esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Methodology  
This section outlines the methodology employed in this work to evaluate the performance of various data detection techniques for wireless communication systems. The focus is on comparing the effectiveness of the conventional methods (MLE, MMSE, and ZF) with a DNN-based detector for SISO systems, as well as the one designed specifically for MIMO systems.  
The primary objective of presenting and comparing SISO and MIMO frameworks, despite MIMO's known superior performance, was to thoroughly understand the foundational differences and identify specific scenarios where SISO might still be relevant or provide insights for simpler systems. Additionally, this comparison aids in highlighting the incremental benefits and challenges posed by transitioning from SISO to MIMO. This comprehensive understanding is crucial for designing more efficient and adaptive DNN-based detectors that can be seamlessly integrated into existing and future communication systems.
# A Novel Bitrate Adaptation Method for Heterogeneous Wireless Body Area Networks
## 4 Heterogeneous WBAN Simulator
### The Results of Measurements Used in the Simulator
#### 4.2.5 Link Quality Indicator  
The LQI parameter, defined in the IEEE 802.15.4 standard, aims at a parametric evaluation of the power and/or quality of the received packet. From the current research, it appears that this parameter is very often coupled with the RSSI, and there is an exponential relationship between them [46].  
The information about the LQI parameter, time synchronized with the RSSI value, was obtained from the NB interface. Therefore, an exponential regression function was fitted:  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**MN LOCATION** & **SCENARIO** & **LOS/NLOS Conditions** & \(P(xLOS)\) & \(P(xLOS^{\prime})\) \\ \hline \multirow{4}{*}{\(\mathrm{HE_{R}}\)} & \multirow{4}{*}{APR} & LOS & 0.89 & 0.98 \\  & & NLOS & 0.95 & 0.97 \\ \cline{2-5}  & & & LOS & 0.89 & 0.98 \\  & & NLOS & 0.95 & 0.97 \\ \hline \multirow{4}{*}{\(\mathrm{TO_{F}}\)} & \multirow{4}{*}{APR} & LOS & 0.88 & 0.99 \\  & & NLOS & 0.95 & 0.97 \\ \cline{2-5}  & & LOS & 0.83 & 0.99 \\  & & NLOS & 0.97 & 0.97 \\ \hline \multirow{4}{*}{\(\mathrm{AB_{L}}\)} & \multirow{4}{*}{APR} & LOS & 0.91 & 0.98 \\  & & NLOS & 0.93 & 0.97 \\ \cline{1-1} \cline{2-5}  & & LOS & 0.88 & 0.98 \\ \cline{1-1} \cline{2-5}  & & NLOS & 0.89 & 0.98 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The probability of the correct detection of the direct visibility conditions.  
\[f_{LQI}(RSSI)\ =\ e^{(-b\cdot RSSI+c)}\ +\ d \tag{14}\]  
where \(b\), \(c\), and \(d\) are the parameters of this function. In Figure 4, a graph of the exponential regression function fitted (red) to the empirical measurement data (black) from all scenarios is presented.  
The values of the LQI are not strongly dependent on the RSSI values. However, small changes in the case of low RSSI values in the NLOS conditions can be seen. The estimated parameters of the exponential function are: \(b\ =\ 0.188\), \(c\ =\ -13.61\), and \(d\ =\ 2.972\). The LQI values' distribution around the fitted regression function is described by the normal distribution, with an average value of \(\mu\ =\ 0\) and a standard deviation \(\sigma\ =\ 0.779\), which indicates a small dispersion of them around the fitted regression function.  
The presented results confirm that, for the analyzed WBAN where there are no interferences from other networks, the LQI cannot be a parameter that determines the allocation of data streams in the NB interface. It is also expected that the ALBS algorithm using this parameter to allocate data streams will obtain worse results, in relation to the other methods, due to the lack of the LQI changes, regardless of the received signal power.
## 3 Methodology
### Congestion Assessment Methodology
The cut-off threshold of boundary of bottleneck influence area, \(\gamma\), is reasonable in that spatial patterns depicts all TMC segments is located upstream have greater AHCI value than downstream TMCs' as seen in Figure 3a-c. The recurrent bottleneck identification methodology developed by [PERSON] et al. [11] employs an exhaustive search algorithm to identify the recurrent bottleneck locations. Two constraints are imposed on this search: First, to be included in a recurrent bottleneck location, all contiguous TMC segments must have AHCI values exceeding 50%. Second, at most two of the TMC segments must be included by spatial pattern 2. A threshold, \(\delta\), is defined as the allowable difference in AHCI. The proposed \(\delta\) value in this study is 2. In Figure 4, the TMC U segment produces an AHCI value greater than \(\delta\), which is greater than the AHCI value for TMC Ds, so this segment is included in the recurrent bottleneck. [PERSON] et al. [11] recommended values of \(\delta\) equal to 2.0, 2.4, and 2.5, which can be selected differently by transportation jurisdictions. The methodology is as follows Algorithm 1. The recurrent bottleneck identification methodology. ```
For all each spatiotemporal AHCI(\(i,t\))  If AHCI(\(i,t\)) \(\geq\) 50%  Spatial pattern 1, 3: \(y_{1}\) = AHCI(\(i,t\)) - 2:AHCI(\(i\)+1, \(t\))  Spatial pattern 2(1): \(y_{2}\) = AHCI(\(i,t\)) - 2:AHCI(\(i\)+2, \(t\))  Spatial pattern 2(2): \(y_{3}\) = AHCI(\(i,t\)) - AHCI(\(i\)+1, \(t\))  If y\({}_{3}\)\(\geq\)0 && y\({}_{1}\)\(\geq\)0\(\|\)\(y_{2}\)\(\geq\) 0 Then, the segment (i) possibly as bottleneck
```  
**Algorithm 1** The recurrent bottleneck identification methodology. Figure 4: Bottleneck segment identification using an AHCI contour map.
## 2 Methodology, System Description, and Problem Formulation
### System Description
#### 2.2.1 Workflow and Latency Model  
The workflow of the proposed 360\({}^{\circ}\) VR video service system is illustrated in Figure 3. In the figure, we detail the operation process of period \(l\). Each period consists of a caching stage and a computation and transmission stage:  
* Caching Stage: The duration of this stage is \(D_{1}\). During the caching stage, the users watch the video tiles received in the previous period. At the same time, the MEC server caches the video tiles that all users may need to watch in the next period, based on the FoV prediction results from the cloud server.
* Computation and Transmission Stage: The duration of this stage for user \(i\) is \(D_{\text{M},l^{\prime}}^{(i)}\) which starts when user \(i\) finishes watching the video tiles obtained from the previous period and ends when the user can start watching the video tiles obtained during this period. For each accessed user \(i\), this stage includes the computation sub-stage delay \(D_{2,l}^{(i)}\) at the MEC server, the transmission sub-stage delay \(D_{3,l}^{(i)}\) from the MEC server to the user, the transmission sub-stage delay \(D_{4,l}^{(i)}\) from the cloud server to the user, and the computation sub-stage delay \(D_{5,l}^{(i)}\) at the user. Obviously, the aforementioned delay components of any user \(i\) depend on the amount of transmission and computing resources to be allocated to the user, which will be discussed in Section 2.2.3. Therefore,  
Figure 2: The designed multi-user 360\({}^{\circ}\) VR video service system.  
for accessed user \(i\), the E2E delay in period \(l\), which is the duration of the computation and transmission stage \(D_{\text{M},l}^{(i)}\), consists of four parts:  
\[D_{\text{M},l}^{(i)}=D_{2,l}^{(i)}+D_{3,l}^{(i)}+D_{4,l}^{(i)}+D_{5,l}^{(i)} \tag{1}\]  
Typically, when the E2E delay exceeds a certain threshold \(D_{\text{th}}\), users may experience dizziness, and the quality of experience significantly decreases. Therefore, when multiple users are simultaneously receiving 360\({}^{\circ}\) VR video services, we need to ensure that the probability of each accessed user's E2E delay exceeding the duration \(D_{\text{th}}\), \(P\left(D_{\text{M},l}^{(i)}>D_{\text{th}}\right)\), is sufficiently small. To ensure fairness, a good 360\({}^{\circ}\) VR video service system should well maintain this probability for each accessed user without any priority distinction. It is also noteworthy that the E2E delay studied here excludes the impact of cache speed. That is, we assume that the cache speed is high enough so as not to affect the E2E delay.
# Environment-Aware Knowledge Distillation for Improved Resource-Constrained Edge Speech Recognition
## 3 Proposed Model
### Innovation #2: Data Augmentation  
Earlier S3 RL models commonly relied on learning features from large unlabeled audiobook data, such as LibriSpeech [23] or LibriLight [24]. However, even though these models can learn fundamental characteristics from speech signals, real-world deployment data often involves diverse channel conditions and environmental noises that harm the system performance, a problem known as domain shift. To tackle this issue, data augmentation has been proposed as a technique to improve data diversity and reduce model bias [4; 15; 27]. Here, we adapt the RobustDistiller recipe proposed by [17], as shown in Figure 2. In particular, aiming to reduce computational requirements, we do not utilize the speech enhancement head present in the original work. At training time, given a batch of clean speech utterances, we sample one action to be applied to each utterance in the batch: (i) no changes are made to the training utterance; (ii) contaminate the utterance with either additive noise with a signal-to-noise ratio randomly chosen from \([0,30]\) dB or convolve the speech waveform with a randomly selected room impulse response. The sampling probabilities of scenarios (i) and (ii) are 30% and 70%, respectively.  
Figure 1: Weight analysis of the HuBERT model. The \(x\)-axis corresponds to each of the Transformer layers of the context network.
## 2 Field Experiment
### Measurement Plan
#### 2.3.3 Target Setting for Image Processing Approach  
Figure 5 shows the configuration of the image processing approach employing MOVIAS neo by Nac. Image Technology Co., Ltd. located in Akasaka, Minato City, Japan, which is widely used in automotive crash test analysis [7]. The targets were Quadrants deployed to track the frames for deflection. They were set at 5.0 m at t\({}_{1}\) to t\({}_{5}\) on the top chord of both the left and right spans [7]. Figure 5a presents the target setup along one of the spans. The approach is an improvement from conventional totals station that only monitors one point, leading to limited information rather than point cloud data [8; 17]. The deformation of the targets was recorded from different monochrome cameras, shooting angles, and resolutions of A: 4608 \(\times\) 3456 pixels, B: 5472 \(\times\) 3648 pixels and C: 5184 \(\times\) 345 pixels. Figure 5b illustrates how the image processing approach determined the deformation. A preselected, fixed reference point was established during the experiment for calibration using the recommended spatial resolution on the user interface--the guard rail bolt, as shown in Figure 5c. The reference point was then used to determine the displacement in reference to the target Quadrants. With a minimum resolution of 15 pixels, high center detection accuracy can be achieved on the quadrant. The target was set so that the pixel luminance information coincides with the template size [7]. Hence, the difference (\(\Delta h\)) is then resolved as the deflection value encapsulated in Equation (3). The instrument was only used during static loading due to scan time delays.  
\[(D_{0}-D_{n})=\Delta h \tag{3}\]  
where, \(D_{0}\) Deformation/deflection reading after loading, \(D_{n}\) Deformation/deflection reading before loading, and \(\Delta h\) Deformation or deflection in mm.  
Figure 5: (**a**) Image processing setup, Targets are set at specific spots, and structural responses are determined from the recorded events. (**b**) Deflection determination was based on target/quadrant vs. fixed bolt relationship. (**c**) Preselected rigid reference point—the guard rail bolt.
## Article
The levelized cost of energy (LCOE) is USD 0.036/kWh (INR 2.61/kWh) with an IRR of 8.55%, which is encouraging for widespread FSPV deployment. This was the project with the highest installed capacity in 2022. Figure 3: Number of publications based on floating PV systems by year. Along with advancements in renewable energy, EV has been the biggest change that is being made in the world to lead a sustainable and clean future. The global electric sales have gone up exponentially. It began with one million electric vehicles sold globally in 2015 and increased exponentially up to ten million in 2021 [18], China and Europe seem to be the leaders in the EV market followed by Europe. The first milestone we expect to reach is 20 million EVs by 2025. After that, low estimates mention 33 million EVs by 2030, whereas high estimates talk about 47 million EVs [19]. Figure 5 represents the EV market Size from 2010-2020 and the projected market size till 2030. The low indicates the case where sustainable development policies are not adopted while the high estimate of 47 million refers to the case when sustainable development is followed. This provides the motivation to explore the performance analysis of EVs when charged by a FSPVS. A research study was conducted by the Department of Energy [20] in which the complete cradle-to-grave lifecycle GHG emissions of EVs were compared to the average gasoline vehicle, which reached the same results as in [19], that hybrid EVs (HEVs), PEVs, and shorter-range BEVs cut greenhouse gas emissions, whereas longer-range BEVs are equivalent to the typical gasoline vehicles. [PERSON] et al. [21] quantified the lifetime GHG emissions and criteria pollutants of electric cars as well as the benefits of oil substitution. They discovered that EVs with larger battery packs are more costly, heavier, and emit more emissions than HEVs and PEVs with smaller battery packs, and that they give less emission gains. In both the basic and optimistic cases, all EVs cut emissions when compared to the average gasoline car. [PERSON] [22] discovered that the manufacture of the EV accounts  
Figure 4: Components of a FSPVS. Figure 5: EV market size (in billion USD) from 2010–2020 and projected market size till 2030.  
for 18.5% of the energy and 17% of the greenhouse gas emissions, while consumption accounted for 81.5% and 83%, respectively. Each study that analyzes the whole cradle-to-grave energy and greenhouse gas (GHG) emissions of electric vehicles concludes that emissions from vehicle usage surpass emissions from vehicle production. The literature highlights the need to drive towards a more sustainable future by reducing the CO2 emissions in EVs. This led to the motivation to utilize FSPVS to perform CO2 analysis of EVs. This has not yet been explored in the literature.
## 4 Results and Analysis
### Predictive Modelling of Material Removal Using ANFIS  
Adaptive neuro-fuzzy inference system (ANFIS) removes the primary problem in fuzzy if-then rules by using the learning ability of an artificial neural network (ANN) for automated tuning of fuzzy if-then rules during training, resulting in a change of the membership function of parameters, thus leading to the automatic adaptation of the fuzzy system based on inputs-output space. The initial step of the ANFIS modelling system is to decide the input and output variables of the fuzzy logic controller. The ANFIS model described in this paper has five input parameters; RPM, feed, force, hardness, grit, and the output as the depth of cut, i.e., material removal rate (MRR).  
The ANFIS model described in this paper is a Multiple Input Single Output (MISO) system with multiple inputs and a single output. Figure 11 shows real inputs and real output with fuzzy rule architecture of the ANFIS. A Taguchi based experimental design with 27 runs, as indicated in Table 2, is used as the inputs for the ANFIS model.
# 6G Network Architecture Using FSO-PDM/PV-OCDMA System with Weather Performance Analysis
###### Abstract  
This paper presents a novel 160 Gbps free space optics (FSO) communication system for 6G applications. Polarization division multiplexing (PDM) is integrated with an optical code division multiple access (OCDMA) technique to form a PDM-OCDMA hybrid. There are two polarization states: one is X-polarization generated from adjusting the azimuthal angle of a light source at 0\({}^{\circ}\) while the other is Y-polarization which is generated by adjusting the azimuthal angle of a light source at 90\({}^{\circ}\). Each polarization state is used for the transmission of four independent users. Each channel is assigned by permutation vector (PV) codes and carries 20 Gbps data. Four different weather conditions are considered for evaluating the performance of our proposed model. These weather conditions are clear air (CA), foggy conditions (low fog (LF), medium fog (MF), and heavy fog (HF)), dust storms (low dust storm (LD), moderate dust storm (MD), heavy dust storm (HD)), and snowfall (wet snow (WS) and dry snow (DS)). Bit error rate (BER), Q-factors, maximum propagation range, channel capacity, and eye diagrams are used for evaluating the performance of the proposed model. Simulation results assure successful transmission of 160 Gbps overall capacity for eight channels. The longest FSO range is 7 km which occurred under CA while the minimum is achieved under HD, which is 0.112 km due to large attenuation caused by HD. Within fog conditions, the maximum propagation distances are 1.525 km in LF, 1.05 km in MF, and 0.85 km in HF. Likewise, under WS and DS, the proposed system can support transmission distances of 1.15 km and 0.28 km, respectively. All these transmission distances are achieved at BER less than 10\({}^{-5}\).  
refe:footnote 1: footnotetext: **Subplisher’s Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
## 3 Proposed Bilateral Attention U-Net for Change Detection
### Dissimilarity Attention Gate (DAG)  
In this study, the proposed DAG is designed to overcome the order-dependency problem by exploiting two combinations of two joint features and difference features. The block diagram of the DAG is shown in Figure 3. \(DG_{l}\) in the \(l\)-th layer receives both feature maps (\(F_{1,l}\) and \(F_{2,l}\)). Firstly, this gate boosts the most informative channels by performing channel attention with the squeeze and excitation (SE) module [27]. Channel attention employs the functions of squeeze (\(SQ\)) and excitation (EX). \(SQ\) observes global spatial information into a channel descriptor by applying global channel-wise average pooling, calculated as follows:  
\[SQ_{c}=\frac{1}{H\times W}\sum_{m=1}^{H}\sum_{n=1}^{W}F(c,m,n), \tag{4}\]  
where \(c\) is the index of channels. Then, EX captures channel-wise dependencies from the information aggregated in the \(SQ\) operation. To limit the model complexity and support generalization, the gating mechanism is parameterized by forming a bottleneck with two FC layers around the nonlinearity. Finally, as shown in Figure 4, attention coefficients  
Figure 2: Proposed bilateral dissimilarity attention U-Net for change detection.  
are produced by applying the sigmoid function, which is multiplied channel-wise by the original feature-map input (\(F\)) to produce the final output.  
Let us define \(SE_{i,l}\) as the channel attention of the \(i\)-th image index at the \(l\)-th layer, resulting in boosted feature maps (\(F_{1,l}\) and \(F_{2,l}\)). The boosted feature maps are then processed to generate a difference feature by applying JF and absolute subtraction. This gate exploits all combination concatenation orders of JF to generate difference features with the first concatenation (\(CT_{1}\)) and the second concatenation (\(CT_{2}\)), which can be defined as follows:  
\[CT_{1}=[F_{1},F_{2}] \tag{5}\]  
\[CT_{2}=[F_{2},F_{1}] \tag{6}\]  
To extract the difference features of each \(CT\), a \(3\times 3\) convolution with shared parameters is applied. The kernels convolve \(CT_{1}\) and \(CT_{2}\) with shared parameters, generating independent difference features from \(CT_{1}\) and \(CT_{2}\). Figure 5 shows the independent difference feature maps on average from \(CT_{1}\) and \(CT_{2}\), from which the proposed DAG can extract the difference features in both image orders. To obtain a general difference feature, this gate  
Figure 4: Flow diagram of the squeeze and excitation (SE) module.  
Figure 3: Block diagram of the proposed dissimilarity attention gate (DAG).  
operates for each order of \(CT\), followed by ReLU. To enhance the feature representation, the general feature is combined with subtracted features, generating an enhanced difference feature (_o_). Then, the difference features are connected to the attention gate at the decoder side. This gate is implemented to the deepest encoding layer, and expanded by the decoder, as shown in Figure 2.
## 5 Conclusions  
This study investigated the mechanical properties and water-retention characteristics of fibre-reinforced unsaturated sand and developed an improved VG-CFR model. The main conclusions of this study are summarised as follows.  
1. The compressibility of soil decreases with increasing fibre length and content. The soil's compression modulus increases with the fibre content and length, whereas the compressibility index exhibits an opposite trend. The addition of fibre increases the initial void ratio. The interface force at the fibre-soil interface can reduce the compressibility of sand;
2. Under the fibre content in this study, the influence of fibres on the internal friction angle is minor, while enhancing the cohesive forces to elevate the shear strength of the soil. Before reaching the critical water content, the internal friction angle and cohesion of the soil increase with increasing water content but decrease beyond the critical water content. Water can fill the soil pores, increasing the internal friction angle and cohesion. The main reason for the decrease in the internal friction angle and cohesion is the significant reduction in soil suction after exceeding the critical water content;
3. The test results of the axis-translation method show that the addition of fibres can improve the water retention of sand. The compression test shows that the addition of fibres can strengthen the initial void ratio of the soil, affect the capillarity of the soil, provide additional contact area between the soil and water molecules, and improve the water-retention characteristics of the soil.  
The proposed model can be used in the design and evaluation of engineering projects related to fibre-reinforced soil, such as slopes and roadbeds Furthermore, this model can be utilized in numerical methods to determine the parameters of water-retention properties for unsaturated fibre-reinforced soil. The lengths of fibres need to be considered to further demonstrate the applicability of the proposed model in this study in future work.  
Conceptualization, X.B. and X.C.; methodology, L.L.; validation, X.B., Z.B. and Y.L.; formal analysis, Z.B.; investigation, P.P.; data curation, L.L.; writing--original draft preparation, Y.L.; writing--review and editing, Z.B. and L.L.; supervision, P.P.; project administration, X.C.; funding acquisition, X.B. All authors have read and agreed to the published version of the manuscript.  
This research is fully supported by the national Natural Science Foundation of China (Nos. 52022060 and 51938008).  
Not applicable.  
The authors declare no conflict of interest.