esa-sceva/satcom-chunk-collection huggingface dataset:
# Classification Performance of Thresholding Methods in the Mahalanobis-Taguchi System
## 2 The Concept of Mahalanobis Distance (MD)
### Box-Cox Transformation  
The distribution of MD values for all samples contributed to the construction of the MTS classifier does not generally follow a normal distribution. They are always skewed to the left and to the right of the MDs distribution plot since normal and abnormal samples are treated as different sample populations. Ref. [18] attempted to transformed the non-normal distribution of MDs into a normally distributed MDs distribution using Box-Cox transformation procedures. The motivation of their work comes from their intention to adopt a Control Chart Limit concept to determine the optimal MDT value. In a control limit procedure, the mean (\(\upmu\)) and standard deviation (\(\sigma\)) of the sample population in a normal distribution could be easily determined. Thus, all samples (in MD terms) are transformed using Box-Cox transformation which is defined in Equation (19)-(21) as follows:  
\[\begin{array}{c}MD_{i}(\lambda)=\frac{1}{\lambda}\ \left(MD_{i}^{\lambda}-1 \right),\ \lambda\ \
eq 0\ ;i=1,2,3,\ldots,n\\ MD_{i}(\lambda)=\ln(MD_{i})\lambda=0\ ;i=1,2,3,\ldots,\ n\end{array} \tag{19}\]  
Figure 6: Example of minimizing the distance from maximum theoretical threshold value of point A to any point on the classifier’s ROC.  
where \(M\!D_{i}\) is the MD of the \(i\)th sample, \(M\!D_{i}(\lambda)\) is the transformed MD value. The value of \(\lambda\) is obtained, such that it maximizes the logarithm of the likelihood function in Equation (20) as the following:  
\[\begin{split}\underset{\lambda}{Max}f(MD,\lambda)=-\frac{n}{2} \ln\biggl{[}\frac{1}{n}\bigl{(}(M\!D_{i}(\lambda)-\overline{MD}(\lambda) \bigr{)}^{2}\biggr{]}+(\lambda-1)\ \sum\limits_{i=1}^{n}\ln(M\!D_{i})\end{split} \tag{20}\]  
where, \(\overline{MD}(\lambda)=\frac{1}{n}\ \sum\limits_{i=1}^{n}(M\!D_{i}( \lambda))\) and \(n\) is the total number of samples (both normal and abnormal).  
And thus, to obtain the optimal threshold value \((\tau_{x})\) of the transformed samples, one has to minimize the following error (\(\varepsilon\)) function according to Equation (21) below:  
\[\varepsilon(\tau)=\frac{e_{1}}{n_{h}}+\frac{e_{2}}{n_{u}} \tag{21}\]  
where \(\tau\) is the threshold value (in Box-Cox transformed term), \(e_{1}\) is the number of samples classified as unhealthy (abnormal) which in fact they were healthy (normal), \(n_{h}\) is the total number of healthy (normal) samples, \(e_{2}\) is the number of samples classified as healthy (normal) which in fact they were unhealthy (abnormal) while \(n_{u}\) is the total unhealthy (abnormal) samples in the dataset.  
Since \(\tau\) threshold value is in the form of a transformed Box-Cox term, to convert the transformed threshold value into a non-transformed MD\({}_{\text{T}}\) form, Equation (19) is deployed by rearranging the equation into MD term by incorporating \(\lambda\) value which was obtained previously and accordingly.
# Joint Iterative Satellite Pose Estimation and Particle Swarm Optimization
## 5 Conclusions
With respect to the Soyuz_hard pretrained weight, PE-PSO achieves 7.8% and 0.3% better mean position estimation error and mean orientation estimation error reductions, respectively, than the conventional does. The PE-PSO process is based on the weight and weight damping ratio, which are variables of the PSO algorithm. Therefore, it is important to select proper values for these parameters.
## 2 Methods and Materials
### Channel Model  
Urban environments are characterized by the presence of various obstacles that deteriorate the performance of V2V communications, especially for vehicles equipped with isotropic antennas in intersection scenarios. To solve this problem, beamforming could be applied to adapt the beam shapes to the context of a specific surrounding environment.  
Different algorithms are used to adapt the beam shapes to the surrounding environment. The algorithms are trained through simulations of urban environments. A channel model for urban V2V communications is necessary for the simulations, since this type of environment has many peculiarities that differentiate it from other communication environments. Typically, the channel model presented in [30] is used for urban V2V communications, but [31] presented an improved version of the channel model, which was adopted in this work to estimate the channel realizations. The following equations describe the channel model used for the simulations,  
\[G(d_{t},d_{r})_{AB}=10\log_{10}(m^{2})+10\log_{10}\biggl{(}\underbrace{\left(g_ {1}\frac{\lambda}{4\pi(d_{t}+d_{r})}\right)^{2}}_{\text{single reflections}}+ \Bigl{(}g_{2}^{N}\frac{\lambda}{4\pi(d_{t}+d_{r})}\Bigr{)}^{2}\biggr{)}+\Psi_{ \sigma}, \tag{1}\]  
where:  
\[N=\max\biggl{[}2\sqrt{\frac{d_{t}d_{r}}{w_{t}w_{r}}}-1,0\biggr{]}, \tag{2}\]  
\(d_{t}\) and \(d_{r}\) are the distances to the intersection center as shown in Figure 1, \(m\) is an offset present due to the differences in antenna height, and \(\lambda\) is the wavelength for the transmitted signal. Besides, there are single-order and high-order reflections that contribute to the received power. These reflections are represented by \(g_{1}\) and \(g_{2}\), where \(g_{1}\) is the mean effective amplitude gain from single-reflection interactions and \(g_{2}\) is the mean effective amplitude gain from multiple-reflection interactions. \(w_{t}\) and \(w_{r}\) are the road width for the transmitting and receiving vehicles, respectively; and \(\Psi_{\sigma}\) is needed to model the large-scale fading as multiple Gaussian processes, with parameter \(\sigma\), for each communication link and iteration. Finally, the values \(g_{1},g_{2}\), \(m\), and \(\sigma\) were estimated from the measurements in [31].
# Study on Components Determination and Performance Evaluation of LS Pre-Maintenance Agent
## 5 Road Performance Test Results Analysis  
### Penetration  
The UV radiation spread with aging asphalt molecules into a 1 mm depth. Because of the existing voids, the depth of radiation ultimately reaches the maximum individual particle mix size, which is about 10 mm. Therefore, we use 10 mm as the standard penetration depth. The average penetration depth results at 0.5-24 h are shown in Figure 8.  
In 0.5 kg/m\({}^{2}\), the surface groove of the specimen was fully filled, a bubble came out from a larger gap that the agent was quickly penetrating and the average penetration depth in 5-7% porosity was over 10 mm after 2 h. It kept growing and reached 15-20 mm after 24 h. In 1 kg/m\({}^{2}\), the average penetration depth reached 10 mm after 2 h, and 15-30 mm after 24 h. We can see that both dosages can completely penetrate the aged asphalt layer. The permeation depth in 2 h and 8 h accounted for about 50% and 75% of the whole 24 h results. The first 2 h was the main infiltration period. By chosing 1 kg/m\({}^{2}\) a faster and deeper penetration effect can be obtained, but it requires higher maintenance costs. In summary, the pavement should be closed at least 2 h after spraying the LS pre-maintenance agent, a conditional section can be extended to 8 h and above. 0.5 kg/m\({}^{2}\) can ensures sufficient penetration depth and effect while controlling the cost.
## 5 Deployment Independence in Action: Aggregate Computing over DingNet
### LoRaWAN  
We here briefly introduce LoRaWAN and its software stack. LoRaWAN is a communication system designed for long-range communication (up to kilometers) and low energy requirements, at the price of data rates in the order of few bytes per second (six or more orders of magnitude less than Wi-Fi or LTE). The foundational elements of LoRaWANs are end devices (or motes), gateways, and LoRa servers. Motes communicate with gateways, forming a star topology. Gateways, in turn, communicate with a LoRa server, building an overall star of stars topology. In such a network, each mote willing to communicate broadcasts its message to all nearby gateways through single hub wireless links. All the gateways receiving the packet forward it to the server through any available networking backhaul (the protocol does not govern how gateways server communicate). When communicating from servers to motes, the server selects the gateway that deems best to deliver data to the recipient. LoRa motes are designed for constrained-energy scenarios--typical industrial implementations provide battery life of 1-3 years. Consequently, motes provide little computation capacity and memory, and hence represent an exemplar case of _thin_ devices.  
Figure 5: A self-healing channel in Protelis.
## 3 Proposed Protocol Architecture
### Addressing Scheme
#### 3.5.3 Inter-SMAG Communication under Different Border Router  
In this case the global unicast address will be used to communicate with the corresponding node. If the IP-WSN node moves from one SMAG domain under one border router to another SMAG domain under another border router then it will get a care of address for the time being for the immediate  
Figure 5: Intra-SMAG Communication.  
Figure 6: Addressing format to communicate among SMAGs under same border router.  
Figure 7: Inter-SMAG communication under same border router.  
connectivity to the corresponding node. After the discovery of the home border router address new location of the IP-WSN will be reintroduced and further communication will be performed by the addressed formed by the EUI-64 address identifier and foreign router prefix. The scenario is depicted in Figure 8.  
In Figures 9 and 10, we demonstrate the addressing format to communicate from IP-WSN to CN (Figure 9) and CN to IP-WSN, as shown in the Figure 10.
# Feasibility of LoRa for Smart Home Indoor Localization
## 2 Literature Review
### ZigBee  
ZigBee is an emerging technology built based on the IEEE 802.15.4 standard. The ZigBee is often used in creating low cost, low data rate, and energy-efficient personal area network [24].  
Some researchers exploited the ZigBee system as the localization technique and tested its applicability in the smart home environment. The performance of ZigBee was evaluated coarsely, room level space used the RSSI proximity-based algorithm. The results indicated the ZigBee have high sensitivity and great energy efficiency of localization. However, this study did not illustrate the values of positioning error [60]. [PERSON] et al. [61] used the weight centroid localization (WCL) algorithm to improve the calculated position of ZigBee in real implementation. And the averaged localization error is 2.6 meters (m) in a grid aligned sensor network, but the beacon distance is only 10 m. Another research group revealed the positioning error of sensor-to-target distance estimation of ZigBee is 1.5-2 m, but this value was collected in the LOS scenario with a high density of sensor nodes (0.27 nodes/m\({}^{2}\)) [43]. These data demonstrated the defects of ZigBee in the localization field. Since the ZigBee is a short-range communication system, only the vast array of sensors and nodes maintain a satisfactory accuracy level.
## 4 Implementation Details
### Interference Detector and SNR\({}_{fe}\) Estimator (IDSE)
#### 4.1.2 SNR Estimation  
Similar to power measurement of interferences, power measured in desired signal channel is  
\[\hat{P}_{sig} = \frac{1}{g^{2}}\sum_{n}^{N}Y_{sig}(n) \tag{8}\] \[\text{where, \ }Y_{sig}(n) = Y_{Isig}^{2}(n)+Y_{Qsig}^{2}(n) \tag{9}\]  
Figure 10: Performance of Interference detector for NF\({}_{FE}\) = \(29\) dB. Minimum \(P_{sig}\) = \(-85\) dBm. NF is calculated for minimum \(P_{sig}\). As figure shows, large desired signal power hinders accurate interference detection. But as evident from Table 1, accurate interference detection is needed until \(P_{sig}\) is \(20\) dB above minimum. \(P_{sig}\) of \(0\) dB corresponds to \(-6\) dB SNR\({}_{FE}\).  
If \(Y_{Isig}\) and \(Y_{Qsig}\) are given by \(x[n]+w[n]\), where \(w[n]\) is AWGN, then  
\[\hat{P}_{sig} = \frac{2}{g^{2}}\sum_{n}^{N}g^{2}(x[n]+w[n])^{2} = 2\sum_{n}^{N}(x^{2}[n]+w^{2}[n]) \tag{10}\]  
Since signal and noise are uncorrelated, \(E(x\,w)=0\),  
\[\widehat{\text{SNR}}_{FE}=\frac{\sum_{n}^{N}x^{2}[n]}{ \sum_{n}^{N}w^{2}[n]}=\frac{\hat{P}_{sig}}{2N \sigma^{2}}-1 \tag{11}\]  
Thus to measure \(\hat{P}_{sig}\), Equations (8) and (9) can be used. Front end of the receiver is designed for a constant noise figure. Thus the worst case variance of noise \((\sigma^{2})\) contributed by the front end is known. Hence, SNR can be estimated using Equation (11). \(\text{SNR}_{FE}\) estimator is ON for one symbol duration.
## 3 Results
### Race for Parameters  
As mentioned earlier, the scale of an LLM depends on the size of the ingested datasets, the amount of training compute, and the number of parameters supported. However, since  
Figure 7: Language of references generated by AI chatbots to an English language prompt.  
developers never fully disclose these data, we follow other studies [9; 110; 111; 9] and compare the number of parameters as a proxy to gauge the scale of the LLM (Figure 8).  
It was not the intention of this article to gain insights into the efficiency of LLMs, but as a side note, the current trend of upsizing the LLMs, sometimes referred to as an AI Arms Race, can be addressed. Currently, numerous well-funded start-ups, including Anthropic, AI21, Coherence, and Character.AI, are putting enormous resources into developing ever larger algorithms, and the number of LLM parameters exceeds the exponential growth trend line. However, it is expected that the returns on scaling up the LLM size will diminish [13].  
In our data, we observed the impact of the exponential growth of the LLM on the content generated. ChatGPT-4 is approximately 10 times larger than ChatGPT-3.5. LaMDA has a similar number of parameters as ChatGPT-3.5, 137 and 175 billion, respectively, but the AI chatbot tested, Bard, only uses the \"lightweight\" version of LaMDA. Assuming that the \"lightweight\" version uses only half the parameters, the size ratio between LaMDA (Bard), ChatGPT-3.5, and ChatGPT-4 is 1:2:20.  
The up to 10% improvement in the generated content of ChatGPT-4 compared to ChatGPT-3.5 is not negligible, but for many use cases, it might not be noticeable. Given the tenfold increase in the number of parameters, this is not particularly impressive. Especially since the 10% improvement is due not only to the new LLM but also to continuous reinforcement learning from human feedback. Nevertheless, the improvement of ChatGPT-4 over Bard in reducing incorrect content is significant. One can surmise that the twenty-fold increase in parameters from Bard to ChatGPT-4 is significant enough to be noticeable in daily use, while the tenfold increase from ChatGPT-3.5 to ChatGPT-4 is mostly only observable in a controlled test environment.  
Given that improvements are diminishing and that continuous exponential growth of LLMs is physically impossible with current technology, we can only conclude that the growth of LLMs has reached a plateau. This observation is consistent with the sparse information coming out of the industry: \"...we're at the end of the era where it's going to be these, like, giant, giant models... We'll make them better in other ways\". ([PERSON], CEO of OpenAI, at an event at MIT on 14 April 2023).  
Figure 8: The race for parameters. The increase in the number of parameters (red dotted line; adapted after [110] and updated with sources cited in Section 2.1) exceeds the exponential trend (orange line); the in-context learning as detected by our test (blue columns; see Section 3.1) only improves with a linear trend (blue line).
## 2 Design Principles  
As visible in Figures 1 and 2, the design of the proposed class of switches relies on a basic arrangement, characterized by uniform meanders, which is varied in order to achieve increasingly low pull-in voltages. Starting from the layout represented in Figure 1a, it is possible to identify several crucial layers, namely the first layer of the fabrication process, the polycrystalline silicon, which composes the central buried electrodes and the lateral decoupling resistors. While the former, deployed under the drilled areas of the membrane, are meant to cause the collapse of the membrane, the latter avoid the flow of the RF signal towards the ground planes when the membrane is actuated. It is also possible to identify square pillars under the drilled areas of the membrane, which are intended to prevent the complete collapse of the membrane onto the underlying layer of silicon oxide, and the chance of stiction (missed release of the membrane when the bias of the electrodes is removed) in the case of charges trapped within the oxide. The pillars are composed by layers of polycrystalline silicon, multi-metal based on aluminum, and evaporated gold. The multi-metal layer is also employed to deploy the central and interrupted RF signal line, whose terminals are covered by rectangular contact pads of evaporated gold. Such areas will be the surfaces that make contact with the collapsed membrane, which is made by two layers of electroplated gold. While the first layer represents the actual structure of the central membrane, the second thicker layer is present just over the surfaces, which are meant to be stiffer (anchors, borders of the movable membrane). Additional details regarding the fabrication process at Fondazione Bruno Kessler (FBK) facilities can be found in [25].  
Figure 1: (**a**) Complete layout of _Dev1_ switch, characterized by uniform meanders. (**b**) Detailed representation of _Dev1_ switch, including its main dimensional parameters.  
As noticeable in the 3D model of Figure 1b, all of the layers below the one of evaporated gold are buried under silicon oxide (except the pillars). The footprint of the whole membrane is 780 \(\times\) 270 \(\upmu\)m\({}^{2}\), featuring a central rectangular plate of 380 \(\times\) 170 \(\upmu\)m\({}^{2}\) and beams shaped by uniform meanders of 50 \(\upmu\)m by 25 \(\upmu\)m (\(a\) and \(b\) in Figure 1b) and characterized by a width of 10 \(\upmu\)m. The second device, partially illustrated in Figure 2a, is marked by non-uniform meandered beams, obtained by doubling the length of each terminal meander. In the third device, whose beam is depicted in Figure 2b, the length of the terminal meander increased by up to four times its initial length, and the lateral dimension of the meanders slightly increased (\(h\) in Figure 2b) to maintain a minimum distance of 10 \(\upmu\)m between the contact pads and the beams. All of the parameters reported in the abovementioned figures are specified in Table 1.
# Enhancing Recognition of Human-Object Interaction from Visual Data Using Egocentric Wearable Camera
## 1 Introduction
Our idea was to leverage the hand joint information from images and videos generated from a wearable egocentric camera for the interpretation of HOI. Such an approach can address the issues related to the texture-, color-, and shape-based features of existing HOIR approaches, while also keeping the system computationally efficient. This paper presents \"HP4 HOIR\", which is a novel framework that uses 3D hand pose data from a wearable egocentric camera for HOIR. The proposed framework emphasizes the dynamics of hand movements for understanding and interpreting interactions between humans and objects. In this work, we used two approaches, i.e., object-dependent interaction recognition (ODIR) and object-independent interaction recognition (OIIR), for HOIR. The first approach, i.e., ODIR, starts by identifying the object held by the human being before determining the nature of the interaction in the next stage. On the other side, the OIIR approach simplifies the process by directly recognizing the HOI without prior identification of the object. We present the detailed results and analysis for both these approaches in the results section (i.e., Section 4). The results show that the proposed framework for HOIR produced effective results while keeping in view the trade-off between accuracy and computational complexity. Thus, the proposed framework paves the way for future innovations in interactive systems and assistive technologies that involve human-robot interactions. It is also beneficial for privacy-sensitive applications, as it offers more privacy by neglecting other information. List of ContributionsTo the best of our knowledge, it is for the first time that the hand pose information, i.e., hand joints, is explicitly used for HOIR in any study. With this, the major contributions of this paper are as follows:  
* We propose a novel framework \"HP4 HOIR\" that leverages hand pose information from an egocentric view for the recognition of HOIs. * We propose a feature extraction and description method for the hand pose data, which captures unique attributes from the hand joints data and represents them as a feature vector for HOIR. * We conducted a detailed analysis of ODIR and OIIR, which provided insights into the accuracy and computational cost of each approach in recognizing HOI.
## 2 Development of FBG Stress Sensor
### Sensor Manufacturing  
The manufacturing materials of each component need to be selected by comparison to ensure that the sensor performance can meet the expected requirements. The final manufacturing materials of sensor components are shown in Table 1, which mainly include stainless steel 304, stainless steel 630 (heat treatment), aluminum alloy 2A12, and polyformaldehyde (POM) plastics. Stainless steel 304 is the most widely used chromium-nickel stainless steel, with high anti-corrosion properties, heat resistance, and processing performance. It is suitable for processing the hemispherical shell, coupler box, connecting rod, and the sealed cover, all of which have requirements for high corrosion resistance, despite having low mechanical performance. Stainless steel 630 is martensitic precipitation-hardened stainless steel, with high strength, hardness, and corrosion resistance. It is suitable for processing a sensing unit that has high corrosion resistance and strength. Aluminum alloy 2A12 is strong, and is approximately one third of the density of stainless steel. It is suitable for machining large volume parts, such as solid spheres, which have to meet lightweight design requirements. POM plastic has the characteristics of high rigidity and wear resistance, and is widely used in the production of internal instruments. Therefore, it was chosen as the processing material of the wire plate. In fact, the selection of sensor materials requires the comprehensive consideration of performance, reliability, cost, and other factors, which need to be constantly optimized and improved in applications. For example, if the solid sphere initially uses 304 stainless steel, the sensor will be \"top-heavy\" due to its high density, which is prone to cause the bending of the connecting rod and deformation of the screw joint. Therefore, an aluminum alloy with a lower density was selected to reduce the weight of the sensing head.  
According to the sensor design and materials, the sensor parts were processed. The assembly process and final appearance of sensor parts are shown in Figure 12.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Material Type** & **Part Name** \\ \hline Stainless steel (304) & Hemispherical shell, connecting rod, coupler box, \\  & sealed cover \\ Aluminum alloy (2A12) & Solid sphere \\ Stainless steel (630, heat treatment) & Sensing unit \\ POM plastic & Optical fiber plate \\ \hline \hline \end{tabular}
\end{table}
Table 1: Manufacturing material of sensor parts.  
Figure 11: Design of measuring direction of sensor head: a, connector; b, connecting rod; c, coupler box; d, optical fiber plate; e, outlet hole; f, connecting hole.