esa-sceva/satcom-chunk-collection huggingface dataset:
# A Constrained Coding-Aware Routing Scheme in Wireless Ad-Hoc Networks
## 4 Performance Evaluation
### Results from a Random Topology
#### 4.2.3 The Impact on Energy Consumption  
Next, we investigated the energy consumption under different numbers of flows in the random topology. Energy consumption was mainly decided by the physical transmission distance [45]. Therefore, to facilitate analysis and calculate the energy consumption, we employed a widely-used simplified model shown as follows:  
\[E_{T}(y,g)=E_{c}\times y+\xi_{amp}\times y\times g^{2}, \tag{10}\] \[E_{R}(y)=E_{c}\times y, \tag{11}\]  
where \(E_{T}(y,g)\) denotes the energy consumed for transmitting \(y\)-bits of data over the Euclidean distance \(g\). \(E_{R}(y)\) represents the energy consumed for receiving \(y\)-bits of data. \(E_{c}\) is a constant value that denotes the energy consumed for transmitting or receiving a one-bit message. \(\xi_{amp}\) is the transmission power amplification. In addition, we set the constant values \(E_{c}=100\) nJ/bit and \(\xi_{amp}=200\) pJ/bit/m\({}^{2}\) in the simulation.  
The energy consumed per packet under different numbers of flows is given in Figure 16. The energy consumed under the COPE + AODV scheme smoothly and slightly rose with the increase in the number of flows. Moreover, we can see that the CCAR and DCAR schemes had similar trends. In particular, in the case of a low number of flows, both schemes had the same energy consumption. As the number of flows became large, the energy consumption decreased first in the range of 4-16 because the network coding can encode packets and decrease the number of transmissions to counteract the effects of multiple flows. Moreover, for a larger number of flows, the advantages of CCAR began to emerge, and its energy consumption was lowest because the proposed scheme had the highest decoded packet ratio and reduced the re-transmission process.  
Figure 15: The impact on average end-to-end delay in the random network.
# A Novel Low-Complexity Fault Diagnosis Algorithm for Energy Internet in Smart Cities
## 1 Introduction
### Our Contribution  
In this paper, we propose a novel low-complexity algorithm named \(SFBT\) for fault detection in smart cities, which consists of a Strong Tracking Unscented Kalman Filter (\(STUKF\)), a Modified Bayes' classification algorithm (\(MB\)), and a Modified Three Sigma test (\(MTS\)), aimed to detect both of attenuation type faults and jump type faults. Besides, as a special case of the hypothesis test, \(SFBT\) can detect Byzantine faults [38]. Note that the message complexity of \(SFBT\) is \(O(n)\) such that the lifetime of a smart city network is extended. The details of our contributions are listed as follows:  
1. The \(STUKF\), along with the \(MB\), is applied to detect faulty cluster-heads in a centralized manner with a high accuracy; meanwhile, the messages exchanged are no more than \(O(n)\) such that the corresponding energy cost is reduced.
2. We develop \(MTS\) to determine the faulty state of every cluster-member within each cluster of a faulty cluster-head, while the cluster with a fault-free cluster-head can easily make a decision by simply comparing the data between the cluster-head and cluster-members. By doing so, the diagnosis accuracy is increased significantly, and the message complexity, which is only \(O(n)\), implies that the energy cost is further reduced.  
The rest of the paper is organized as follows. The system model and notions are given in Section 2. The strategies are elaborated in Section 3. Section 4 gives the theoretical analysis on the performance of the \(SFBT\). The validation results are presented in this section, as well. We conclude this paper in Section 5.
# Compact Full Ka-Band Waveguide Directional Coupler Based on Rectangular Aperture Array with Stairs
## 4 Results  
### Fabrication  
The 3 dB full Ka band waveguide directional coupler based on rectangular aperture arrays with stairs is fabricated according to the configuration parameters given above and assembled/disassembled pictures are shown in Figure 6. The coupler is fabricated by a brass nut, gold plate.  
Figure 5: Prototype coupler (**a**) with transitions, (**b**) the 3D-structure diagram, and (**c**) exploded schematic.
## 1 Introduction
The results indicated a decrease in CO and HC emissions and an increase in fuel consumption. [PERSON] et al. [24] investigated the optimization process for the production of biodiesel from blends of edible and inedible vegetable oils. The results show that the biodiesel obtained has good combustion properties that meet the ASTM standards. [PERSON] et al. [25] studied the production of biodiesel from blends of residues of fish oil, palm oil, and frying oil. It was concluded that fuel properties, such as viscosity and oxidation stability, show improvements. The research developed by [PERSON] et al. [26] showed that olive oil could be implemented to improve the properties of biodiesel produced from waste oils from the fishing industry. The previous studies show the viability of the blends of different raw materials for the production of biodiesel, showing in some cases an improvement in the physical and chemical properties compared to the single use of a material. However, a large part of the biodiesel studies consisting of blends of different oils, do not show results from the analysis of this type of biodiesel in the combustion parameters and its relationship with polluting emissions. The objective of this work is to evaluate the combustion, performance and emission characteristics of a diesel-biodiesel blend produced from two major polluting sources: residual sunflower oil and palm oil from agroindustry liquid waste because this two residues offer the highest production per hectare of crop and have characteristics relatively close to conventional diesel compared to other sources of raw material. Therefore, a combustion diagnostic model based on the combustion chamber pressure has been developed in order to study the effect of the biofuel produced in the combustion processes of a single-cylinder diesel engine of low displacement, which is widely used in areas not interconnected for energy production. In addition, the effect of the combustion process on yield and CO, CO\({}_{2}\), NOx, and smoke opacity emissions are studied.
## 3 Results and Discussion
### Evaluation with Accuracy Metrics  
As the last evaluation step, the precision, recall, and F1 scores were calculated for all networks and all datasets with a 0.5 IOU threshold. To observe how the models can generalize the training data, these metrics were calculated for the training data as well. Moreover, the first attempt of the training for SSD and Faster R-CNN were added to the evaluation in this step to observe the improvements gained by second training with modified parameters for these models. According to the results presented in Table 6, the Faster R-CNN with the second training parameter set provided the highest precision, recall, and F1 scores for both the DOTA and large-scale test sets. Moreover, it took second place after YOLO-v3 with slight differences for the DOTA training set, which indicates good generalization and learning through the training phase. The YOLO-v3 performance is ranked as second for both test sets, with comparatively low recall values, which is a sign of an increment in non-detected objects. SSD with the second training parameter set provided the lowest scores for test sets as well as the training set, which indicates a low level of generalization and learning process (Figure 11). When the results of SSD and Faster R-CNN with the first training parameter set were compared with the second parameter set, an obvious improvement was observed with the modified parameters, indicating the importance of parameter selection in the training phase.  
Figure 10: Graphic representation of COCO AP metric results.
# Feasibility Analysis on the Use of Ultrasonic Communications for Body Sensor Networks
## 2 Principles of Ultrasonic Waves
### Far Field  
The characterizations of ultrasonic beams are classified by two areas, near field and far field, as shown in Figure 1. The amplitude in the near field is irregular and can be extremely difficult to accurately evaluate in transmission. The area beyond the near field, where the amplitude of the ultrasonic beam is more uniform, is called the far field. In this area, the amplitude becomes inversely proportional to the distance from the transducer.  
The far field is the area beyond N where the sound field pressure gradually drops to zero. Due to the variations within the near field, it can be difficult to accurately evaluate flaws using amplitude. In the experimental design, the receiver should be placed in the far field. The distance starting from the far field can be calculated by Equation (3), where \(D\) is the diameter of the ultrasound transducer and \(\lambda\) is the wavelength in the transmission medium.  
\[N>\frac{D^{2}}{4\lambda} \tag{3}\]  
Figure 1: Beam profile with different distances.
## 2 Materials and Methods
### Data Acquisition and Preprocessing
#### 2.2.3 Satellite Remote Sensing Data
The moisture-related indicators, including evapotranspiration (ET), the root-zone soil moisture (SM), the land surface water index (LSWI), and the surface water capacity index (SWCI), were used as an alternative to soil moisture status. The ET data used in this study is derived from the MOD16A2 V6 product, which is the sum of an 8-day composite, has a 500-m pixel resolution, and is produced by the logic of the Penman-Monteith equation algorithm. The accumulative 8-day MOD16A2 ET products were averaged into daily ET to be consistent with the acquisition time of FCO\({}_{2}\)[64; 65]. We also obtained the SMAP root zone soil moisture (SM) product to represent the SM, which has the global coverage at a spatiotemporal resolution of 9 km and 3-h intervals. In this study, the mean of SMAP SM data at 10:30 AM and 1:30 PM was collected to match the measurement time of FCO\({}_{2}\). Besides, the LSWI and SWCI here are calculated from the above-mentioned MOD09A1 products. The calculation formulas of LSWI and SWCI are shown in Equations (4) and (5). \[\text{LSWI}=\frac{\rho_{2}-\rho_{6}}{\rho_{2}+\rho_{6}} \tag{4}\]  
\[\text{SWCI}=\frac{\rho_{6}-\rho_{7}}{\rho_{6}+\rho_{7}} \tag{5}\]  
where \(\rho_{6}\) and \(\rho_{7}\) are the MODIS shortwave infrared (SWIR1) and (SWIR2) bands (centered at 1640 and 2130 nm, respectively). In this study, the quality assurance files included in these remote sensing products (e.g., MODIS products) were also obtained for excluding contaminated and low-quality pixels [66]. We employed these quality assurance files to eliminate the poor quality data and then used the linear interpolation to replace the missing values [8; 64; 65; 67]. All the RS data sets were multiplied by their corresponding scale factor and converted to the commonly used units such as converting the Kelvin to Celsius of LST (Please refer to the MODIS and SMAP User Guide). Furthermore, all the RS data were resampled to 500 m using the nearest interpolation method. Based on the geographic coordinates (i.e., latitude and longitude) of each site, we then extracted all the RS variables using a 3 \(\times\) 3 pixel buffer around each site. Eventually, all the RS variables in this study were used as the model (see below) inputs for modeling the FCO\({}_{2}\).
## 6 Results and Discussion  
We evaluated our MT systems on the test sets described in Section 4 and measured their performance using SacreBLEU [41]. To report the results, we use three evaluation metrics: BLEU, chrF [42], and COMET [43]. To measure the training costs of the student models, we measured the energy consumption and carbon emissions [44] during training, for which we used Machine Learning Emissions Calculator ([[https://mlco2.github.io/impact/](https://mlco2.github.io/impact/)]([https://mlco2.github.io/impact/](https://mlco2.github.io/impact/)); accessed on 1 June 2025). The CO\({}_{2}\) emissions generated during training were computed using Equation (9):  
\[\text{CO}_{2}\text{ Emissions}=\text{Power Usage}\times TT\times C \tag{9}\]  
where \"Power Usage\" is a constant related to the hardware type (we used an A100 GPU for model training, for which the power usage is 0.4 kWh), \(TT\) represents the training time, and \(C\) represents the carbon intensity of the power grid (C = 0.43 \(kg\) CO\({}_{2}\)/kWh). Below, we present and discuss the obtained results.  
The performance of the teacher (NLLB-200-3.3B) and baseline (NLLB-600M) models can be seen in Table 2. In Table 2, the second row shows results for the vanilla baseline model, which used the NLLB-600M model without any modifications. The final row of Table 2 refers to the NLLB-600M baseline model trained on the original parallel data only.  
It can be seen from Table 2 that the performance of the vanilla baseline model is quite good and close to the performance of the teacher model. As pointed out above, our experimental setups included two specific use cases involving industrial translation setups with access to only (i) a bilingual corpus of limited size and (ii) a monolingual corpus of the source language. The following sections discusses the findings on each of these scenarios.  
\begin{table}
\begin{tabular}{l l c c c} \hline \hline  & & **SacreBLEU** & **chrF** & **COMET** \\ \hline NLLB & IN22-Gen & 40.57 & 66.62 & 85.55 \\  & FLORES-200 & 39.02 & 60.99 & 87.35 \\ \hline Vanilla Baseline (NLLB-600M) & IN22-Gen & 37.48 & 63.31 & 83.73 \\  & FLORES-200 & 31.87 & 58.80 & 82.63 \\ \hline Baseline (NLLB-600M) & IN22-Gen & 37.38 & 65.17 & 84.56 \\  & FLORES-200 & 31.05 & 60.62 & 83.72 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Performance of the teacher and vanilla baseline models. Teacher models: NLLB-200-3.3B and NLLB-200-600M.  
Figure 5: Confidence filtering in KD. SDD: Standard Distilled Data, FDD: Filtered Distilled Data, OPD: Original Parallel Data.
## 4 Results
### Thermal Equalization with FFT Algorithm  
The Gaussian-filtering-based the FFT algorithm is a method to reduce the influence of high frequencies in the time or space domain. Here, high frequency refers to an area where the temperature rises, such as the defective area, due to foreign substances on the surface of the specimen. In addition, the current density locally increases at the edges of the specimen due to [PERSON]'s heating. Therefore, these areas can also be considered as high frequency, and applying the FFT algorithm can reduce the influence of crack recognition when YOLOv4 is applied. As a result, the detectability and accuracy of cracks can be improved. For thermal equalization in Figure 5, it is necessary to move to the frequency domain, and the equation is as follows [12; 20; 21].  
\[H(u,v)=e^{-\frac{1}{2}\frac{((u-u_{0})^{2}}{\sigma_{u}^{2}}+\frac{(v-v_{0})^{2 }}{\sigma_{v}^{2}})} \tag{4}\]  
where \(H(u,v)\) is the frequency domain of Gaussian filtering, \((u,v)\) is coordinate of frequency domain, \(u_{0}\) and \(v_{0}\) are center position of Gaussian filtering, and \(\sigma_{u}\) and \(\sigma_{v}\) are standard deviation of Gaussian distribution.  
\[G(u,v)=F(u,v)\cdot H(u,v) \tag{5}\]  
where \(F(u,v)\) is the image with the FFT algorithm applied to thermal image \(F(x,y)\) and \(G(u,v)\) is the image of frequency domain. And then, if the inverse transform is performed on \(G(u,v)\) again, it is as follows.  
\[G(x,y)=IFFT\{G(u,v)\} \tag{6}\]  
Figure 6 shows an image that applies the Gaussian-filtering-based the FFT algorithm to each moving speed. The Gaussian standard deviation is set to 200. The degree to which the foreign substance distribution has been removed can be confirmed, compared to Figure 5. However, it is difficult to qualitatively detect them because it is affected by the crack in the first row. Therefore, setting the ROI labeling of the crack area is very important.  
For each moving speed, the FFT algorithm was applied to the images of all frames to perform thermal equalization, and the images were integrated into one video to obtain six data for each moving speed. After that, it was applied to YOLOv4 to perform AI-based automatic crack detection.  
Figure 5: Sequence thermal image for each moving speed.
## 2 System Model
To solve the problem, in this paper, we propose the scheme using hybrid \(l_{1}/l_{2}\) norm minimization and OMP algorithm. According to the hybrid \(l_{1}/l_{2}\) norm minimization, at the \((k+1)\)-th iteration, the proposed scheme will reweight the residual vector \(\mathbf{r}=\widetilde{L}^{\mathrm{H}}\widetilde{w}_{i}-\widetilde{L}^{-1} \widetilde{r}_{\Delta,i}\) with an \(N\times N\) iteratively updated diagonal weighting matrix \(\mathbf{Q}^{(k)}\), and minimize the weighted \(l_{2}\) norm  
\[J(\widetilde{\mathbf{w}}_{i})=\|\mathbf{Q}^{(k)}\widetilde{L}^{\mathrm{H}}\widetilde{ w}_{i}-\mathbf{Q}^{(k)}\widetilde{L}^{-1}\widetilde{r}_{\Delta,i}\|_{2}^{2}. \tag{13}\]  
Then solve the above problem using the OMP algorithm to find a sparse solution. \(\mathbf{Q}^{(k)}\) is updated from the current residual through the hybrid \(l_{1}/l_{2}\) norm minimization. The diagonal entries of \(\mathbf{Q}^{(k)}\) are as follows  
\[q_{m}^{(k)}=\frac{1}{\bigg{(}1+\left(r_{n}^{(k)}/\mu\right)^{2}\bigg{)}^{1/4}},\,1\leq n\leq N, \tag{14}\]where \(r_{n}^{(k)}\) is the \(n\)-th entry of the current residual \(\mathbf{r}^{(k)}\) and \(\mu\) is determined by the sparse solution. Substituting (14) into (13), minimizing (13) is equivalent to minimizing  
\[J(\widetilde{\mathbf{w}}_{i})=\sum_{n=1}^{N}j(r_{n}^{(k)})=\sum_{i=1}^{N}\frac{{(r_ {n}^{(k)})}^{2}}{\sqrt{1+{(r_{n}^{(k)}/\mu)}^{2}}}. \tag{15}\]  
For an arbitrary value of \(r_{n}^{(k)}\), the approximation of \(j(r_{n}^{(k)})\) is  
\[j(r_{n}^{(k)})\approx\left\{ \begin{array}{cc}{(r_{n}^{(k)})}^{2},&{\left|r_{ n}^{(k)}\right|}\ll\mu\\ {\mu\left|r_{n}^{(k)}\right|},&{\left|r_{n}^{(k)}\right|}\gg\mu\end{array} \right. \tag{16}\]  
which suggests \(J(\widetilde{\mathbf{w}}_{i})\) behaves like an \(l_{2}\) norm for small residuals and like an \(l_{1}\) norm for large residuals. Therefore, the hybrid \(l_{1}/l_{2}\) norm minimization will make the sparse solution more robust and accurate by decreasing the sensitivity of the solution to large residuals. The procedures of the proposed scheme for finding the sparse solution of the FIR MIMO DFE are as follows:  
_Initialization:_ Set the initial residual \(\mathbf{r}^{(0)}=\widetilde{\mathbf{L}}^{-1}\widetilde{\mathbf{r}}_{\Delta,i}\), define the index set \(\Omega=\{1,2,\cdots,N\}\), and initialize the diagonal weighting matrix \(\mathbf{Q}^{(0)}=\text{diag}(1,1,\cdots,1)\) for the first iteration. _Main iteration:_ Set \(k=1\) and perform the following steps:  
_Step 1_: Reweight the residual vector \(\mathbf{r}=\widetilde{\mathbf{L}}^{\text{H}}\widetilde{\mathbf{w}}_{i}-\widetilde{\mathbf{L}} ^{-1}\widetilde{\mathbf{r}}_{\Delta,i}\) and formulate the problem  
\[\min\lVert\mathbf{Q}^{(k-1)}\widetilde{\mathbf{L}}^{\text{H}}\widetilde{\mathbf{w}}_{i}- \mathbf{Q}^{(k-1)}\widetilde{\mathbf{L}}^{-1}\widetilde{\mathbf{r}
# Rateless Coded Uplink Transmission Design for Multi-User C-RAN
## 5 Simulation Results
Finally, it can be seen that under all the three channel states, the optimized profile under the eight-bit case achieves \(10^{-4}\) BER at an overhead of less than \(1.14\), which is close to the theoretical limit (which ignores the quantization noise), i.e., \(overhead=1\). * 2 Throughout performance  
Figure 7: Bit error rate (BER) performance with the optimized profiles and benchmark profile for the cases of the three-bit quantizer and the eight-bit quantizer under different channel matrices: (**a**) \(\mathbf{H}_{1}\), (**b**) \(\mathbf{H}_{2}\), and (**c**) \(\mathbf{H}_{3}\). We simulate the average sum throughput under different SNRs. We fix \(\sigma_{0}^{2}=1.2^{2}\) but change the transmission power \(P\) for different SNRs. For each SNR, we optimize the degree profiles of the two users for both three-bit and eight-bit quantizer cases. Figure 8 shows the simulation results. For each point in the figure, we simulate 100 rounds of transmission, and the channel matrix is uniformly and randomly chosen from \(\mathbf{H}_{1}\), \(\mathbf{H}_{2}\) and \(\mathbf{H}_{3}\) in each round. As can be seen in Figure 7, the optimized degree profile achieves a higher throughput compared with the BEC profile. The performance of the eight-bit quantization case with the optimized degree profiles is within only \(11\%\) away from the theoretical limit. Here, the theoretical limit is the upper bound of the average theoretically achievable sum rate, which is calculated by  
\[C_{lim}=\frac{1}{\sum_{q=1}^{3}C^{-1}\big{(}H_{q},P\big{)}\text{Pr}\big{(}H_{q }\big{)}}. \tag{34}\]
# Review and Evaluation of MAC Protocols for Satellite IoT Systems Using Nanoatellites
## 1 Introduction
[14]; nevertheless, besides the fact that the mentioned works do not include a quantitative performance evaluation and comparison,  
Figure 1: Approximate coverage of a geostationary satellite located at 91\({}^{\circ}\) W.  
a good part only discuss terrestrial IoT wireless technologies; hence, the discussion is oriented to different channel and network conditions from the ones addressed in this survey. The contributions of this paper are threefold: (1) we review MAC protocols employed or proposed for satellite systems from a novel viewpoint that considers the restricted characteristics of CubeSat technology for wireless communications together with the particular requirements of IoT services and applications; (2) we provide a comparative quantitative and qualitative evaluation of the current protocols with metrics including communications performance (i.e., throughput, channel load, packet loss), dependency of network topology, implementation complexity, and energy consumption; and (3) we discuss the open research and implementation challenges to address by the next generation of nanosatellite networks for IoT environments. The remainder of this paper is organized into seven sections. In Section 2 we present the fundamental aspects of satellites, space communication systems, and nanosatellites technology. It also includes a discussion about the IoT requirements. In Section 3 we introduce the specifics about the proposed IoT scenario served by a constellation of CubeSats. In Section 4 we provide the backgrounds on MAC layer protocols and introduce the metrics for evaluation. Section 5 presents a detailed review of MAC protocols designed and developed for satellite systems and other IoT-related technologies. It also includes a performance evaluation with objective metrics relevant to the IoT study scenario. In Section 6 we discuss the advantages and shortcomings of the existent protocols and identify the open challenges. Section 7 presents the final remarks.