esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Analytical Results
Figure 5 shows the ABER evolution for single-hop, dual-hop, triple-hop or quad-hop LOS THz link configurations over a wide range of transmitted power through strong atmospheric turbulence, strong NZB pointing errors, rain along with moderate-to-high air humidity conditions. Thus, in view of the above, Figure 5 depicts the worst-case scenario for each topology. In order to address this issue, we have selected the high gain transceiver pair of \(G=70\) dBi along with the operation frequency \(f=0.3\) THz which is more resilient to humid air than \(f=0.35\) THz, as mentioned above. The results demonstrate that, under these adverse circumstances, it may not be wise to extend the end-to-end link length beyond two or three hops, especially for low transmitted power values and high ABER performance demands. Figure 6 visualizes the ABER evolution for dual-hop or quad-hop LOS THz link configurations with different \(L\)-PAM modulation schemes over a wide range of transmitted power through strong atmospheric turbulence, weak NZB pointing errors, rain and moderate air humidity conditions. The ABER performance comparison between dual-hop configurations indicates that 2-PAM, which is a special case equivalent to OOK, outperforms 8-PAM. Nevertheless, considering that 8-PAM is more bandwidth-effective than OOK, it is highlighted that especially for higher transmitted power values, the selection of 8-PAM instead of OOK seems to be reasonable at the expense of an acceptable ABER increase for very high  
Figure 4: ABER vs. transmitted power for a triple-hop THz link configuration under strong turbulence, moderate-to-high water vapor concentrations, different operational frequencies and strong NZB pointing errors. bandwidth demanding applications. Additionally, the ABER performance comparison between quad-hop configurations indicates that, especially for lower transmitted power values, 8-PAM outperforms 16-PAM. Thus, the number of symbols to be transmitted should be increased with caution. Figure 5: ABER vs. transmitted power for single-hop and multiple-hop THz link configurations under strong turbulence, rain, high water vapor concentration and strong NZB pointing errors. Figure 6: ABER vs. transmitted power for dual-hop and quad-hop THz link configurations with 2-, 8- or 16-PAM modulation formats under strong turbulence, rain, moderate water vapor concentration and weak NZB pointing errors.
# Mixed Reality-Based Robotics Education--Supervisor Perspective on Thesis Works
## 3 Results
### Student B--Realizing a Digital Twin for a Six-Axis Robot Using Augmented Reality
#### 3.2.3 Main Findings  
Improved Control and Visualization: The digital twin significantly enhances the ability to visualize and control the six-axis robot, providing real-time feedback and accurate modelling of its actions.  
Positive User Feedback: Users found the digital twin to be a valuable tool for understanding and controlling the robot, highlighting its potential for industrial applications. Users responded positively to the application, noting that it made learning more interactive and enjoyable.  
Technical Challenges: Some challenges were identified, including the need for precise synchronization between the physical robot and its digital twin, and the performance limitations of current AR technologies. Furthermore, the solution is bound to the the real robot as a basis for the digital twin and therefore limits access for in-class use.
## 2 Ground Fault Protection of Ungrounded System  
### Characteristics of Ungrounded Systems  
According to IEC 60364-1, the grounding method of distribution lines can be classified into three types: The TT system, TN system, and IT system. The first character represents the connection between the neutral point of the power facility and the ground, and the second character represents the connection between the enclosure of the load facility and the ground. The letter T (Terra) indicates direct grounding, N (Neutral) denotes direct connection to the neutral conductor, and I (Isolation) refers to ungrounded or high-impedance grounding exceeding 2000 [\(\Omega\)][22].  
Amongst the three systems, the IT system is completely insulated from the earth or grounded through high-impedance grounding. Hence, only the enclosure of the load facility is grounded. The ungrounded method is mainly used for systems with short feeders and low voltage. Due to an absence in grounding, the occurrence of electrical corrosoins of the ground electrodes are less frequent. Therefore, the method is adequate for the DC distribution systems where corrosoins are of serious concern [7].  
Unlike the case of grounded systems, when a SLG occurs in an ungrounded system, a return path for the fault current is not formed through the power supply. Since the induced fault current is small in magnitude, being several [A] or less, there is indeed an advantage of continuous power supply. Moreover, if necessary, the power supply can be continued by switching to a V-connection. Considering these characteristics, ungrounded systems are generally applied to airports, hospitals, and large-scale factory facilities, where considerable economic damage may be inflicted in the event of power outages or accidents, which may pose a risk to public safety [8; 9].  
On the other hand, since a SLG fault current of the ungrounded system is very small, complications occur when detecting the fault. If the first fault is left unattended, the electric potential of the sound phase can escalate to 6-8 folds of the phase voltage. Hence, the primary fault must be accurately detected and rapidly restored to prevent accident propagation or device damages. In the case of a secondary fault, as with other system grounding methods, a large fault current is induced, and the power is consequently cut off.
## 2 Experimental Equipment Construction and Thermal Deformation Measurement
### Built-in Spindle Cooling Channel Patterns  
Cooling channel patterns of the motor can be divided into helical type, paralleling type, axial type, and trapezoidal type. The cooling channel patterns are shown in Figure 2. In these four cooling channel patterns, the front bearing is cooled by the coolant before the motor. The coolant flows in the helical type cooling channel in the spiral direction for motor cooling, as shown in Figure 2a. In terms of the paralleling type cooling channel, the two coolant flow streams in a single-turn pair channel merge at a meeting point and then move downstream to the next single-turn pair until they reach the coolant outlet, as shown in Figure 2b. The axial type cooling channel performs reciprocating cooling in parallel, as shown in Figure 2c. In terms of the trapezoidal type channel, the channel pattern is similar to the helical type cooling channel. The cross-section shape in the helical type channel is rectangular,  
Figure 1: Experimental equipment. (**a**) Horizontal machine tool and built-in spindle; (**b**) Built-in spindle; (**c**) Dimensional drawing.  
whereas the cross-section shape in the trapezoidal type channel is trapezoidal, as shown in Figure 2d. A single-turn cycle design and paralleling type design were considered for bearing cooling, as shown in Figure 2e,f, respectively. In this study, an optimal design for these four cooling channel patterns and corresponding front bearing channels was obtained; the related detailed dimensions are shown in Figure 3.  
Figure 3: \(Cont\).  
Figure 2: Cooling channel types. (**a**) Helical type; (**b**) paralleling type; (**c**) axial type; (**d**) trapezoidal type; (**e**) single-turn type for bearing; (**f**) paralleling type for bearing.  
Figure 3: _Cont._  
In this study, the cooling efficiencies of different cooling channel types of the motor and front bearing were investigated numerically to determine the suitable cooling channel types of the motor and front bearing in terms of the thermal deformation, temperature variation, and pressure drop of the cooling flow. The design of experiments method is performed to determine the optimal cooling channel dimensions of the determined channel type.
# IMM Filtering Algorithms for a Highly Maneuvering Fighter Aircraft: An Overview
## 6 EKF, UKF, and CKF
### Unified Algorithm of UKF and CKF
#### 6.2.2 Measurement Update  
**Step 1**: Perform a Cholesky decomposition on \(\mathbf{P}_{k|k-1}\),  
\[\mathbf{P}_{k|k-1}=\mathbf{S}_{k|k-1}\mathbf{\hat{S}}_{k|k-1}^{\prime} \tag{72}\]  
**Step 2**: Compute the cubature points,  
\[\mathbf{X}_{i,k|k-1}=\mathbf{S}_{k|k-1}\mathbf{\bar{\xi}}_{i}+\mathbf{\hat{x} }_{k|k-1},\;\;i=i_{0},...,m. \tag{73}\]  
**Step 3**: Compute the predicted measurement sample points,  
\[\mathbf{Z}_{i,k|k-1}=\mathbf{h}(\mathbf{X}_{i,k|k-1}),\;\;\;i=i_{0},...,m. \tag{74}\]  
**Step 4**: Compute the predicted measurement at time \(t_{k}\),  
\[\hat{\textbf{z}}_{k|k-1}=\sum_{i=i_{0}}^{m}w_{i}\textbf{Z}_{i,k|k-1}. \tag{75}\]  
**Step 5**: Compute the innovation covariance,  
\[\textbf{P}_{\textbf{zz},k}=\sum_{i=i_{0}}^{m}w_{i}\textbf{Z}_{i,k|k-1}\textbf{Z} _{i,k|k-1}^{\prime}-\textbf{z}_{k|k-1}\textbf{z}_{k|k-1}^{\prime}+\textbf{R}_{ k}. \tag{76}\]  
**Step 6**: Compute the cross-covariance matrix,  
\[\textbf{P}_{\textbf{xz},k}=\sum_{i=i_{0}}^{m}w_{i}\textbf{X}_{i,k|k-1}\textbf{ Z}_{i,k|k-1}^{\prime}-\hat{\textbf{x}}_{k|k-1}\textbf{z}_{k|k-1}^{\prime}. \tag{77}\]  
**Step 7**: Compute the updated state estimate and associated covariance at time \(t_{k}\) using (11)-(14).
## 2 Materials and Methods
### Determination of Agrochemical Residues  
Dimetheoate is a widely used organophosphate insecticide [23]. Glyphosate is a broad-spectrum systemic herbicide [24]. AMPA is a primary degradation product of glyphosate [25]. AMPA is considered with similar toxicological assets as glyphosate itself [26]. Ammonium glufosinate is also a broad-spectrum herbicide [27]. Dimetheoate is not recommended to treat _IP_ fields. However, it is used to control Psyllidae, its principal pest [28]. Conversely, there are no references of the direct use of glyphosate and ammonium glufosinate in _IP_ culture. However, the expansion of soybean culture across the Amazonia is causing close interactions with _IP_ fields. As a consequence, the presence of glyphosate, AMPA, and ammonium glufosinate in _IP_-based products must be investigated.  
Crystalline solid standards of dimetheoate (98.0%), glyphosate (97.0%), AMPA (98.0%), and ammonium glufosinate (97.5%) were obtained from Dr. Ehrenstorfer GmbH (Augsburg, Germany).  
The determination of dimetheoate was carried out using the QuEChERS extraction method followed by Liquid Chromatography Triple Quadrupole Mass Spectrometry(UHPLC-MS/MS) [29]. The UHPLC-MS/MS system has a detection limit of 10 \(\mathrm{\SIUnitSymbolMicro g}\)/kg for dimethoate quantification. The determinations of glyphosate, AMPA, and ammonium glufosinate were carried out by derivatization with 9-fluorenylmethylchloroformate (FMOC-Cl) (Merck, Darmstadt, Germany), followed by solid phase extraction cleaning (SPE) and UHPLC-MS/MS. The UHPLC-MS/MS system was composed of an Acquity UPLC\({}^{\text{\textregistered}}\) liquid chromatograph (Waters, Milford, MA, USA) coupled to a triple quadrupole mass spectrometer equipped with an ESI source able to operate in either the positive or negative-ion mode (TQD, Waters Micromass, UK). Chromatographic and mass spectrometry data handling were performed using MassLynx software v 4.1 (Waters, Manchester, UK). This system also has a detection limit of 10 \(\mathrm{\SIUnitSymbolMicro g}\)/kg for glyphosate, AMPA, and ammonium glufosinate quantification. Further details about the analytics can be found in [29]. All solvents used in the analytics were HPLC quality with gradient grade \(\geq\)99.9% (Cicarelli, Argentina).
# MC-EVM: A Movement-Compensated EVM Algorithm with Face Detection for Remote Pulse Monitoring
## 2 Related Works  
### Pulse and Other Human Bio-Signals Monitoring  
Human bio-signals monitoring is one of the most important research topics nowadays as it deals with human health status either on a real-time basis or in on an offline basis. It helps to provide solutions to facilitate doctors' tasks by providing reports on the health status of their patients in hospitals, in intensive care units, or even during their daily life: working, sleeping, performing sports, etc.  
These types of data can be collected using different types of sensors, either non-invasive sensors such as body temperature sensors, ECG single-channel/multi-channel sensors, pulse belts, and/or pulse meters, or minimal-invasive sensors such as blood glucose sensors or Continuous Glucose Monitoring devices (CGM). Therefore, telemedicine aims to create a unified solution where patients can share their measured data with their doctors in order to allow them to check their health status on a real-time basis.  
Many different types of research have been conducted in recent years in this field. In 2024, a pilot study by [PERSON] et al. [4] showed the usage of pulse rate monitoring for the assessment of a virtual reality (VR)-based aerobic exercise program. This research concludes that using pulse rate monitoring for the assessment of VR-based aerobic exercise is a promising approach for inducing physiological changes and is suitable to be used among a healthy population. Other research was published by [PERSON] et al. [5] in 2024 which highlighted the advancements in wearable triboelectric nanogenerators (TENGs) for the purpose of pulse waves monitoring. This research showed a solution that is suitable for contiuous pulse wave monitoring accurately which can be very useful to diagnose cardiovascular diseases.  
Furthermore, in 2018, research conducted by [PERSON] et al. [19] investigated the feasibility of using a smartphone camera to accurately measure pulse rate variability (PRV). The aim is to compare it with traditional electrocardiogram (ECG)-based methods and check whether there can be a food alternative to it. This study involved the usage of photoplethysmography (PPG) to detect changes to blood volume in the tissue in order to capture pulse data. In addition, [PERSON] et al. [20] introduced advanced methods to monitor cardiac pulse using the multi-scale spectrum analysis which aims to improve the accuracy of heart rate monitoring and other bio-signals. This approach discusses the potential of this method and its feasibility for both clinical and personal health status monitoring.  
[PERSON] et al. [21] investigated the impact of various factors on the accuracy of oscillometric blood pressure (BP) monitoring devices which are widely used due to their ease-of-use. This research highlighted the need for more considerations and validation for these devices in various health monitoring scenarios. Other research results for the use of EVM-based solutions are presented in Section 2.2.
## 3 Results  
Figure 2 presents the comprehensive workflow of the ProLinker-Generator framework, which integrates advanced deep learning techniques for the de novo design of PROTAC linker molecules. The framework comprises the following four core stages: pre-training, transfer learning (fine-tuning), RL optimization, and comprehensive evaluation. In the pre-training phase, a Transformer-based GPT model is trained on a large-scale public molecular dataset to capture general chemical patterns and structural features. This is followed by transfer learning, wherein the model is fine-tuned on PROTAC-DB--a domain-specific dataset focused on the PROTAC chemical space--to enhance its understanding of target-domain molecular construction rules. In the third stage, RL is introduced to guide the molecular generation process. Starting from the initial character \"C\", the fine-tuned model sequentially generates a complete SMILES string. The generated molecules are evaluated based on the following two criteria: (1) the syntactic validity of the SMILES representation and (2) compliance with predefined chemical or functional requirements, i.e., 1 < LogP < 3 and SMILES length less than 20. These scores are used as feedback to iteratively refine the model's generation strategy, enabling it to produce molecules that meet the desired design specifications. Finally, a comprehensive evaluation is performed on the model, the generated linker molecules, and the resulting PROTAC architectures, assessing the structural validity, chemical feasibility, functional potential, and quantitative performance metrics. During training, the model employs a self-supervised learning paradigm, predicting the next character in a SMILES sequence until an end-of-sequence token is generated.  
By integrating fine-tuning-based generation with reinforcement learning-based policy optimization, the model effectively learns the key structural motifs and property distributions characteristic of effective PROTAC linkers. The final output consists of synthetically viable linkers within the learned chemical space, demonstrating optimized performance while being supported by rigorous quantitative evaluation.
# Adaptive Power-Controlled Depth-Based Routing Protocol for Underwater Wireless Sensor Networks
## 3 The Proposed Protocol
### Detailed Design of APCDBRP
#### 3.3.1 Route Establishment Phase
Based on the information in the packet and the power level, they calculate the forwarding factor from their positions to the sink node and store it in their routing tables. Subsequently, each node updates the information in the BEACON packet and broadcasts it  
Figure 3: The structure of routing table. Figure 2: The structure of packets. at the maximum power. This process continues until the source node receives the BEACON packet. Nodes (i.e., N1 or N10) that do not receive the BEACON packet do not participate in the communication as they lack a suitable next-hop node. ```
1:\(depth_{node}\leftarrow\) depth in routing table of the node
2:\(depth_{packet}\leftarrow\) depth in packet
3:\(T_{send}\leftarrow\) the time of the last broadcasted beacon packet on the node
4:\(T_{min}\leftarrow\) the minimum interval time between consecutive beacon packet broadcasts
5:from the same node
6:procedure SendBeaconPacket
7: SetBeaconPacket()
8: broadcast packet
9:endprocedure
10:if node \(\in\) sink node then
11: SendBeaconPacket()
12:endif
13:
14:recv(packet)
15:procedureBeaconPacketIn
16:if\(depth_{packet}<depth_{node}\)then
17: drop(packet)
18:else
19: UpdataRoutingTable()
20:if\(nowTime-T_{send}\geq T_{min}\)then
21: SendBeaconPacket()
22:endif
23:endif
24:endprocedure
```  
**Algorithm 1**Route establishment procedure
# A LoRaWAN Architecture for Communications in Areas without Coverage: Design and Pilot Trials
###### Abstract  
This article proposes a system based on a long-distance communications system with low economic and energy costs that allows connectivity to be carried out independently from the existing cellular coverage in the area. In addition, it describes the design, development, implementation and analysis of an Internet of Things (IoT) architecture based on Long-Range Wide-Area Network (LoRaWAN). Moreover, the system has been deployed as a prototype, and the behavior and scope of the system have been analyzed in various real environments: urban, rural and natural. The results obtained from the analysis show that the system is suitable for working in areas without coverage such as mountains.  
LPWAN; LoRaWAN; long range (LoRa); uncovered; low-cost; communications +
Footnote †: journal: Electronics
# Multi-Level Credit Assignment for Cooperative Multi-Agent Reinforcement Learning
## 4 Method
### The Hierarchical Model  
In order to achieve the multi-level credit assignment, we propose a two-level hierarchical model for each agent first. Inspired by the feudal network, the hierarchical model consists of the top level (the manager) and the lower level (the worker) and can be represented as Figure 1[24]. The manager aims to learn high-level plans that are denoted as \(\alpha_{n}\). These high-level plans are used to guide the low-level worker to generate primitive actions based on \(\alpha_{n}\). Thus, the manager can focus on learning high-level decisions and the worker only needs to follow the guidance from the manager.  
As Figure 1 represented, the observation \(o_{t}^{n}\) is taken as the input of the hierarchical model. It first needs to be processed by the perception network \(f_{per}\), which can be represented as  
\[z_{t}=f_{per}(o_{t}^{n}), \tag{3}\]  
where \(z_{t}\) denotes the embedding results of the perception network. Then, \(z_{t}\) is sent to the manager and the worker, respectively. We first present the workflow of the manager. \(z_{t}\) is processed by the RNN of the manager first, which can be denoted as  
\[\
u_{t},m_{t}^{n}=f_{m}^{RNN}(z_{t},m_{t-1}^{n}), \tag{4}\]  
where \(m^{n}\) denotes the n-th agent's hidden states in the manager. Then, the intermediate result \(\
u_{t}\) is sent to the option network \(f^{option}\) to produce the embedding results \(\alpha^{n}\) of high-level policies, which can be represented as  
\[\alpha^{n}=f^{option}(\
u_{t}). \tag{5}\]  
To this end, high-level policies have been generated.  
Then, we present the worker inside the worker and the mechanism that how the high-level plans guide the low-level actions. \(z_{t}\) is processed by the RNN of the worker first, which can be denoted as  
\[\mu_{t},w_{t}^{n}=f_{w}^{RNN}(z_{t},w_{t-1}^{n}), \tag{6}\]  
where \(w^{n}\) denotes the n-th agent's hidden states in the worker.  
Then, intermediate result \(\mu_{t}\) is sent to the embedding network \(f^{U}\) to produce \(U^{n}\), which can be represented as  
\[U^{n}=f^{option}(\mu_{t}). \tag{7}\]  
In order to incorporate the high-level plans generated by the manager, the \(U^{n}\) is designed to combine with the \(\alpha^{n}\). To achieve this, \(\alpha^{n}\) is processed by the \(\phi\) first. These two process can be denoted as  
\[logits=U^{n}\cdot\phi(\alpha^{n}) \tag{8}\]To this end, the policy \(\pi\) can be obtained by  
\[\pi=Softmax(logits) \tag{9}\]  
It is worth noticing that the manager is supposed to generate high-level information. In this section, we introduce the hierarchical model but the details about the manager are not provided. Thus, we present the way we empower the manager with this ability, i.e., temporal abstraction.
## 2 Architecture and Operation of Proposed RO PUF
### Security Analysis  
#### 3.4.1 SVM Attack  
With the above-mentioned reconfigurability in Section 2, the proposed PUF is able to thwart the SVM modelling attack. In order to have a fair comparison with SVM attack on the 64-stage arbiter [11],  
Figure 8: (**a**) The measured average reliability of the proposed RO PUF versus the temperature variations; (**b**) the measured average reliability of the proposed RO PUF versus the voltage variations.  
Figure 9: The measured average power consumption per challenge-response pair (CRP) of the proposed PUF at different RO’s frequencies.  
we have extensively simulated a 64-stage proposed PUF using the UMC 65 nm CMOS technology to generate the enough training and testing CRPs. Figure 10 shows the prediction results for the proposed 64-bit RO PUF with and without reconfigurability using the tool \(SVM^{light}\)[19]. The reconfigurability is disabled by fixing the value of \(N_{clk}\) in the LFSR counter. The prediction accuracy is higher than 90% with only 1000 training CRPs for the RO PUF without reconfigurability. This is because, if the reconfigurable parameter \(N_{clk}\) is fixed, the relation of \(C\) and \(C^{\prime}\) is fixed. The model for the proposed PUF is similar to the arbiter PUF [11], which is very vulnerable to the SVM attack. However, when the reconfigurability is enabled by setting \(N_{clk}\) as a random number, our proposed PUF is proved to be more resilient to the SVM modeling attack, as the SVM predication accuracy for our proposed PUF response only fluctuates around 50% even with a large training set size of ten thousand CRPs.