\) & \(X_{ND}\) \\
6 & Dissolved oxygen in \(Tank_{3}\) & \(S_{O}\) \\
7 & Nitrate and nitrite nitrogen in \(Tank_{3}\) & \(S_{NO}\) \\
8 & \(NH_{4^{+}}+NH_{3}\) nitrogen in \(Tank_{5}\) & \(S_{NH}\) \\
9 & Soluble biodegradable organic nitrogen in \(Tank_{5}\) & \(S_{ND}\) \\
10 & Particulate biodegradable organic nitrogen in \(Tank_{5}\) & \(X_{ND}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: The monitored sensors for BSM1 plant.  
Figure 4: General overview of the benchmark simulation model No. 1 (BSM1) plant.  
Figure 5: Simulink of BSM1.
# Parameters Sensitivity Analysis of Position-Based Impedance Control for Bionic Legged Robots' [PERSON]
## 3 HDU Position-Based Impedance Control
### Mathematic Model of HDU Position-Based Impedance Control
#### 3.1.1 Principles of Impedance Control  
The inner loop of the position-based impedance control is a closed-loop control. When the inner loop is affected by a disturbance force, the impedance control outer loop should be added to the system to equip the system with impedance control characteristics. The function of this outer loop is to transform the disturbance force into position error. Then, the desired stiffness \(K_{D}\) can be obtained, which causes an elastic force. In the same way, desired damping \(C_{D}\) and desired mass \(m_{D}\) are obtained, which can cause viscous force and inertia force, respectively. The HDU force schematics with impedance control outer loop are shown respectively in Figures 4 and 5.  
During the robot's walking process, the load, such as grounds and steps, provides the disturbance force to the HDU, because the force sensor is mounted on the piston end of the HDU. In this paper, the force control system of the simulated load provides the disturbance force to the performance test platform.  
As it is shown in Figure 4, the load is pressed to position \(\Delta X\), \(B_{p2}\) refers to damping coefficient at load, \(m_{l2}\) refers to equivalent mass at load, and \(F_{f2}\) refers to friction at load. The force acting on the piston, which is provided by the sensor, is the disturbance force of the HDU position control system, and is defined as \(\Delta F_{a}\). Further, the force acting on the load, which is provided by the sensor, is defined as \(\Delta F_{b}\). The force balance relation in Figure 4 can be expressed as follows:  
\[\Delta F_{b}=F_{L}-m_{t2}\Delta\ddot{X}-B_{p2}\Delta\dot{X}-K\Delta X-F_{f2} \tag{1}\]  
Third, the force schematic of the HDU is showed in Figure 5.  
Figure 4: Force schematic of the load.  
Where \(B_{p1}\) refers to the viscous damping coefficient, \(m_{t1}\) refers to the equivalent mass at piston, and \(F_{f1}\) refers to friction in the cylinder. The position error can be expressed as follows:  
\[\Delta X=\frac{\Delta F_{a}-F_{f1}}{(m_{t1}+m_{D})s^{2}+\big{(}B_{p1}+C_{D}\big{)} s+K_{D}} \tag{2}\]  
Based on the theoretical analysis above, the schematic of the position-based impedance control is shown in Figure 6.  
As can be seen in Figure 6, when the disturbance force tested by the force sensor acts on the HDU, the impedance control outer loop generates a corresponding calculated position that disturbs the input position. Then, the new input to the position control system is formed. Thus, the final input signal enters the position control inner loop, and a new output position is formed to equip the system with impedance characteristics.
# Multi-Tier Cellular Handover with Multi-Access Edge Computing and Deep Learning
## 5 Proposed Algorithm
### Benchmarking Scheme  
#### 5.1.1 Handover Logic  
For the UE to trigger a handover, its RSS has to be greater than the A3 entry condition. Table 4 describes the equations and offset values used. This table is used for the decision made after the TTT period, whether it is an A1 or an A3 handover.  
If it is an A1 handover, it will revert back and remain connected to the source gNB. If it is an A3 handover, the UE will move on to the handover initiation phase. Otherwise, if the UE's CQI for the serving BS drops below a value of 1 during this process, the UE becomes idle and begins to reconnect to the base station that meets the A1 handover conditions. For all cases, the handover trigger instance will be recorded.
## 5 Conclusions  
In this paper, the proposed antenna is designed for 5G applications at sub-6 GHz bands. The antenna is structured using MTM technology to miniaturize the size up to \(\lambda/8\) at 3.5 GHz. The antenna shows four operating bands with S\({}_{11}\leq-10\) dB at 3.7 GHz, 4.6 GHz, 5.2 GHz, and 5.9 GHz with an average gain from 6.1 dBi to 8 dBi. To ensure antenna operation with a high reconfiguration mechanism, four LDR diodes are conjugated to the TR structure to control the surface current flow for frequency control. Therefore, the antenna operation and performance nominated it as an excellent candidate for 5G networks at sub-6 GHz frequency bands. The antenna performance in terms of S\({}_{11}\) and gain spectra along with the radiation patterns that are evaluated from the numerical simulations agrees very well with measured results. Finally, the investigated results reveal that the proposed antenna is an excellent candidate for short and medium wireless communications of portable wireless systems.  
Conceptualization, S.T.A.-H.; methodology, T.A.E.; software, A.A.I.; validation. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors would like to express their thanks and gratitude to express their thanks to the International Applied and Theoretical Research Centre (IATRC), Baghdad Quarter, Iraq for their support during the measurement process for their unconditional support.  
The authors declare no conflict to interest.
# Visual Object Detection with DETR to Support Video-Diagnosis Using Conference Tools
###### Abstract  
Real-time multilingual phrase detection from/during online video presentations--to support instant remote diagnostics--requires near real-time visual (textual) object detection and preprocessing for further analysis. Connecting remote specialists and sharing specific ideas is most effective using the native language. The main objective of this paper is to analyze and propose--through DEtection TRransformer (DETR) models, architectures, hyperparameters--recommendation, and specific procedures with simplified methods to achieve reasonable accuracy to support real-time textual object detection for further analysis. The development of real-time video conference translation based on artificial intelligence supported solutions has a relevant impact in the health sector, especially on clinical practice via better video consultation (VC) or remote diagnosis. The importance of this development was augmented by the COVID-19 pandemic. The challenge of this topic is connected to the variety of languages and dialects that the involved specialists speak and that usually needs human translator proxies which can be substituted by AI-enabled technological pipelines. The sensitivity of visual textual element localization is directly connected to complexity, quality, and the variety of collected training data sets. In this research, we investigated the DETR model with several variations. The research highlights the differences of the most prominent real-time object detectors: YOLO4, DETR, and Detectron2, and brings AI-based novelty to collaborative solutions combined with OCR. The performance of the procedures was evaluated through two research phases: a 248/512 (Phase1/Phase2) record train data set, with a 55/110 set of validated data instances for 7/10 application categories and 3/3 object categories, using the same object categories for annotation. The achieved score breaks the expected values in terms of visual text detection scope, giving high detection accuracy of textual data, the mean average precision ranging from 0.4 to 0.65.  
object visual detection; DETR; multilingual OCR; real-time translation; remote diagnostics; YOLO4; Detectron2; realtime text detection; assessment