esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Error Metrics for Different Rainfall Intensity Intervals
Additionally, IMERG-E and IMERG-L notably underestimate the contribution of heavy to very heavy rainfall to the total monsoon rainfall over the west coast, whereas IMERG-F shows better agreement with the rain-gauge-based dataset. It indicates that heavy rainfall over the complex terrain is usually underestimated by satellite-only precipitation products. Bias and RMSE in IMERG estimates for different rainfall intensity intervals as compared to the IMD rain-gauge-based dataset is illustrated in Figure 12 for all-India, central India, and the west coast. The three IMERG estimates systematically overestimate light rainfall and underestimate rather heavy to very heavy rainfall. Additionally, RMSE is the largest for light rainfall, followed by moderate rainfall intensity. Interestingly, IMERG-F shows the largest bias and RMSE compared to IMERG-E and IMERG-L over the west coast in light rainfall estimation. It indicates that IMERG estimates under-detect and overestimate light rainfall, which needs to be improved in the next release. However, there is a notable reduction in bias in the IMERG-F product as compared to IMERG-E and IMERG-L products over the west coast for rather moderate to very heavy rainfall intensities. IMERG-L has a smaller magnitude of bias than IMERG-E and IMERG-F for rather heavy to very heavy rainfall intensities for all-India and central India but has the largest RMSE. These results clearly demonstrate the regional variations in error characteristics of IMERG products associated with distinct topography and precipitation processes. Figure 11: Frequency and contribution of different rainfall intensity intervals to the total monsoon rainfall for all-India, central India, and west coast from the IMD rain-gauge-based data and IMERG version 6 estimates for 2014–2017.
# High-Quality, InN-Based, Saturable Absorbers for Ultrafast Laser Development  
[PERSON]  
1  
[PERSON]  
1  
[PERSON]  
2  
[PERSON]  
1  
[PERSON]  
1 Grupo de Ingenieria Fotonica, Departamento de Electronica (EPS) Universidad de Alcala, Campus Universitario, Alcala de Henares, 28871 Madrid, Spain; [PERSON]@gmail.  
quality as a way of enhancing its optical performance in terms of the absorption band, modulation depth, or saturation intensity [24; 25; 26].  
Among semiconductor SAs, indium nitride (InN) has demonstrated remarkable advantages in comparison to other semiconductors, due to its low bandgap energy (0.65-0.9 eV) and the possibility of extending the emission wavelength from infrared to ultraviolet region when using InN and its alloys with GaN or AlN [27; 28]. Furthermore, InN epitaxial layers have demonstrated high thermal stability and huge nonlinear behavior at telecom wavelengths [29; 30]. For this material, the reduction of the residual carrier concentration is a challenge, since structural defects and incorporation of common impurities like oxygen or hydrogen results in efficient \(n\)-type doping [28]. Recent progress in molecular beam epitaxy has led to InN epitaxial layers with a residual carrier concentration below \(10^{18}\) cm\({}^{-3}\), which results in an absorption edge energy below 0.7 eV at room temperature [30]. It is hence interesting to study this material as a saturable absorber at 1560 nm.  
In this work, we report on the application of InN layers as saturable absorbers in a laser cavity in mode-locked operation at 1560 nm. We show that a reduction of the free carrier concentration leads to an enhancement of the nonlinear optical response. We demonstrate saturable and non-saturable absorption coefficients of \(4\times 10^{4}\) cm\({}^{-1}\) and \(-3\times 10^{4}\) cm/GW, respectively, a modulation depth of 23%, and saturation attained for a power density of 4 GW/cm\({}^{2}\). This material was inserted in a fiber laser cavity, delivering pulses with an ultrashort pulse duration of 134.4 fs and energies of up to 5.6 nJ. These results make InN a promising candidate as a saturable absorber for ultrafast laser applications in the telecom range.
# Energy Cost Minimization with Hybrid Energy Storage System Using Optimization Algorithm
## 5 Conclusions
It should be noted that the impact of leaving a battery in a discharged state, which happens during winter months in the simple energy balancing, is not considered in the model. Obviously, the financial results depend on a variety of factors, such as battery performance and cost, energy usage and generation patterns and most of all energy price profiles. For this reason, future work includes analyzing different HESS operating scenarios and adjusting the optimization method to take into account additional services that the HESS can provide. The designed methods will be tested in a real environment with forecasted profiles being the basis for the optimizer. What is more, we plan to test the optimizer on the prices from the day-ahead market, where both the purchase and selling prices are changing every hour. The tool is intended to be further developed into a commercial tool for ESS installation planning and management. The tool will be modified to work with the predictions of load and production rather than with historic data, then it will become a scheduler that can be used to manage the operation of the energy storage, together with a real-time controller. Conceptualization, supervision, funding acquisition, validation, K.R. ; methodology, software, formal analysis, investigation, writing--original draft preparation W.R.; software, data curation, visualization, O.G. ; supervision, resources, review and editing, H.B., writing--review and editing, J.V. All authors have read and agreed to the published version of the manuscript. The work was financially supported by The National Centre for Research and Development via grant No. LIDER/30/0166/L-10/18/NCBR/2019. Not applicable. The authors declare no conflict of interest.
# Ultra-Wideband Analog Radio-over-Fiber Communication System Employing Pulse-Position Modulation
## 4 Experimental Results  
Two sets of experiments were performed, with and without a 20 km SMF link and BER measurements were made with and without ECC. The results are presented in Figures 9 and 10 and summarized in Table 1. For error-free demodulation, the BER is reported as \(<\)8.17 \(\times\) 10\({}^{-7}\), as lower BER values cannot be measured with the given data set size. All measurements with a time bin duration of 200 ps demonstrate error-free transmission. Error-free transmission was also achieved with both 500 ps and 200 ps pulse widths, using a time bin duration of 100 ps. Figure 10: BER measurement results for TR-PPM transmission over ARoF experiments, using PRBS, for back-to-back setup. Bars shown at BER limit 8.17 \(\times\) 10\({}^{-7}\) have 0 errors in the received data. Figure 9: BER measurement results for TR-PPM transmission over ARoF experiments, using PRBS, for setup with 20 km SMF. Bars shown at BER limit 8.17 \(\times\) 10\({}^{-7}\) have 0 errors in the received data. It can be seen that the employment of Gray code, which ensures that only one bit is flipped if the received pulse is located in the adjacent time bin, slightly decreases the BER, which is further reduced by the use of RS (255, 223) code. Employment of RS code significantly reduces the BER for measurements with the pulse width of 100 ps and time bin duration of 100 ps. For all measurements involving the time bin duration of 50 ps, the impact of RS code is negligible, which can be attributed to the high amount of flipped bits in the TR-PPM frames which carry the parity bytes. When considering time bin durations smaller than the pulse width, the effect of the additive noise is especially significant, as the detection threshold may be reached over a time span longer than the time bin duration in systems employing a timer-based TDC. Comparing measurements with and without the 20 km SMF, the back-to-back measurements result in a lower overall BER than when the 20 km fiber is connected. BER degradation in the latter case can be attributed to dispersion in the fiber, which causes degradation of the pulse shape and a reduction in the steepness of the rising edge. This, in turn, increases sensitivity to additive noise, resulting in errors in pulse position measurements by the ESTT. The results strongly agree with previously published findings [40; 41; 42] in that in PPM data transmission, pulse shape plays a vital role in the overall system performance. The presented pulse waveforms after the RF transmission show distortions not dissimilar to
## 3 Experimental Setup and Evaluation Indicators  
### Dataset  
The PCB soldering defect dataset used in this study consists of five types of defects [22], namely missing solder (MS), missing proteus (MP), solder short (SS), overturned direction (OD), and solder ball (SB) defects. MS refers to components having insufficient or no solder joints, which may cause short circuits on the PCB. MP indicates that the components are not installed in the correct position, resulting in an open circuit. SS refers to two or more adjacent solder joints of components melted together, leading to open circuits or abnormal circuit functions. OD denotes the reverse installation of chip components, which may affect the functionality of the PCB. SB represents solder balls that violate the minimum spacing requirements. Figure 5 provides examples of these five types of defects. Additionally, there are 285 instances of MS defects, 166 cases of MP defects, 92 cases of SS defects, 128 cases of OD defects, and 177 instances of SB defects.
# Positioning-Based Uplink Synchronization Method for NB-IoT in LEO Satellite Networks
## 3 Position-Aided Uplink Synchronization Method
### NPSS Detection
#### 3.1.1 Coarse Estimation of Time Delay Observations
Therefore, the resulting peak is not influenced by Doppler shift. At this point, the values \(v(m)\) corresponding to different \(m\) are similar, and the minimum among them is taken as the metric value at sampling point \(n\). * \(n=\delta+a\cdot N\), \(a=\pm 1\), \(\pm 2\cdot\ldots\ldots\pm 11\); When the current sampling point is located at an integer multiple of the OFDM symbol interval adjacent to the actual synchronization point, a peak may also appear. However, the resulting values of \(v(m)\) vary significantly across different \(m\) values. The minimum \(v(m)\) is approximately equivalent to the correlation value with uncorrelated noise \(x(n)\), resulting in a small metric value at sampling point \(n\). By examining the detection metric over all sampling points \(n\), the location of the peak indicates the coarse synchronization point \(\hat{n}_{k}\):  
\[\hat{n}_{k}=\max_{n}p(n) \tag{8}\]
## 3 Background
### Power Sub-System Overview
#### 3.2.3 Power Management and Distribution (PMAD)  
The power management and distribution system is often custom designed based on the mission objective, this however is an extremely expensive development process, typically only utilized by large organizations such as Space X and NASA with healthy budgets. Many CubeSat developers are smaller organization with smaller budgets such as universities and small corporations. These companies rely on off-the-shelf components (Figure 10) developed by a range of businesses including ISIS, Clyde Space and Pumpkin. Typically, standardized PMAD components have a main battery bus voltage of 8.2 V, with the ability to distribute regulated 5 V and 3.3 V supplies [23]. The PMAD possess the ability to manage the battery, control capacitor charging/discharging and condition and amplify the voltage signal [3].  
Figure 10: PMAD Developed by Clyde Space Industries [25].  
Figure 9: Li-Ion and Li-Po Energy Densities for Off the Shelf Products [30].  
The PMAD is a vital piece of equipment currently installed in every CubeSat launched into orbit [2]. The balance of power generation, storage, and consumption on board a CubeSat is very delicate; therefore, it is vital a suitable PMAD is selected. Every PMAD possesses junctions to transfer power from the generation source, a power conditioner, and a switching circuit to divert power to systems/instrumentation. The component is usually connected to a universal bus. PMADs are beginning to be developed with the ability to only divert power from the battery once the solar panels are no longer able to supply adequate power, preserving the electrical energy reserve for the eclipse period and the ability to \"dead switch\" [1,3].  
When the standard voltage supply from the battery is insufficient, a capacitor is typically incorporated to provide a burst of electrical energy, usually to power the payload in applications such as high-resolution imaging [3]. High-end systems can relay operational power system data back to ground control and command the inclination of the craft to maximize incident solar radiation to the Photovoltaic cells. Stras space manufacture a power converter with a voltage input range between 3.3-40 V with an efficiency of 90% [23].
# Breaking Alert Fatigue: AI-Assisted SIEM Framework for Effective Incident Response
## Appendix A Example of Security Log in CEF and JSON Format
### Using Service Port for Effective Alert Correlation  
This appendix delves deeper into the three-tuple [source IP, destination IP, service port] that is used to correlate and group alerts into events.  
In Figure 10, we present several real-life examples to illustrate why the service port is a better option than the source and destination ports in this context. Each ATG in the figure depicts alerts generated during communication between two hosts over a specific period. The \(x\)-axis of the source panel shows the time of issuance, while the \(y\)-axis represents the source port of the communication. The \(x\)-axis of the destination panel denotes the type of alerts (with detailed information omitted for clarity), while the \(y\)-axis represents the destination port. To distinguish groups of alerts with different features, we used red and blue circles. The red circles indicate groups of alerts that share the same source ports, while the blue circles highlight groups that share the same destination ports. It is clear that selecting either the source or destination port as one of the three tuples for correlation analysis cannot cover both cases simultaneously. However, by utilizing the technique proposed in Section 4.2.1 to determine the service port and using it to correlate alerts into events, we can effectively address all the cases demonstrated in the figure.
# Design of a Tri-Band Wearable Antenna for Millimeter-Wave 5G Applications
## 3 Experimental Results and Discussions  
The measured and simulated results are presented in this section to validate the performance of the proposed antenna design. The antenna prototype is fabricated on ROGERS 5880 substrate, as shown in Figure 7a. A 50 Ohm-thin block end launch connector (withwave(r)) is used to measure the return loss and radiation pattern. The maximum working frequency of this connector is 67.5 GHz [25]. The comparison between the simulated and measured return loss in shown in Figure 7b. As shown in the Figure 7b, the proposed antenna resonates at three operating modes, with operating frequencies of 28, 38, and 60 GHz, respectively. The impedance bandwidth for 28, 38, and 60 GHz are 21 GHz to 31 GHz, 37 to 39 GHz, and 56 to 61.5 GHz, respectively. There is a decent agreement between simulation and measurement results, however, the little variance is due to inaccuracies in the manufacturing and measurement equipment.
## 2 Materials and Methods
### Angular Loss Modelling  
Angular reflection losses describe power losses due to the deviation of the irradiance angle of incidence from the surface's perpendicular (normal incidence). The orientation and tilt of BIPV modules often differ from the optimal module positions typically found in PV plants, which leads to angular losses playing a significant role in the performance of BIPV systems. While STCs consider normal incidence, AOI variations continuously occur under module operation. In addition, module characteristics (surface, materials, and thicknesses) have different influences on reflectance and absorptance, which means that each PV system has a particular angular response.  
To account for the angular losses, the PV output power, \(P\), is obtained by correcting the nominal PV power, \(P_{0}\), with three angular power loss factors that are applied to the three corresponding components of irradiance \(-\)direct (beam, \(B\)), diffuse (\(D\)), and ground-reflected (albedo, \(A\))--following Equation (9):  
\[L_{\text{AOI}}=1-\frac{B\cdot\left(1-F_{\text{B}}\right)+D\cdot\left(1-F_{ \text{D}}\right)+A\cdot\left(1-F_{\text{A}}\right)}{B+D+A} \tag{9}\]
## 3 Data
### Ground Data  
The ground data are from the Heihe Watershed Allied Telemetry Experimental Research (HiWATER) experiment in 2012. The experiment area is located in the middle reaches of the Heihe River Basin, a typical inland river basin with a semi-arid climate in Northwest China (Figure 2). The land surface is heterogeneous and is dominated by maize, vegetables, orchards, and residential areas. The spatial distribution of _in situ_ Water Vapor Content measurements sites is also shown in Figure 2, including Wuxing Village (including Building 7 and Gouqu) and the Gaoya hydrological station. The details are shown on Section 3.3.2. Additional details can be found in [PERSON] _et al._[25] and [PERSON] _et al._[26].  
The _in situ_ LST is derived from the land surface (Upwelling) Longwave Radiation (Rlu) and atmospheric (Downwelling) Longwave Radiation (RId), which were observed using four-component radiation measurements. For the radiometer intercomparison in HiWATER, the four-component radiation measurements exhibit good agreement. The average RMSE and Mean Relative Error (MRE) are 6.43 W\(\cdot\)m\({}^{-2}\) and 0.62%, respectively, for RId and 3.16 and \(-\)0.34%, respectively, for Rlu [25,27]. The _in situ_ measurements are stored in 10-min intervals, and the cloud-free measurements concurrent with the satellite overpasses (approximately 3:40 UTC, 11:40 Beijing Time both for HJ-1B and MODIS) are used for comparison with the retrieved LST.  
Because of the large spatial variations, daytime _in situ_ measurement data used in previous validation studies are scarce [2,28,29]. Although the spatial and temporal variability of the LST is smaller at nighttime, satellite-derived LST products should also be validated during the daytime because the LST data are mostly required during the day, e.g., for monitoring surface heat fluxes [30]. For these reasons, only the daytime values at the homogeneous sites (14 maize sites, 1 vegetable site, and 1 orchard site) were used in this study.  
Figure 1: Spectral response function of HJ-1B/IRS in Longwave Infrared band (LWIR).
## 2 Methodology  
### Energy Band Operation Scheme  
In general, the load exhibits relatively sudden fluctuations owing to the presence of renewable customers with a massive load in the grid-connected MG. The operational efficiency can be improved, and optimization can be achieved by sharing the operational information with the DER and the demand between the DN and MG system operator (DSO and MGO). The responsibility for balancing the supply-demand problem is transferred to the DSO when the MG is grid-connected. We used an operational scheme based on an energy band to divide the responsibility for balancing the supply and demand. This concept is based on a modification of the operational scheme originating from the frequency control band in the reserve capacity market [26]. An energy band operation scheme of a tie-line flow is illustrated in Figure 1. Here, the contractual tie-line flow refers to the existing contract power between the DN and the MG. The energy band is the marginal power of the existing contractual tie-line flow. On the other hand, the rescheduled tie-line flow indicates the modified tie line flow through the energy band from the contractual tie-line flow. The DSO and MGO can regulate the cost for the tie-line flow so that it remains reasonable, ensuring stable operation of both grids.  
The rescheduled tie-line flow, \(P^{*}_{tie}\), is expressed as:  
\[P^{*}_{tie,c}-P^{EB}_{tie}\leq P^{*}_{tie}\leq P^{*}_{tie,c}+P^{EB}_{tie} \tag{1}\]  
\[P^{*}_{tie,c}(t+1)-P^{EB}_{tie}(t)-P^{*}_{tie}(t)\leq u^{*}_{tie}(t)\leq P^{*}_{ tie,c}(t+1)+P^{EB}_{tie}(t)-P^{*}_{tie}(t) \tag{2}\]  
where \(P^{*}_{tie}\) is the rescheduled tie-line flow; \(P_{tie,c}\) is the contractual tie-line power flow between the DN and MG; \(P^{EB}_{tie}\) is the size of the energy band; \(u^{*}_{tie}\) is the control signal for the tie-line flow.  
The energy band constraint (Equation (2)) is introduced under the bi-level optimization model and rescheduled tie-line flow given in (Equation (1)), to prevent sudden changes in the tie-line flow. This scheme does not provide for the additional energy cost for a breach of the contractual tie-line flow within the energy band. However, the rescheduling information should be identified by the MGO prior to changing the feedback information from \(P_{tie,c}\), because it is a schedule for the DER of the MG. This information can assist the operators in sharing the responsibility and maintaining stable operation.
# A Systematic Literature Review on Military Software Defined Networks
## 5 Additional Contributions
### Software Defined Satellite Network Applications  
Future military space operations are expected to become more complex and operate further from Earth. Hence, there is a need for autonomous networks that can configure themselves with minimal human intervention. Automation through SDN principles is a promising technology for overcoming this need. A survey of satellite communication systems by [PERSON] et al. [94] states that, for the OSI layer 1, Software Defined Radio (SDR) is expected to enable radio frequency sharing jamming detection, while traditional SDN enables flexible routing based on layers 2 and 3. Two papers have been found that discuss SDR in the satellite context [95; 96], while SDR is further discussed in Section 5.6.  
Operationally Responsive Space (ORS) is a satellite technology defined by the US Department of Defense. It is suggested to base the network on SDN, but only two papers concerning the military application domain and Software Defined satellite Networks have been found in the literature review. The two research challenges that are pointed out are orchestration and available resources. SDN in space differs from ground SDN, due to the distinct mobility patterns, and the fact that satellites can come out of reach due to their mobility patterns. However, their location can be predicted if they come out of reach [97]. Hence, an abstract network model is needed, in order to make an application that can make such predictions. Space networks have a long transport delay and limited resources that result in a flow table management problem. Hence, making small SDN flow tables is preferable, without having too many table-misses. [PERSON] et al. [98] suggested an algorithm to balance flow table size and table misses, but does not discuss any security implications about reducing the table size.