esa-sceva/satcom-chunk-collection huggingface dataset:
# Time-Sensitive Networking Technologies for Industrial Automation in Wireless Communication Systems
## 1 Introduction
3 GPP and IEEE are standardizing and researching technologies for wireless TSN in 5 GS and WLAN respectively. However, we expect that 5 GS is used in conjunction with WLAN to implement wireless TSN. Therefore, we proposed an integration network architecture that can be used together with 5G new radio (NR) and WLAN, and then we derive necessary technologies for providing TSN service based on the proposed architecture. The paper is organized as follows. First, we introduce Ethernet-based TSN technologies and use cases in Section 2. Sections 3 and 4 introduce the research results of wireless TSN over 5G networks and 802.11-based WLAN. Then, we explain the integration model of 5 GS and WLAN for wireless TSN in Section 5. Finally, The conclusions are presented in Section 6. Figure 1: Network architecture for TSN.
## 6 Numerical Example
### The Effect of Parameters
#### 6.1.2 The Effect of q  
We take parameter \(q=3\) in the proposed numerical example and aggregate the data by utilizing the q-ROFAAWA and q-ROFAAWG AOs. If DMs can vary the value of parameter q, the ranking sequence also changes with the variation in q. We observed that the behavior of our proposed q-ROFAAWA and q-ROFAAWG AOs depends upon the parameter q for the interpretation of raking results. When we change the value of parameter q in the proposed q-ROFAAWA and q-ROFAAWG AOs, raking results are also affected by the changing of parameter q, which can be observed in Tables 8 and 9, respectively.  
Figure 3: The graphical representation of score function, variation by \(\mathcal{N}\), where the lines represent the raking of score value.  
Figure 2: The graphical representation of score value, variation by \(\mathcal{N}\).  
The ranking values of Table 8 are geometrically represented in Figure 4, as given below. Here, the range of score values describes vertically, and lines on horizontal lines show different values of the q.  
The geometrical illustration of the ranking order in Table 9 is presented in Figure 5. Here, lines on horizontal lines represent the values of the SF, and vertical lines denote the range of score values of \([-1,\ 1]\).  
Figure 4: The geometric representation of score value, variation by q. Lines on horizontal lines represent the raking of score values, and here vertical line shows the range of score values of \([-1,\ 1]\).  
\begin{table}
\begin{tabular}{c c} \hline \hline \(\mathbf{q}\) & **Ranking of Score Values of q-ROFAAWA** \\ \hline
3 & \(a_{1}>a_{2}>a_{3}>a_{5}>a_{4}\) \\ \hline
6 & \(a_{5}>a_{2}>a_{3}>a_{1}>a_{4}\) \\ \hline
9 & \(a_{5}>a_{2}>a_{1}>a_{3}>a_{4}\) \\ \hline
12 & \(a_{5}>a_{2}>a_{1}>a_{3}>a_{4}\) \\ \hline
15 & \(a_{5}>a_{2}>a_{1}>a_{4}>a_{3}\) \\ \hline
18 & \(a_{1}>a_{2}>a_{5}>a_{4}>a_{3}\) \\ \hline
36 & \(a_{1}>a_{2}>a_{5}>a_{3}>a_{4}\) \\ \hline \hline \end{tabular}
\end{table}
Table 8: Ranking of score values by changing q.  
\begin{table}
\begin{tabular}{c c} \hline \hline \(\mathbf{Q}\) & **Ranking of Score Values of q-ROFAAWG** \\ \hline
3 & \(a_{5}>a_{2}>a_{3}>a_{1}>a_{4}\) \\ \hline
6 & \(a_{5}>a_{2}>a_{3}>a_{1}>a_{4}\) \\ \hline
9 & \(a_{5}>a_{2}>a_{3}>a_{1}>a_{4}\) \\ \hline
12 & \(a_{5}>a_{3}>a_{1}>a_{2}>a_{4}\) \\ \hline
15 & \(a_{5}>a_{3}>a_{2}>a_{1}>a_{4}\) \\ \hline
18 & \(a_{5}>a_{2}>a_{3}>a_{1}>a_{4}\) \\ \hline
36 & \(a_{5}>a_{2}>a_{3}>a_{1}>a_{4}\) \\ \hline \hline \end{tabular}
\end{table}
Table 9: Ranking of score values by changing q.  
It is found that, in the sequence of the alternatives, when we take \(q=3\), we obtain the sequence of the listed five alternate values as follows \(a_{5}>a_{2}>a_{3}>a_{1}>a_{4}\). It is also a highly observable thing; when we take the parameter \(q=12\), then the ranking of alternatives also varies due to changes in \(q\); the variation in the sequence of alternatives is as follows \(a_{5}>a_{3}>a_{1}>a_{2}>a_{4}\). Therefore, we can confidently say that the variation in the parameter \(q\) also changes the sequence of alternatives.
## 4 Advanced SensorNC: In-Network Processing
### Data Scaling in Sink Trees  
With the aid of the scaling element, a much more comprehensive model of a WSN that integrates the processing besides the communication resources can be facilitated. Figure 5 illustrates the advanced SensorNC model.  
Besides the scaling elements, a multiplexing element is also introduced. The multiplexing element enables an explicit modeling of the aggregation of a set of flows, for instance arbitrary, FIFO or using strict priorities between flows. Thus, data flows arriving from predecessors in the sink tree are multiplexed and then scaled in order to transform them into local processing demand. The local sensing data are taken into account by a scaling element just before they are multiplexed with the other data flows. The overall aggregate is then processed by the sensor node modeled by a service curve \(\beta^{comp}\). As the amount of data that are forwarded downstream depends on how the processing is, for example, able to compress the data flow by, e.g., data aggregation, another scaling element translates the flow back to the required communication resources before it is served by the communication subsystem of the sensor node represented by another service curve element \(\beta^{comm}\).  
The challenge of this new advanced SensorNC model lies in the scaling elements that prevent an immediate end-to-end analysis following the PMOO principle. Yet, building on the theory from the previous subsection, we can shift all of the scaling elements upstream across service curve elements in order enable a true (PMOO) analysis again. The only remaining challenge is the shifting of scaling elements across multiplexers. In the following theorem, we prove a sufficient condition that allows this shifting without compromising the worst-case semantics.  
**Theorem 6**: _(Shift of the scaling element across the multiplexer) Assume a situation as depicted in Figure 6, i.e., two flows are multiplexed and then fed into a scaling function \(S\) with maximum scaling curve \(\overline{S}\) and minimum scaling curve \(\underline{S}\). Provided that the minimum scaling curve is super-additive and the maximum scaling curve is sub-additive, we can transform System (a) into System (b) without improving the worst-case scenario in System (b) over the one in System (a)._  
This can be found in [18].  
Consequently, all of the scaling elements can be moved to the sources of traffic. In principle, all of the input to the system is translated to the same resource units before entering a system of  
Figure 5: Advanced sensor network model.  
\"homogenized\" servers, such that we can apply the end-to-end concatenation-based techniques from Section 3.
## 4 Experimental Evaluation--RIOTA's Implementation and Cork as a UNO
### Results
#### 4.4.1 Discussion
In order to enhance the time performance, a preliminary work on a Content Based Image Retrieval (CBIR) for this system was already proposed [54]. In that work, the matching process takes 10 ms to perform 100 comparisons. In the aforementioned scenario, the time needed to perform 1,000,000 comparisons is 100 s, which is more acceptable. Despite the tremendous improvement on the time performance, once again, this improvement may not suffice in a real-world system. Other time improvements may include parallelization of the software application and/or reducing the image to hash codes [22; 55; 56]. Comparing with other state-of-the-art works, [PERSON] et al. [12] measured a time of 0.827 s to perform 1 vs. 1 comparison, while [PERSON] et al. [15] reported a time to perform 1 vs. 1 comparison (The authors state that: \"in the present case of 115 PCB parts, the identification process for one part takes 1.11 s\", so we are assuming that is the time needed to perform 115 comparisons (the identification process does not stop after finding a correct correspondence). In that case, the extraction time takes 680 ms and the time for 1 vs. 1 comparison is calculated as \(1000\times 0.42/150=3.7\) ms.) of 3.7 ms. From the performed analysis, it is clear that time is one of the limitations of the proposed approach and the trade-off between time and accuracy may also be considered. As such, a careful optimization to comply to real-world requirements should be conducted.
# A Roadmap for Gigabit to Terabit Optical Wireless Communications Receivers
## 4 Discussion
### Selecting between SiPMs for NIR  
Experiments have been performed with 405 nm light to limit the impact of ambient light from white LEDs; however, this choice of wavelength limited the eye-safe power limit [12]. Changing to 850 nm would increase the eye-safe power limit by a factor of approximately 50 and mean that existing high-bandwidth 850 nm transmitters could be used.  
The RB series of SiPMs have a higher PDE at 850 nm than the J series SiPMs. This would suggest that they are the better choice for operation with 850 nm transmitters; however, as shown in Table 3, they also have broader fast output pulses. The potential performance of a J30020 and arrays of nine RB10010 SiPMs and nine RB10020 SiPMs is shown in Figure 13. All of these systems have the same overall area. Consequently, their higher PDE means that the two systems made from RB series SiPMs support the lower data rates at lower irradiances than a J30020; however, at data rates of more than 1 Gbps the narrower output pulses of a J30020 mean that the performance of this SiPM is expected to be similar to the performance of the array of RB series SiPMs. Cost would then favor a J30020.  
Figure 12: A view of a J series 30035 SiPM. (**a**) shows the back of the SiPM, where different traces are highlighted different colors for visibility. Yellow is the cathode (connected to bias source), blue is the anode (connected to ground, or a series resistor to measure instantanous bias current). Red traces are the fast outputs. The fast output on this device is combined from six separate regions. (**b**) shows the top of the SiPM, where the through-silicon via processes connect to the traces on the rear. (Adapted from [28]).  
At 405 nm an eye-safe transmitter in a typical office would deliver 3 mWm\({}^{-2}\) at the edge of its coverage area [12]. Changing to 850 nm would increase this to approximately 150 mWm\({}^{-2}\). The results in Figure 13 suggest that, at this irradiance, the SiPMs would support approximately 2 Gbps. This is a little higher than the data rate, 1.4 Gbps, achieved using a 405 nm transmitter in the same scenario; however, the results with a 405 nm transmitter were obtained in 500 lux of ambient light using filters to protect a SiPM from ambient light. Despite the increase in eye-safe power, a change to 850 nm is therefore not expected to support significantly higher data rates.
# Securing Network Traffic Classification Models against Adversarial Examples Using Derived Variables
## 2 Related Work
### Common AE Generation Methods
#### 2.2.2 Project Gradient Descent (PGD) Attack  
[PERSON] et al. [38] proposed the PGD attack to improve the robustness of neural network models with adversarial training. This is an advanced iterative attack method used to evaluate the robustness of ML models against adversarial attacks. The PGD attack involves iteratively perturbing the input data in small steps in the direction of the gradient of the loss function with respect to the input while projecting the perturbed data back onto a restricted set of allowed values. The projection step ensures that the perturbed data remain within a bounded region of the input space, which is typically defined by the maximum permissible perturbation or range of the allowed input values. The PGD attack can be customized by varying the number of iterations, the step size, and the projection function. A higher number of iterations and smaller step sizes can result in stronger attacks but may also require more computation time. The projection function can also affect the effectiveness of an attack, with different projection functions leading to different levels of success in generating AEs. The PGD attack is formulated as shown in Equation (3).  
\[x_{t+1}=\Pi_{x+s}\big{(}x^{t}+\alpha sign(\bigtriangledown xL(\Theta,x,y)) \big{)} \tag{3}\]  
where \(\Pi(.)\) is the projection operator, \(t\) denotes the current step, \(\alpha\) represents the step size, and \(\epsilon\) is the magnitude of the perturbation. This attack is considered to be one of the strongest and most effective attacks in adversarial ML.
## 3 Analysis and Discussion
### Measurement and Monitoring of Soil Moisture Dynamics  
For the evaluation and monitoring of SM in semi-arid areas, various methodologies have been implemented, including satellite imagery, UAV-mounted cameras, and in situ measurements, or a combination thereof. The selection of an approach is primarily determined by the spatial and temporal scale, spectral resolution of the imagery, type of vegetation, and specific parameters being measured or evaluated. In this review, we analyze and discuss the most relevant SM measurement and monitoring approaches, identifying short- and medium-term research gaps and options in semi-arid areas of the western US.  
When selecting satellite imagery or UAV imagery for SM monitoring, we must consider the significant differences in spatial and temporal scales. Satellite platforms such as SMAP offer high temporal resolution (frequency of data collection), capturing data daily, which facilitates continuous monitoring of SM at regional and global scales. However, their spatial resolution (pixel dimension) is relatively low, approximately 9 km, limiting  
Figure 3: (**a**) Spatial representation of Land cover in western US, (**b**) principal rangeland in western US, year 2021 (Source: [PERSON], 2023) [40].  
their effectiveness in detailed site-specific studies where small-scale soil and vegetation heterogeneity is critical.  
In contrast, UAV imagery provides much finer spatial resolution, often reaching centimeter scales, making it ideal for detailed SM studies in small areas, capturing the impact of microtopography, vegetation, and soil structure. However, UAVs have more restricted temporal coverage due to operational constraints such as site accessibility and weather conditions. The choice between using satellite or UAV imagery for SM monitoring should be guided by the study's specific spatial and temporal requirements, with satellites being preferred for continuous large-scale monitoring, and UAVs for detailed, high-resolution analyses in specific locations.
## 3 Vibration Analysis and Experiment at Operating Speed Range  
To calculate the acceleration response induced by the electromagnetic force of the BLDC motor, an electromagnetic-structural vibration coupled analysis is conducted. This method is one of the methods used for analyzing the effect of the electromagnetic force on the structure. It is a one-way coupling between the electromagnetic domain and the structural domain. Over the operating speed range, the distribution of the magnetic field density of the motor air gap is computed using 2-D electromagnetic FE analysis. The calculated magnetic flux density is converted to the force on the surface of the stator teeth by using the Maxwell stress tensor theory. Modal transient analysis is performed by applying the transformed force to the 3-D entire FE model validated previously. To analyze the results on the frequency domain, the results are transformed through FFT. The results in the frequency domain are compared with the frequency response results of the experiment. The vibration characteristics of the BLDC motor are evaluated over  
Figure 6: Results of the modal test (left) and modal analysis (right). (**a**) 1 st mode; (**b**) 2 nd mode; (**c**) 3 rd mode; (**d**) 4 th mode.  
the operating speed range by using the analysis results. Figure 7 shows the procedure for predicting the electromagnetic force induced vibration characteristics of the variable-speed BLDC motor.  
The effect of PWM frequency is ignored in this study because, in general, PWM frequency is a band of high frequency. Therefore, only the fundamental frequency is considered for sine input current.
## 2 Theoretical Investigation of Monitoring Models  
### Monitoring Principles  
The sensing structure consists primarily of a Hall probe, a rectangular permanent magnet, and an epoxy resin mold, which are tightly bonded to the steel wire through an epoxy adhesive, as shown in Figure 1. The Hall probe is placed above the rectangular permanent magnet, aligning with its axis POB, thereby enabling the measurement of the z-component of the magnetic induction density \(B_{x}\) of the magnet.  
In the absence of external force on the steel strands, as shown in Figure 1, the probe's measurement point coincides with the midpoint O of the central axis. The midpoint of the permanent magnet is positioned at a distance \(l\) from the midpoint O' of the Hall probe's epoxy resin base. When tensile force is applied, the steel strands deform, resulting in the displacement of both the permanent magnet and the probe, denoted as \(\Delta x_{1}\) and \(\Delta x_{2}\), respectively. Due to variations in bonding positions and lengths, a relative displacement differential \(\Delta x=\Delta x_{1}-\Delta x_{2}\) arises between the permanent magnet and the probe. Consequently, on the permanent magnet, the probe's measurement point shifts from point O to point M, with a displacement of \(\Delta x\). Since the magnetic induction density varies across different points in the space of the rectangular permanent magnet, the magnetic induction density \(\Delta B_{x}\) measured by the probe also changes correspondingly, denoted as \(\Delta B_{x}=\Delta B_{x1}\)\(-\)\(\Delta B_{x2}\). Thus, within the sensor's measurement range, different tensile forces correspond to varying \(B_{x}\) values. By analyzing the relationship between the tensile force and \(B_{x}\) and establishing a mathematical model between these two parameters, the tensile force in the steel strands can be monitored.
# Aspect-Based Sentiment Analysis Using Aspect Map
## 3 Sentiment Analysis Using Aspect Map  
Aspect-based sentiment analysis (ABSA) is a task of making a sentiment decision \(s\) for a given text \(\mathbf{t}\) and a target aspect \(c\). For instance, if the very short review text in Figure 1 is considered as \(\mathbf{t}\), it contains two aspects of _LOCATION_ and _FOOD_. Thus, two input tuples should be considered for ABSA as in Figure 2. If the tuple \((\mathbf{t},\text{LOCATION})\) is given to an ABSA system, then the system should output **positive** as a sentiment label, while **negative** is the right decision when the tuple is \((\mathbf{t},\text{FOOD})\). In this paper, we divide the ABSA task into two pipelined tasks. That is, after an aspect map for an input tuple is extracted, sentiment classification is performed for the input tuple with the extracted aspect map.  
Figure 3 describes the overall architecture of the proposed model, which consists of two CNNs. The first CNN is aspect-map extraction network (AMEN). The goal of AMEN is to identify which parts of a text \(\mathbf{t}\) are actually related with a target aspect \(c\). Thus, the result of AMEN is an aspect map \(\mathcal{A}^{c}\) for the target aspect \(c\) that is extracted from \(\mathbf{t}\). That is, AMEN takes both \(\mathbf{t}\) and \(c\) as its input, and returns \(\mathcal{A}^{c}\) as its output. The aspect map \(\mathcal{A}^{c}\in\mathbb{R}^{N}\) indicates the relevances of all words in \(\mathbf{t}\) to the aspect \(c\), where \(N\) is the length of \(\mathbf{t}\). Then, the words with a high relevance become an aspect expression for \(c\). For instance, the words \"_sushi_\" and \"_rolls_\" are extracted as an aspect expression for _FOOD_ in Figure 3 by applying the aspect map \(\mathcal{A}^{FOOD}\) to \(\mathbf{t}\).  
The other CNN is aspect map-based sentiment analysis network (AMSAN), which classifies the sentiment polarity of the text \(\mathbf{t}\) for the target aspect \(c\). Because AMSAN has to know what is the aspect expression for \(c\) in \(\mathbf{t}\) for correct classification, it takes, as its input, the aspect map \(\mathcal{A}^{c}\) as well as \(\mathbf{t}\) and \(c\). The aspect map \(\mathcal{A}^{c}\) is a type of positional filter that leaves a sentiment expression appearing near the aspect expression for \(c\). For example, in Figure 3, the word \"_bad_\" survives and affects the process of sentiment classification, while the word \"_nice_\" is excluded by \(\mathcal{A}^{c}\) because it is far from the aspect expression \"_sushi rolls.\"  
Figure 2: The description of the ABSA task with two different tuples from a single review text.
# Investigating Master-Slave Architecture for Underwater Wireless Sensor Network
## 2 Related Work
### Localization-Based Routing Protocols
It performs the selection of the forwarder similar to the technique of VBF but with the addition of network density consideration. However, eventually, this also faces the same issue of energy holes and void holes such as the VBF in the sparse network due to the death of the forwarder nodes. * MEES: In this protocol, the two mobile sinks are chosen which are far away from each other [11]. Data collection from the nodes is performed via predefined linear paths. The experimental results demonstrate the improvement of network throughput and lifetime as well as energy consumption. * DTMR: To reduce energy consumption in terms of signaling power and transmission delay, the authors in [12] proposed the Direction Transmission or Mobile Relay (DTMR) protocol where a source node can send data through a mobile relay node or directly to the destination node; however, there is no guarantee for the reliable communication of data to the destination because of the harsh underwater environment. * FVBF: The Fuzzy logic-based Vector-based Forwarding protocol (FVBF) [13] is another attempt to improve VBF. It considers the valid distance, projection, and energy of a node for the selection of the next forwarder. Valid distance determines the closeness of a node to sink while projection determines its closeness to the routing pipe.
# Review and Evaluation of MAC Protocols for Satellite IoT Systems Using Nanoatellites
## 4 Background on MAC Protocols  
MAC layer groups a set of protocols and mechanisms in order to distribute the resources for the nodes to make an effective (and efficient) use of the communications channel. The resources are typically distributed in terms of time assignment, frequency assignment or code assignment. In the particular case of broadcast links, a MAC protocol is in charge of coordinating the frame transmission.  
Each MAC protocol is designed to cover different requirements, and its performance can be quantified with different metrics. In some cases, the priority is set to the performance concerning data transmission rate, for which the normalized offered load and the normalized throughput are measured. Other priorities may include measurements of delays in sending data or the packet loss ratio (PLR). In the particular case of IoT applications, there may be limitations regarding processing capabilities, available storage, hardware complexity, and energy consumption.  
In this section, the authors provide a set of metrics that can quantify the fulfillment of the different requirements objectively. We also present the traditional categorization employed to classify the existent MAC protocols for broadcast channels.