esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
As the deployment of LoRa Sub-GHz technologies and LPWAN Short-Range Devices (SRD) is rapidly growing, there is little knowledge of interference in the unlicensed 868 MHz ISM band and its signal degradation effect on urban LoRa transmissions. In particular, the number of LoRa end-devices is steadily growing [18], making LoRa-based transmissions subject to interference despite the presence of DC and maximum transmission power (Tx) regulatory regime. With this in mind, the probability that the presence of interference degrades signal-to-interference ratio of LoRa transmission, resulting in a loss of communication in Glasgow CBD, is important to determine. This paper investigates performance of a live LoRaWAN network over multiple gateways at multiple sites carrying live traffic. It builds on an earlier analysis of LoRa range and LoRaWAN networking reported in [19], which fell short of interference analysis on the performance of performance. Additionally, the paper investigates presence of interference in the 868 MHz ISM band in Glasgow CBD and degradation effects on LoRa transmissions. The interference study in the 868 MHz ISM band comes after it emerged that LoRaWAN outdoor packet losses measured from Glasgow CBD were 50 X greater than the indoor measurement test results. The SNR levels recorded in the Glasgow CBD environment were attributed to possible interference from other sources, including ISM band technologies working in the same band and other LoRaWAN installations. Hence,the establishment of an understanding of interference in the 868 MHz ISM band and whether the behavior of signals occupying LoRa core uplink channels, 868.1 MHz, 868.3 MHz, and 868.5 MHz, and the downlink channels, 869.4-869.65 MHz may have had degradation effects on LoRaWAN packet losses in Glasgow CBD enhances the present knowledge of the LoRaWAN transmission environment. The contributions of this work are four folds:* The performance analysis of a live LoRaWAN multi-gateway in an indoor site. * The comparative performance analysis of LoRaWAN packet losses for indoor and outdoor measurements results. * Measurements of signal activity and power levels in the 868 MHz ISM band in five different locations in Glasgow CBD. * An analysis of the measurement results to determine the extent which interference in Glasgow CBD is likely to prevent LoRa communication. The rest of paper is organised as follows: The related work in Section 1, a brief introduction to LoRaWAN operation is specified in Section 2, followed by the description methodology in Section 3. Section 4 presents results and measurements analysis. The discussion follows this section in Section 5 and the conclusion and future work in Section 6.
# A Dual-Connectivity Mobility Link Service for Producer Mobility in the Named Data Networking
## 5 A Producer Mobility Link Service in NDN Face
### Transaction Structure  
The task unit in NDN can be viewed as the exchange of the <Interest, Data Packet > pair. The PMLS views the exchange of the pair as a transaction.  
The PMLS defines the transaction structure, which is used to track the exchange state of an <Interest, Data Packet > pair. As an Interest is typically a short message, it is transmitted as a single packet. On the other hand, the Data packet contains a content, so the size of the Data packet may be big enough to be fragmented into segments at the PMLS. When a PMLS receives an Interest from an upper layer or a lower layer, it creates an instance for the transaction structure. Figure 3 shows the transaction structure at the PMLS. The PMLS records the exchange state of the transaction in the instance of the transaction structure and is used to recover the transmission state afterward.  
The elements of the transaction structure are defined as follows:  
* Content name: The content name derived from the Interest. It is used to identify the instance of the transaction structure in a PMLS.
* Nonce: The fixed-length value from a nonce field of the Interest. The nonce is set by the original data requester. The nonce is included in the PMLS control messages used to identify a relevant instance of the transaction structure at the peer PMLS. It reduces unnecessary packet overhead caused by variable-length content names.
* Transmission state : This is used to track the transmission state of the Data packet segments and to check how much of the expiration time of a transaction remains. A status report message uses this information.
* Address information: The peer transport endpoint information for control messages. It is used to transmit PMLS control messages. It is updated by a status report message sent by its peer PMLS if the state message contains this information.  
Figure 2: Producer Mobility Link Service in NDN Face.
## 3 Results
### Effect of FMLSP on Glucose Metabolism in Mice  
With a relative rise of 139.98%, the insulin content of mice in the T2 DM group was significantly greater (\(p<0.01\)) than that of the NC group, as shown in Figure 3E. This was due to the diabetic mice developing insulin resistance (IR), which made it difficult for insulin to function resulting in accumulation [37]. Mice in the FMLSPL, FMLSPH, and Met groups had lower insulin contents after Met and FMLSP treatments than those in the T2 DM group by 43.78%, 52.34%, and 54.53%, respectively. FMLSP has a moderating impact on IR symptoms, as seen by the reduction in insulin.  
The mouse body's average blood glucose level during the previous two to three weeks is reflected in GSP [38]. Mice in the T2 DM group had a GSP level that was 1.86 times greater than the NC group, however, after the FMLSPH and Met treatments, the GSP level decreased by 33.58% and 34.12%, respectively, indicating that FMLSP effectively ameliorated the hyperglycemia in T2 DM mice (Figure 3F). The liver glycogen content in the NC group  
Figure 3: Effect of FMLSP on glycolipid metabolism in T2 DM mice. (**A**) Serum of TC; (**B**) serum of TG; (**C**) serum of LDL-C; (**D**) serum of HDL-C; (**E**) insulin content; (**F**) GSP content; (**G**) liver glycogen content; (**H**) activity of HK; (**I**) activity of PK. (*) \(p<0.05\) and (**), \(p<0.01\) vs. T2 DM group.  
was 2.06 times higher than that in the T2 DM group, as shown in Figure 3G. Treatment with FMLSP and Met resulted in a significant increase in glycogen levels (\(p<0.01\)). When compared to the T2 DM group, the liver glycogen content rose by 61.08% and 66.36%, respectively, indicating that the effects of FMLSPH and Met were comparable.  
HK and PK activities in T2 DM mice demonstrated a marked reduction compared to normoglycemic controls (\(p<0.01\)), reflecting impaired glycolysis and compromised blood glucose regulation in the diabetic cohort (Figure 3H,I). FMLSP administration upregulated HK and PK catalytic capacities in a way that depends on dosage (\(p<0.05\)). This illustrates how FMLSP intervention might somewhat increase the activity of enzymes linked to glycolysis, which in turn encourages glycolysis to reduce blood glucose.
## 4 Discussion
### Future Work  
Following an extensive benchmarking of multiple classification architectures and acknowledging the inherent constraints within the existing dataset, an augmentation strategy utilizing synthetic data samples generated by a Generative Adversarial Network (GAN) is proposed. This method seeks to expand the dataset in terms of volume and variability, consequently aiming to enhance classification outcomes. The hybrid CNN-Transformer architecture, specifically TransUNet, emerges as a candidate due to its integration of CNNs with Transformer mechanisms. This hybridized approach permits simultaneous modeling of detailed local features alongside global context relationships, attributes that previously demonstrated substantial performance improvements within medical imaging segmentation tasks. The research extends further by juxtaposing the TransUNet [57; 58] model against various established segmentation frameworks, including but not limited to UNet, VGG, SC-NET, AlexNet, CornerNet, DenseNet, GAN-based models, and Swin-Transformer. This comparative analysis, conducted on datasets enhanced with synthetically generated samples, evaluates classification efficacy specifically for galaxy image segmentation. Performance metrics such as the DICE coefficient and F1-score form the basis of this comparative evaluation. The literature suggests that enhancements ranging from approximately 0.3 to 1.5 percent could be achievable through GAN-driven augmentation strategies, notwithstanding the potential risks of generating inaccurately labeled synthetic samples. Embedded systems, including smartphones and IoT devices, present significant computational limitations [59], predominantly in processing power and memory constraints. Traditionally, computationally intensive model training occurs on cloud or high-performance computing infrastructure, while inference tasks run directly on embedded hardware. This workflow reduces latency and mitigates deployment costs. Techniques such as TL, leveraging pre-trained models from repositories like the TensorFlow Model Zoo and Keras, have notably improved embedded model development. Innovations in adaptive model selection frameworks have demonstrated improved outcomes on platforms like the Jetson TX2, particularly concerning inference speed and accuracy. Additional research emphasizes utilizing embedded ML in diverse applications, including real-time blood pressure monitoring via smartphone-based neural network classifiers. The investigation of split learning architectures within IoT environments and bio-inspired [60] has provided insights into balancing computational efficiency, accuracy, and data privacy. Furthermore, efforts to optimize memory usage, including SRAM-focused implementations and TinyML solutions [61], underscore ongoing advancements in ML model deployment on resource-constrained embedded platforms.
## 4 Experimental Results
### Case Studies  
#### 4.2.1 Typhoon Tembin (1214)  
The time-series plot of the maximum wind speed for Typhoon Tembin (1214) is shown in Figure 9a. In this figure, the green dot indicates the estimated maximum wind speed by the proposed regression model, and the red line showed the maximum wind speed by the CMA best-track data. This figure shows that the variations of the maximum wind speed estimated by the regression model has good consistency with the maximum wind speed by the TC best-track data. However, in individual cases, the estimated intensity of TC will sometimes be underestimated or overestimated. Figure 9b is the time-series plot of the maximum wind speed for Super Typhoon Noru (1705). We can see that the tendency of intensity change corresponded reasonably well with the maximum wind speed by the TC best-track data, but there were two clearly underestimated values in the initial stage and in the maximum wind speed stage.  
We can observe that in the weak TC stages (<15 m/s), the outputs of the proposed method tended to underestimate the maximum wind speeds (Figures 7e and 9). The possible reason for this was the difficulty in positioning the TC center, due to an unorganized cloud pattern. Figure 10 shows the SSMIS and HY-2A images of Super Typhoon Noru (1705) at 20 July 2017 (the early stage). The maximum wind speed estimated by the regression model was 8.1 m/s, but the maximum wind speed in the CMA best-track data was 13.6 m/s. This may be related to the accuracy in the positioning of the center, and the unorganized patterns.  
Figure 8: RMSE values of the proposed model calculated at 5 min intervals of overpass time difference (**upper panel**); and the percentage of the sample number at each bin (**lower panel**).
# Adaptive Estimation Algorithm for Photoplethysmographic Heart Rate Based on Finite State Machine
## 2 Algorithm
### Preprocessing  
The algorithm is set up with a window size of 8 s and a step size of 2 s. In order to remove the noise that lies outside the normal heart rate range, including industrial frequency interference, baseline drift, etc., the raw PPG signals of the two channels and the triaxial acceleration signals are band-pass filtered in the frequency domain from 0.4 to 3.5 Hz. The signals are first converted from the time domain to the frequency domain by setting the spectral coefficients outside this frequency range to zero, and then the time domain signals are obtained by the Fourier inverse transform [15]. The filtered signals are normalized by the \(L_{2}\) norm, which can be calculated as follows:  
\[L_{2}=||\textbf{X}||_{2}=\sqrt{\sum_{i=1}^{n}x_{i}^{2}} \tag{1}\]  
In order to preserve the PPG signals for each channel and the acceleration signals for each direction, which are obtained by calculating the normalized signals arithmetic mean:  
\[PPG_{com}=\frac{1}{2}\bigg{(}\frac{PPG_{1}}{||PPG_{1}||_{2}}+\frac{PPG_{2}}{||PPG _{2}||_{2}}\bigg{)} \tag{2}\]  
\[\textbf{Acc}_{\text{com}}=\frac{1}{3}\bigg{(}\frac{\textbf{Acc}_{x}}{|| \textbf{Acc}_{x}||_{2}}+\frac{\textbf{Acc}_{y}}{||\textbf{Acc}_{y}||_{2}}+ \frac{\textbf{Acc}_{z}}{||\textbf{Acc}_{z}||_{2}}\bigg{)} \tag{3}\]  
where \(PPG_{1}\) and \(PPG_{2}\) denote the PPG signals of the two channels after band-pass filtering, and \(\textbf{Acc}_{x}\), \(\textbf{Acc}_{y}\), and \(\textbf{Acc}_{z}\) denote the three-axis acceleration signals after band-pass filtering, respectively.  
Figure 1: Flowchart of heart rate estimation.
# Quadratic Curve Fitting-Based Image Edge Line Segment Detection: A Novel Methodology
## 2 Derivation and Parameter Solving of the Edge Line Segment Model  
### Construction of the Edge Line Segment Model  
The straight edges in three-dimensional space possess collinearity during the imaging process, meaning that they remain straight when projected from a 3D space onto a two-dimensional image plane. Assuming the ideal equation of the projection of a 3D straight line edge onto a two-dimensional image plane is determined as follows:  
\[ax_{u}+by_{u}+c=0 \tag{1}\]  
where \((x_{u},y_{u})\) represents any arbitrary pixel point on the ideal projected straight line.  
In the actual imaging process, camera lenses typically exhibit radial distortion, tangential distortion, and thin prism distortion due to manufacturing tolerances. However, the thin prism distortion and tangential distortion of the lens are typically negligible compared to radial distortion and can be ignored in practical applications. Therefore, the radial distortion of the camera lens can be represented using a single-parameter DM (division model) [21] as follows:  
\[\begin{cases}x_{u}=\dfrac{x_{d}}{1+\lambda r_{d}^{2}}\\ y_{u}=\dfrac{y_{d}}{1+\lambda r_{d}^{2}}\end{cases} \tag{2}\]  
where \((x_{u},y_{u})\) is the distortion-free image point, \((x_{d},y_{d})\) is the distortion image point corresponding to the distortion-free image, \(\lambda\) is the distortion factor, and \(r_{d}^{2}=x_{d}^{2}+y_{d}^{2}\). Bringing Equation (2) into Equation (1) we have the following:  
\[a(\dfrac{x_{d}}{1+\lambda r_{d}^{2}})+b(\dfrac{y_{d}}{1+\lambda r_{d}^{2}})+c=0 \tag{3}\]  
Equation (3) is brought into \(r_{d}^{2}=x_{d}^{2}+y_{d}^{2}\). After sorting, we have the following:  
\[x_{d}^{2}+y_{d}^{2}+ex_{d}+fy_{d}+g=0 \tag{4}\]  
Among them, \(e=\dfrac{a}{c\lambda}\), \(f=\dfrac{b}{c\lambda}\), and \(g=\dfrac{1}{\lambda}\). From (4), it can be seen that under the effect of lens distortion, the projection of a straight line segment in three-dimensional space in the two-dimensional plane is no longer an ideal straight line but a quadratic curve.
## 1 Introduction
### Our Contribution  
We design practical implementations for compressing bit-vectors for rank- and select-queries that are compressible under a different measure of compressibility, namely bit-vectors having a low empirical entropy of order \(k>0\) (see Section 2 for formal definitions). Our algorithmic approach is to encode fixed-length blocks of the bit-vector with different kinds of codes (Section 3.1). While this approach has been theoretically proposed before [9], we are not aware of any practical evaluations of this scheme in the context of bit-vectors and rank-/select-queries. An interesting finding (Section 3.2) is that we can show that the block sizes can be chosen rather large (basically only limited by the CPU's word size), which automatically lowers the redundancy of the (otherwise incompressible) rank-information. We focus on data structures for rank, noting that select-queries can either be answered by binary searching over rank-information, or by storing select-samples in nearly the same way as we do for rank [10].  
We envision at least two possible application scenarios for our new data structures: (1) compressible trees [11], where the bit-vectors are always _dense_ (same number of opening and closing parentheses), but there exist other kinds of regularities. Easy examples are stars and paths, having balanced parentheses representations \((()()\dots())\) and \((((\dots)))\), respectively. (2) wavelet trees on repetitive collections of strings that are otherwise incompressible, like individual copies of DNA of the same species [7].
# A High-Efficiency Method of Mobile Positioning Based on Commercial Vehicle Operation Data
## 5 Implementation and Evaluation of a Commercial Vehicle Operation System  
In this section, the architecture of a CVO system is proposed and implemented, and the experimental practical results from the CVO system are compared with different location determination methods to evaluate the proposed mobile positioning method [33].
# Experimental Study of Perfobond Rib Shear Connector under Lateral Force
## 4 Results Discussion
### Rib Hole Diameter
As demonstrated in Figure 10, unlike in the BH 3-2 specimen, both BH 2-3 and BH 4-1 specimens had reinforcement bars failures, and those reinforcement bars deformed heavily. The heavily deformed reinforcement bars indicate that a large amount of the shear loads were shared by the reinforcement bars, and the concrete in Figures 11 and 12 also confirmed that the reinforcement bars take the majority of the shear loads. Figure 11: Concrete dowel and reinforcement of the BH 4-1 specimen after the test. Figure 10: Rib plate and reinforcement of BH 2-3 and BH 4-1 specimens after the test: (**a**) Deformation form of reinforcement of BH 2-3; (**b**) Deformation form of reinforcement of BH 4-1. Another comparison ground is made in Figure 13. BH-5 and BH-10 specimens have identical reinforcement diameters of 25 mm and rib thickness of 20 mm, and the only difference between them is that the BH-5 specimen has a rib hole diameter of 75 mm, whereas the BH-10 specimens has a rib hole diameter of 90 mm. As demonstrated in Figure 13, the BH-10 specimen has better overall resistance (11% improvement) and ductility (55% improvement) than the BH-5 specimen. The failure modes of those specimens are analyzed in Figures 14 and 15. A couple of things are noticed in Figures 14 and 15. Firstly, the BH-10 specimen has more noticeable deformation in both reinforcement bars and the ribs than the BH-5 specimen. Secondly, the rib in the BH-10 specimen is very heavily deformed, whereas the rib in the BH-5 specimen is not. This is because the increased rib hole diameter in the BH-10 specimen on one hand enhances the concrete dowel action, but on the other hand decreases the strength of the rib itself.
# Gelatin-Siloxane Hybrid Monoliths as Novel Heavy Metal Adsorbents
## 3 Results and Discussion
### Resistance of Gelatin Beads to Shaking in Water Solutions
#### 3.6.4 The Effect of Metal Ions Concentration
The comparison of the maximum adsorption capacity of studied metals onto various adsorbents described in the literature is presented in Table 5. It indicates that the samples examined in this work show lower values in comparison to many other adsorbents, including some gelatin-based ones [32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47]. In case of chitosan-gelatin monoliths reinforced with graphite oxide nanosheets (GO), reported by [PERSON] et al. [34], the adsorption capacity measured for Pb(II) and Cu(II) was 100 mg/g and 130 mg/g, respectively. In both cases, the addition of GO nanosheets improved the strength of chitosan-gelatin adsorbents and provided a great number of functional groups that allow for the binding with metal ions. Comparable values of adsorption capacity were reported for olive stone, pomegranate peel, bael tree leaf powder and monolithic xerogel [41; 42; 43; 45]. However, despite the fact that adsorption capacity values for hybrid adsorbents tested in this study are not very high, it should be underlined that their preparation is easy and cost-effective. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**adsorbent** & **qn, mg/g** & **Metal** & **Reference** \\ \hline \multirow{2}{*}{CGGO (chitosan-gelatin/graphene oxide) monoliths} & about 100 & Pb(II) & [34] \\ \cline{2-4}  & about 130 & Cu(II) & \\ \hline Granular activated carbon & 26.546 & Pb(II) & [35] \\ \hline Fly ash & 51.98 & Pb(II) & [36] \\ \hline Green algae Spirogyra & 90.91 & Pb(II) & [37] \\ \cline{2-4}  & 38.61 & Cu(II) & \\ \hline \multirow{2}{*}{Chitosan crosslinked with epichlorohydrin triphosphate} & 130.72 & Cu(II) & \\ \cline{2-4}  & 83.72 & Cd(II) & \\ \cline{2-4}  & 166.94 & Pb(II) & \\ \hline PVA/gelatin hydrogel beads & 211.86 & Pb(II) & [29] \\ \hline PVA/CS/GO hydrogel beads & 162 & Cu(II) & [39] \\ \hline Fly-ash-based SBA-15 & 131.00 & Pb(II) & [40] \\ \hline Olive stone & 5.88 & Pb(II) & [41] \\ \cline{2-4}  & 7.33 & Cd(II) & \\ \hline Bael tree leaf powder & 4.065 & Pb(II) & [42] \\ \hline Pomegranate peel & 1.32 & Cu(II) & [43] \\ \hline Pink bark & 11.94 & Cu(II) & [44] \\ \hline Monolithic xerogel & 0.585 & Cu(II) & [45] \\ \hline Silica gel with SGA & 0.22 mmol/g & Cu(II) & [46] \\ \hline Magnetic nanocomposites & 4.11 & Cd(II) & [47] \\ \hline Silica-Coated Magnetic Nanocomposites & 14.9 & Pb(II) & [48] \\ \hline \multirow{2}{*}{Gelatin-siloxane monoliths} & 3.75 & Pb(II) & \\ \cline{2-4}  & 1.76 & Cu(II) & \\ \cline{2-4}  & 1.5 & Cd(II) & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The comparison of adsorption capacity of studied metal ions onto various adsorbents.
## 5 Experiments and Results
### Performance Evaluation
#### 5.2.3 Performance Overhead Evaluation  
The embedded system has a relatively simple structure and limited performance. The introduction of security mechanisms will cause the performance of embedded systems to decrease. The embedded processor runs these benchmarks in turn, and records the average Cycles Per Instruction (CPI) of the processor to evaluate the performance overhead caused by the HMM. Table 8 lists the average CPI of these 10 benchmarks with and without HMM, and with the corresponding performance overhead. It can be seen from Table 8 that for each benchmark, the performance overhead caused by HMM is less than 9%, with an average value of 6.18%.  
There are two factors that cause performance overhead:  
* The LHash algorithm. After receiving all the instructions of a BB, it needs three clock cycles to calculate the digest (LHashDynamic). Therefore, the HMM requires more clock cycles to check the data integrity of each BB.
* The search for BB reference information. When the program is running, the HMM needs to search for the corresponding reference information in the Integrity Parameter Memory according to the start address of the currently executed BB. The larger the size of the HIREF, the longer the search time required.  
To reduce the performance overhead, embedded system developers can use the hash functions with faster calculation speeds. However, these methods will inevitably increase on-chip resource overhead. At present, most of the hash functions with faster calculation speeds adopt the parallel structure, and their hardware implementation cost is much higher than that of the LHash algorithm [1].
# Burst Channel Error Reduction Based on Interleaving for Efficient High-Speed Wireless Communication
###### Abstract
Then, Section 3 describes the proposed interleaver. In addition, the proposed system model is described in Section 4. The simulation results are given in Section 5. Finally, the conclusions are presented in Section 6.