esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Comparison of PV Potential in Individual Sections
#### 3.2.2 Comparison of PV Potential in Two Sections with Different Azimuth (Same Slope)  
The PV potential of each module was compared and analyzed in two sections with the same slope but different azimuths. In the cases of sections 3 and 4, where the driving directions are opposite (180\({}^{\circ}\) difference), and sections 7 and 8, where the azimuths differ by 90\({}^{\circ}\), are presented.  
Figure 9 shows the modules' PV potential based on travel time in sections 3 and 4, which were selected as the comparison target sections because both roads are flat and the driving directions are opposite (180\({}^{\circ}\) difference). Module A exhibited the same PV potential in both sections because the inclination was 0\({}^{\circ}\); hence, the azimuth was not defined in the two flat sections regardless of the moving time. As the solar radiation varies throughout the day, the PV potential differs depending on the time of movement (in the order 12:00, 9:00, and 15:00). The azimuths of modules C and D in section 3 are northwest and southeast, respectively, and the azimuths of modules C and D in section 4 are southeast and northwest, respectively; hence, the PV potential exhibits an opposite trend. Therefore, the PV potential of module C in section 3 is approximately equal to that of module D in section 4.  
Figure 9: Comparison of PV potentials of four modules by time in section 3 and section 4 in opposite azimuth (Korean labels in the map indicate the building name).  
Figure 10 shows the PV potential of the modules based on travel time in sections 7 and 8. The two sections were selected as the comparison target sections because the driving directions were flat and orthogonal. At 12:00, the PV potential and insolation were maximum. At 9:00, the PV potential of module B was higher in section 8 compared to section 7 because in section 8, the module faces southeast toward the sun, whereas it faces southwest in section 7. For module C, the PV potential was low because it did not face the sun in either section at 9:00. For module D, the PV potential was high in section 7 because it faced the direction of the sun. Contrastingly, in section 8, the PV potential was low because it faced northeast. At 12:00, the sun is in the south, and module B faces southwest and southeast in sections 7 and 8, respectively. As the angle of incidence of sunlight with module B in the two sections are almost similar, the PV potential is approximately equal. For module C, the PV potential is small in section 7 because it faces the opposite direction to the sun, whereas in section 8 it faces southwest; hence, the power output potential is relatively high.  
The results indicate that the amount of power output expected for each module is significantly different based on the relationship between the position of the sun and vehicle's moving time, and the azimuth of the module and driving path.
# Systematic Error Correction for Geo-Location of Airborne Optoelectronic Platforms
###### Abstract  
In order to improve the geo-location accuracy of the airborne optoelectronic platform and eliminate the influence of assembly systematic error on the accuracy, a systematic geo-location error correction method is proposed. First, based on the kinematic characteristics of the airborne optoelectronic platform, the geo-location model was established. Then, the error items that affect the geo-location accuracy were analyzed. The installation error between the platform and the POS was considered, and the installation error of platform's pitch and azimuth was introduced. After ignoring higher-order infinitesimals, the least square form of systematic error is obtained. Therefore, the systematic error can be obtained through a series of measurements. Both Monte Carlo simulation analysis and in-flight experiment results show that this method can effectively obtain the systematic error. Through correction, the root-mean-square value of the geo-location error have reduced from 45.65 m to 12.62 m, and the mean error from 16.60 m to 1.24 m. This method can be widely used in systematic error correction of relevant photoelectric equipment.  
airborne optoelectronic platform; error analysis; Monte Carlo analysis method; systematic error correction
# High-Gain Multi-Band Koch Fractal FSS Antenna for Sub-6 GHz Applications
## 3 Results and Discussion
### Simulated and Measured Reflection Coefficients  
The reflection coefficients of the simulated and measured proposed circular Koch fractal antenna design without the FSS and with the FSS are compared in Figure 14a,b. The proposed antenna's reflection coefficients are measured using the Keysight vector network analyzer (N9916A) separately. The measurement is conducted for a frequency sweep of 2 GHz to 14 GHz. They are found to be in excellent agreement in the lowest band, while some bandwidth variations are noticed in the middle and upper bands. In the lowest band, without the FSS, the resonance is measured at 3.38 GHz with a measured bandwidth of 2.90 GHz to 4.62 GHz, while, in the lowest band, with the FSS, the resonance is measured at 3.50 GHz with a measured bandwidth of 2.83 GHz to 4.64 GHz. These bandwidths are sufficient to cover the three most important bands (n77, n78, and partial n79) of the sub-6 GHz FR-1 range. In the second band, the variations in the measured bandwidth and the resonance frequencies are obvious because the antenna was fabricated on a low-cost FR-4 substrate that is suitable for a frequency range of up to 6 GHz. Meanwhile, above 6 GHz, it will show dispersion loss and frequency nonlinearity effects in its relative permittivity and loss tangents. Therefore, the overall losses of these materials increase above 6 GHz, and this will cause errors in the measurement process. According to the proposed antenna spectrum, as displayed in Figure 14a,b, the proposed antenna has tri-band performance with the ranges of (2.87-4.62 GHz), (6.99-8.81 GHz), and (10.22-10.92 GHz). This implies that the suggested circular Koch fractal antenna with an FSS is suitable for Wi-MAX (3.5 GHz), n77 (3.3-3.8 GHz), n78 (3.3-4.2 GHz), and the partial n79 band from 4.4 GHz to 4.62 GHz, as  
Figure 13: FSS loading effect on miniaturized Koch fractal antenna: (**a**) antenna radiator (top view), (**b**) antenna ground (bottom view), (**c**) FSS structure, and (**d**) antenna prototype model.  
n79 has a band range of 4.4-4.99 GHz. In the lower mid-band frequency ranges combined, the antenna covers the C-band, which ranges from 4 GHz to 8 GHz, with an existing notch band ranging from 4.62 to 6.99 GHz. Above 6 GHz, the low-cost FR-4 material results in dispersion in permittivity and nonlinearity in the loss tangent; thereby, the simulated and measured S11 values of the spectrum have errors above 6 GHz. However, the proposed antenna with the FSS is designed in such a way that these losses do not significantly affect the measured and simulated results in the upper frequency band (10.22-10.92 GHz), which is part of the X-band (8-12 GHz). Therefore, in the upper band range, the antenna supports the X-band. However, due to the practical limitations of FR-4, above 6 GHz, the suggested antenna can no longer be used for X-band applications.  
Figure 14: Simulated vs. measured reflection coefficients: (**a**) without FSS and (**b**) with FSS.
## 2 Method
### Noise estimation  
The noise in the EL speech, which included the additive radiated noise and environment noise, is a highly nonstationary noise, so it is imperative to update the estimate of the noise spectrum frequently. This study adopted the minimum-statistics method proposed by [PERSON] and [PERSON] [21] for noise estimation, since this method is computationally efficient, robust with respect to the input SNR, and has an ability to quick follow any abrupt changes in the noise spectrum. The minimum tracing is based on a recursively smoothed spectrum which is estimated using first-order recursive averaging:  
\[\left|\hat{D}_{(k,l)}(\omega)\right|^{2}=\lambda_{D}\left|\hat{D}_{(k-1,l)}( \omega)\right|^{2}+(1-\lambda_{D})\left|\hat{Y}_{(k,l)}(\omega)\right|^{2} \tag{8}\]  
\(0<\lambda_{D}<1\), where \(\left|\hat{D}_{(k,l)}(\omega)\right|^{2}\) and \(\left|\hat{Y}_{(k,l)}(\omega)\right|^{2}\) are the \(k\)th components of noise spectrum and noisy speech spectrum at the frame \(l\), and \(\lambda_{D}\) is a smooth parameter. Let \(p^{\prime}(k,l)\) denote the conditional signal presence probability in [PERSON] and [PERSON] (2002)[21], then Eq. (8) implies:\[\left|\hat{P}_{(k,l)}(\omega)\right|^{2}=\hat{\lambda}_{D}(k,l)\left|\hat{D}_{(k -l)}(\omega)\right|^{2}+(1-\hat{\lambda}_{D}(k,l))\left|\hat{Y}_{(k,l)}(\omega) \right|^{2} \tag{9}\]  
where \(\hat{\lambda}_{D}(k,l)\underline{\Delta}\lambda_{D}+(1-\lambda_{D})p^{\prime} (k,l)\) is a time-varying smoothing parameter. Therefore, the noise spectrum can be estimated by averaging past spectral power values. For more descriptive details of this algorithm, the reader is referred to [21, 22].
# Recent Advances in Optical Detection of Aminoglycosodies
## 3 Colorimetric Assays
### Discussion  
Colorimetric assays have many advantages such as their low cost, rapidity, and the fact that there is no need for special equipment. In some situations, naked-eye observation can achieve semi-quantitative detection. So, the colorimetric assays are powerful tools for POCTs for AGs. Gallic acid-functionalized Ag NPs and chlortetracycline-functionalized Ag NPs exhibited good sensitivity for the detection of kanamycin (0.12 nM) and streptomycin (0.036 nM). However, other colorimetric assays suffer from poor sensitivity. In addition, a non-specific interaction between the target AG and recognition element immobilized on the surface of metal NPs was employed for the recognition of AGs. A non-specific interaction may induce poor selectivity in the proposed method because there are a large number of interferents contained in the real samples, which may also have non-specific interactions such as hydrogen bond interactions and electrostatic interactions with recognition molecules on the surface of metal NPs. To remove these interferents, sample pretreatment is needed.
## 3 Proposed Methodology
### Flow Table Update at the Controller
Thus, in Line 16, source node addresses (\"src_node_addresses\") are mapped using the current hop ID (\"current_hop\") and link type between the current hop and the next hop (\"current_hop_link_type\"). Thus, the packet will be forwarded through the communication channel that exists between the current hop (the source node) and the next hop. The addresses of the next hop to be modified as the source header fields before forwarding the packet depend on the link type that exists between the next hop and the next hop of the next hop. Thus, Line 17 maps the next hop's addresses (\"next_hop_addresses\") corresponding to the next hop ID (\"next_hop\") and the communication channel that  
Figure 12: A sample routing path of the adjacency list \(P_{8}[3]\), depicting node ID and link types. exists between the next hop and the next hop of the next hop (\"next_hop_link_type\"). The algorithm also inspects the destination node ID (\"\(S\)\") and the link type that exists between the hop before the destination and the destination (\"destination_link_type\"), and maps the corresponding addresses from the topology database at the controller to retrieve \"destination_addresses\", as evident from Line 18 of Figure 2.  
Then, the flow table is updated to conditionally forward the packet using the \"Update_flow_table_conditional_forward\" function, as shown in Line 19. It updates source node addresses (\"src_node_addresses\") and destination addresses (\"destination_addresses\") for the rules field of the flow table. Next hop addresses (\"next_hop_addresses\") will be set as an instruction in the action field to conditionally forward a packet through the source port if the route valid timestamp is greater than the present timestamp, and, if not, send a packet_in message to the controller, and the route valid timestamp will be set by adding the current timestamp to the \"valid_time\". Finally, the element of the \"visited\" array corresponding to \"current_hop\" will be set as true, as shown in Line 20, to mark in the flow table related to \"current_hop\" that the source node and destination node \(S\) have already been updated and are not required to be updated again.
## 1 Introduction  
### Energy Consumption of Residential Buildings in Northern China  
#### 1.1.1 Overall Energy Consumption  
Due to the rapid economic growth and increasing urbanization, China has become the largest energy consuming nation, accounting for 23.6% of global energy consumption in 2018 [1]. Energy consumption in China has increased from \(1.4\times 10^{9}\) tons of standard coal equivalent in 2000 to \(4.1\times 10^{9}\) tons of standard coal equivalent in 2017 [2]. Amongst all energy uses, the building sector accounts for 31.4% of the total [3]. The International Energy Agency (IEA) also announced that the total energy consumption in China would be 170,277 PJ or \(5.81\times 10^{9}\) tons of standard coal equivalent by the year 2030, and energy use in the building sector would account for 44,547 PJ. In recent years, residential buildings accounted for the majority of newly constructed buildings in China, with approximately 70% to 80% of the total built areas in 2015-2019 [4; 5].  
By 2017, 14.0 billion m\({}^{2}\) of buildings had been installed with centralized heating systems in the cities of northern China [6]. According to the standard _GB50178-93_ released by the Housing and Urban-Rural Development Ministry of the People's Republic of China, there are five climate regions which are established to classify the thermal technical designs of buildings in China [7]. These five climatic regions are the Severe Cold regions, the Cold regions, the Temperate regions, the Hot Summer and Cold Winter regions, and the Hot Summer and Warm Winter regions [7]. The large part of Northern China, which accounts for about two-thirds of the total area of mainland China, is located in Severe Cold and Cold regions (Figure 1) [8]. Due to the poor-quality building thermal performance and the requirements for higher indoor temperatures, heating energy consumption reached accounted for 44.7% of total residential energy in these areas in 2017 [9].
# IoT and UAV Integration in 5G Hybrid Terrestrial-Satellite Networks
## 2 IoT Technologies  
The IoT refers to the data exchange between a large amount of devices and sensors connected wirelessly without human interference. It can be used in various fields such as smart metering, smart buildings, and environmental monitoring. The number of IoT connected devices installed worldwide has increased exponentially from 15.41 billion in 2015 to 23.14 billion in 2018, and it is expected to reach 75.44 billion in 2025 [6]. IoT wireless technologies can be divided into two categories: short and long range. Table 1 contains a summary of short-range solutions and of long-range brand-defined approaches available on the market and also identified as commercial solutions in the following. Long-range commercial solutions shown in Table 1 share the common network architecture in Figure 1. IoT devices transmit their data to a brand-defined gateway through a technology acting at the data link and physical layers where IoT application data are directly encapsulated. All involved protocols are strictly brand defined. Each gateway is a protocol converter encapsulating the private IoT application into an Internet protocol stack to be conveyed to the IoT cloud where a brand-defined server will apply an application layer conversion from the IoT application to an Internet application protocol (such as HTTP) suitable for end users. \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Specifications** & \multicolumn{3}{c}{**Short Range**} & \multicolumn{3}{c}{**Long Range**} \\ \hline  & **Bluetooth** & **ZigBee** & **WIFI** & **LoRaWAN** & **SigFox** & **Ingenu** \\ \hline Modulation & GFSK/DQPSK/DPSK & BPSK/QQPSK & various schemes & Chirp Spread Spectrum & DBRSK(UL) & RPMA-DSSS(UL) \\  & & & (CSS) & GFSK(DL) & CDMA(DL) \\ \hline MAC & FDMA/TDMA & CSMA/CA & CSMA/CA & unsoluted MAC & unsoluted ALOHA & CDMA-like \\ \hline Data rate & 3 Mbps & 250 kbps & 7 Gbps & 0.3 kbps–50 kbps & 100 bps(UL) & 78 kbps(UL) \\  & & & & & 600 bps(DL) & 19 kbps(DL) \\ \hline Coverage & up to 30 m & up to 100 m & up to 100 m & up to 5 km (urban) & 10 km (urban) & up to 15 km \\  & & & & 15 km (rural) & 50 km (rural) & (urban) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison between short-range and long-range technologies [7, 8]. In parallel with private commercial investments, plenty of standardization efforts in the IoT environment are promoted by various standardization bodies and special interest groups such as the Institute of Electrical and Electronics Engineers (IEEE), the Third-Generation Partnership Project (3 GPP), WEIGHTLESS-SIG, the European Telecommunications Standards Institute (ETSI), and the DASH7 Alliance. All the proposed solutions fall in the category of the so-called Low Power Wide Area protocols (LPWA) [7]. Table 2 summarizes the different technical specifications of the solutions created by the aforementioned groups. The architecture of these technologies is similar to the one presented in Figure 1, but the brand-defined gateway is substituted by a base station of the cellular network.
## 7 Discussion, Limitations, and Future Research Trends
### Limitations  
1. Our literature review primarily focuses on the detection of pedestrians, vehicles, and road lanes, which may not encompass all possible objects and scenarios relevant to AVs in adverse weather conditions. There may be other critical elements, such as traffic signals or animals, that warrant further investigation.
2. The detection algorithms discussed in our review may have inherent limitations, such as difficulties in detecting small or occluded objects, which could impact the accuracy and reliability of AVs in certain situations.
3. Our study primarily considers a range of adverse weather conditions, but there may be other environmental factors, such as dust storms or heavy fog, that were not extensively covered in the reviewed literature.
4. The field of AV technology is rapidly evolving, and new detection algorithms and sensor technologies may emerge that could significantly impact the findings of our review. Our study may not capture the most recent advancements in this area.
5. While our study includes datasets that simulate adverse weather conditions, the simulation environments may not perfectly replicate real-world driving scenarios. The complexity of real-world conditions, including unpredictable human behavior and dynamic traffic patterns, can introduce additional challenges not fully captured in simulated datasets.
6. The ethical considerations and societal acceptance of AVs, especially in challenging conditions, are not addressed in our study. Public trust and the ethical use of AV technology are essential factors for their successful integration into smart cities.  
Figure 15: Paper distribution for one-stage and two-stage detectors.
## 4 Experiments
### Experiments on Benchmark Datasets
#### 4.1.2 Results and Discussion  
We perform independent Monte-Carlo simulations under the preset parameters. We take the mean value of classification error rates as the final result to reduce accidental error. The classification error of each set is accurate to three decimal places, as shown in Table 2. It also shows the votes of the best results (Best), the first three results (Better-3) and the mean ranking (Mean-ranking) of each method for measurement.  
In addition to the intuitionistic judgment of the effectiveness of our method, we also illustrate the significant differences between our method and some other classical methods by the critical difference diagram (CDD), proposed in [47]. Figure 8 shows the CDD of eight methods above.  
According to Figure 8, BICORN-RNNs obtain the highest accuracy, located in the furthest right of the figure; it is quite remarkable that this novel structure can effectuate such excellent classification performances in several datasets. We notice that the classification accuracies of FNNs and RNNs are the lowest, and they fail to gain assessments of \"Best\" or \"Better-3\", whereas LSTMs, an improved branch of RNNs, demonstrate satisfactory performance. BICORN-RNNs gain a comprehensive victory in \"Best\", \"Better-3\", and \"Mean-ranking\".  
It is predictable that the rudimentary neural networks still fail to classify time series when faced with different characteristics. Fortunately, the fusion structure we proposed in this paper may have advantages in learning both data, and features, of the time series.
# A Fault Section Location Method for Distribution Networks Based on Divide-and-Conquer
## 2 Numerical Computational Model for Distribution Grid Fault Location
### Basic Principle of Distribution Network Fault Location
#### 2.2.3 Numerical Computational Expressions for the Switching Function  
This section equivalent transforms the logic \"OR\" operation in Formula (10) of the switching function based on the properties of the road vector and deduces the numerical calculation expression of the switching function.  
We claim that the following numerical computational relationship exists between the hypothetical fault scenario \(\mathbf{X}\) and the switching function vector \(\mathbf{J(X)}\):  
\[\mathbf{e}-\mathbf{J(X)}=\prod\limits_{x_{k}\in\mathbf{X}}^{*}(\mathbf{e}-\mathbf{P}_{k}) \tag{14}\]  
The mathematical induction method is applied for the proof. Use the data structure \"Queue\" to represent \(\mathbf{X}\), \(\mathbf{X}=\{x_{k1},x_{k2},\ldots,x_{p},x_{p+1},\ldots\ \}\). Let \(\mathbf{X}_{u}\) be the queue consisting of the first \(u\) elements of \(\mathbf{X}\).  
\[\mathbf{X}_{u}=\{x_{kz}\in\mathbf{X}|1\leq kz\leq u\} \tag{15}\]  
1. When \(u=1\), by Equation (10), \(\mathbf{J(X_{1})}=\mathbf{P}_{k1}\). Obviously, \(\mathbf{e}-\mathbf{J(X_{1})}=\mathbf{e}-\mathbf{P}_{k1}\).
2. Suppose that \(\mathbf{e}-\mathbf{J(X_{p})}=\prod\limits_{x_{k}\in\mathbf{X_{p}}}^{*}(\mathbf{e}-\mathbf{P}_{k})\) when \(u=p\). When \(u=p+1\), we apply Equation (10) to expand \(\mathbf{J(X_{p+1})}\): \[\mathbf{e}-\mathbf{J(X_{p+1})}=\mathbf{e}-(\mathbf{P}_{k1}\lor\mathbf{P}_{k2}\lor\ldots\lor\mathbf{P}_ {kp}\lor\mathbf{P}_{kp+1})=\mathbf{e}-\left[\mathbf{J(X_{p})}\lor\mathbf{P}_{kp+1}\right]\] (16) Because for any 0-1 vector \(\mathbf{a}\), \(\mathbf{b}\), we have \(\mathbf{a}\lor\mathbf{b}=\mathbf{a}+\mathbf{b}-\mathbf{a}*\mathbf{b}\), \(\mathbf{a}-\mathbf{a}*\mathbf{b}=\mathbf{a}*(\mathbf{e}-\mathbf{b})\), \[\mathbf{e}-\mathbf{J(X_{p+1})} =\mathbf{e}-\left[\mathbf{J(X_{p})}+\mathbf{P}_{kp+1}-\mathbf{J(X_{p})}*\mathbf{P}_{kp +1}\right]\] (17)  Substituting the assumptions \(\mathbf{e}-\mathbf{J(X_{p})}=\prod\limits_{x_{k}\in\mathbf{X_{p}}}^{*}(\mathbf{e}-\mathbf{P}_{k})\) into Equation (17), we obtain \[\mathbf{e}-\mathbf{J(X_{p+1})}=\left(\mathbf{e}-\mathbf{P}_{kp+1}\right)*\prod\limits_{x_{k} \in\mathbf{X_{p}}}^{*}(\mathbf{e}-\mathbf{P}_{k})=\prod\limits_{x_{k}\in\mathbf{X_{p+1}}}^{*} (\mathbf{e}-\mathbf{P}_{k})\] (18)  
This proves that Equation (14) holds. By shifting the terms of Equation (14), the numerical expression of the switching function is obtained as follows.  
\[\mathbf{J(X)}=\mathbf{e}-\prod\limits_{x_{k}\in\mathbf{X}}^{*}(\mathbf{e}-\mathbf{P}_{k}) \tag{19}\]