esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Industroyer
#### 4.1.2 Technical Analysis of Industryer  
In analyzing the Industryer malware, KERNEL32.DLL was identified as a critical exploitation module within energy infrastructure systems. Objective: The analysis aimed to understand how the KERNEL32.DLL functions were manipulated to escalate privileges and disrupt system processes, forming the basis of a targeted detection strategy against such intrusions. KERNEL32.DLL is crucial in managing essential functions in the Windows operating system. While direct execution logs were unavailable, tools such as OlyDbg and IDA revealed how Industryer leveraged specific functions within KERNEL32.DLL. Malware samples retrieved via Hybrid Analysis further aided this investigation. The system image analysis using OlyDbg confirmed that KERNEL32.DLL was loaded and used functions such as PssDuplicateSnapshot, PssFreeSnapshot, PssQuerySnapshot, and PssWalkMarkerCreate. By exploiting these functions to manipulate system processes and escalate privileges, Industroyer disrupted operations within the target systems. * Implementing AI-based anomaly detection systems to monitor the abnormal use of KERNEL32.DLL functions and detect suspicious activities before the malware can execute its attack. * Network segmentation to isolate critical system components and prevent malware from spreading across systems. * Real-time monitoring of KERNEL32.DLL functions to ensure immediate detection and response to any abnormal activities, mitigating potential disruptions. Figure 8 visually illustrates the activation of the KERNEL32.DLL module within a system infected by Industroyer, highlighting key functions such as PssDuplicateSnapshot and PssFreeSnapshot that the malware utilizes to manipulate system processes. Detailed analysis using OlyDbg provided critical insights into how these functions were exploited by the malware. This allowed security analysts to understand how the malware leveraged these system-level functions to disrupt energy infrastructure operations. Additionally, analysis of malware samples obtained through Hybrid Analysis provided a deeper examination of its internal structure and behavior, offering valuable insights into how Industryer targets and manipulates system processes to achieve its objectives.  
Figure 9 illustrates the increasing risk levels associated with each stage of the Industryer attack, beginning with malware infiltration, followed by snapshot replication and information gathering, progressing to vulnerability identification, and culminating in system takeover and manipulation. As depicted in the figure, the darker the color of the bars, the higher the associated risk level.  
Figure 8: KERNEL32.DLL utilization in Industryer malware operations.  
Figure 9: Industryer malware risk level graph.
# Internet of Bikes: A DTN Protocol with Data Aggregation for Urban Data Collection
## 1 Introduction
We show that it is however not necessarily the best solution. In particular, our second main contribution is to evaluate the potential gain provided by data aggregation mechanisms that leverage the relaying of data among bikes. Data aggregation approach is a vast research domain of wireless sensor networks [12]. Combining several data from potentially different sensors into a single packet reduces the number of actual data transmissions, hence saves energy and improves on the network throughput. Depending on whether it can alter the data, there might be a trade off between the intensity of aggregation and the data fidelity. We propose three variants of IoB-DTN: IoB based on spatial aggregation (IoB-SA), IoB based on temporal aggregation (IoB-TA) and IoB based on spatiotemporal aggregation (IoB-STA). Through extensive evaluation by simulating real world data based urban scenarios, we derive the following engineering insights. * Sharing packets among bikes improves on the performances, but there is no need to flood the network. * Nevertheless, the buffer management policy of each bike has to give priority to its own packets over the relayed ones to obtain better delivery rates. * There is an obvious trade-off between delivery ratio and average delivery delay. * Long range low power technologies, e.g., LoRa or NB-IoT, are a serious challenger to multi-hop networks when energy is very constrained and throughput can be sacrificed. * Taking advantage of the bike to bike transmissions with data aggregation mechanisms, in particular spatiotemporal aggregation, improves dramatically the performances of the protocol. The rest of this paper is constructed as follows. Section 2 outlines the related work. Section 3 describes our scenario. Section 4 introduces IoB-DTN. Section 5 presents our simulation settings. The performance evaluation of IoB-DTN is depicted in Section 6. Section 7 presents the performance comparison between the multi-hop IoB-DTN and the low-power wide-area network technology, LoRa type. Section 8 dedicated to the performance improvement evaluation of IoB-DTN based on data aggregation approach. Section 9 concludes the paper.
## 4 Experimental Results and Analysis
### Experiment Case I
Bright red signifies that in each run the marker was consistently found. Blue means that the marker did not appear in any solutions. To validate and compare the performance of this approach, we used Windows QTL Cartographer (WQTLC) Ver. 2.5 [36] that uses a Bayesian Interval Mapping approach. The comparative results are shown in Table 2. The results show that MOEA method improves the performance in every category. Identification of correct markers is consistently better than Cartographer, with the best improvement for smaller data sets. Also the MOEA approach has lower rate of detection of extraneous linked QTL and does not predict extraneous unlinked QTLs. We performed the two-sample Kolmogorov-Smirnov test to compare the results obtained by our MOEA method against the WQTLC results. This test quantifies the distance between two results sets and analyzes the null hypothesis that the two sets are from the same distribution. This test was performed for all corresponding data elements in Table 2; using 1% and 5% significance levels, the null hypothesis was rejected for all the tests.
## 2 Materials and Methods
### EUV Full-Field Microscope  
In this system, Ar plasma radiation was collected and spectrally filtered using an ellipsoidal off-axis mirror coated with Mo/Si multilayers. This mirror was employed as a condenser (Reflex S.R.O., Prague, Czech Republic--substrate and IOF, Jena, Germany--coating). The condenser was designed and manufactured in order to reflect the radiation at the wavelength of 13.5 \(\pm\) 0.5 nm at 45\({}^{\circ}\) incidence angle in off-axis geometry. To eliminate longer wavelengths emitted from the Ar plasma (at wavelengths \(\lambda\) > 16 nm) a 250 nm thick zirconium filter (Lebow Co., Goleta, CA, USA) was used. The sample was imaged using a Fresnel Zone Plate (FZP) objective (diameter 200 \(\upmu\)m, number of zones 1000 and outer zone width \(\Delta r\) = 50 nm, made in 200 nm thick PMMA on top of a 50 nm thick Si\({}_{3}\)N\({}_{4}\) membrane (Zoneplates Ltd., Claverings Industrial Estate, London, UK) onto the CCD camera (Andor, iKon-M DO-934-BN camera, Andor Technology Ltd., Belfast, UK). As in the case of the SXR microscope, an incoherent illumination was provided by matching the numerical aperture of the condenser and of the FZP objective. The geometrical magnification of the objective was 410\(\times\), however, it can be easily changed by adjusting the FZP-CCD distance. Such a system, in this configuration, is capable to achieve a half-pitch spatial resolution of 48 nm [38]. More details about this system can be found in [39]. The photograph of this system is depicted in Figure 2b.
## 6 Recommendations
### Adopt Collection-Based Processing  
A collection, in the context of this discussion, relates to all products downstream of the rawest form of the main input data (telemetry), produced sequentially by a given entity. Collection-based processing means that the entire data collection from an instrument is processed whenever an upgrade is necessary, and not just a part of it. Minor changes to a collection can be referred to as updates, while reprocessing of the entire collection can be considered upgrades. This approach has been used with MODIS data since its inception, has recently been adopted by USGS EROS for Landsat data, and is under consideration for Sentinel 2 as the system matures. This approach also implies that reprocessing of data is done on an infrequent basis and only when necessary to ensure data quality. The significant benefit to users is that they can always have access to a complete data set from any instrument and be assured of incorporation of the latest enhancements into the entire data set.  
For the specific sensors being considered, the panel recommended that Sentinel 2A/B join Landsat and adopt collection-based processing. However, this approach can also be recommended for sensor systems in general. One last recommendation on this topic is that, if possible, Landsat and Sentinel 2 should coordinate their collection upgrades so they occur at the same time.
# Review of Land Surface Albedo: Variance Characteristics, Climate Effect and Management Strategy
To address these problems, many climate intervention strategies have been implemented to offset anthropogenic climate change, especially for global warming [19; 20]. At present, several studies have reviewed surface albedo in some directions. First, the primary definition [21], algorithm and product [13] and surface validation [22; 23] were reviewed. Then, several studies focused on the variance characteristics over main land cover types, including forest [24], urban [25], snow-covered [26; 27] and ice-covered areas [26; 28]. In addition, the effect of surface albedo on climate responses was also summarized, including its application in climate simulation [2] and the relationship between surface albedo and CO\({}_{2}\) emission [29]. Moreover, studies on surface albedo in a special ultraviolet (UV) waveband were also reviewed [30]. However, reviews of a single land type generally lack an overall understanding of surface albedo for research, and increasing attention has been placed on the albedo-induced climate feedback under the background of global warming [26; 31] and corresponding albedo strategies for climate mitigation [20; 32] in recent years. Thus, it is necessary to provide an overview of surface albedo over various land types and its impact on climate change as well as management methods, which will enhance the application potential of surface albedo in climate governance for both researchers and decision makers. In this paper, we present a comprehensive literature review of the variance pattern of surface albedo over typical land types and special objects, subsequent effects on climate, and albedo management strategies for mitigating these undesirable effects. First, several definitions related to \"albedo\" are introduced to avoid confusion and distinguish surface albedo from other research objects, and a literature analysis is presented. Second, we review the spatiotemporal patterns of typical land covers, mixed types and special surfaces. Then, short-to-long-term albedo monitoring and the application and management of these monitoring techniques are summarized. In addition, the main findings, some inconsistencies, and future work are discussed, and our conclusions are presented.
# Electromagnetic Field-Aware Radio Resource Management for 5G and Beyond: A Survey
## 4 EMF Exposure Level and 5G Technologies
### EMF Evaluation Methodology  
The evaluation methodology for EMF exposure is defined in international standards, such as IEC 62232 [46] and ITU K.91 [70]. These standards provide guidelines for assessing the EMF levels emitted by individual base stations (BSs) and BS sites through either calculations or measurements. The main objective of these evaluation guidelines is to determine the compliance boundaries of cellular BSs and devices in terms of EMF exposure. Typically, simplified models such as the free-space formula and time-averaged antenna patterns are used to calculate exclusion zones, which may not accurately represent the spatial distribution of EMF levels across the entire site area. Alternatively, field measurements can be conducted using operational 5G sites to evaluate EMF exposure [45; 71]. However, this approach presents challenges such as site and equipment availability, complex parameters of BSs and devices, and dynamic traffic and multi-user conditions [69]. Furthermore, conducting fine-grained analysis for the entire site area through measurements is not feasible, leading to significant variability in EMF levels between different locations in urban scenarios. In such cases, deterministic techniques like ray-tracing offer a promising approach for effectively evaluating EMF exposure. EMF evaluation metrics depend on two main measurement approaches, i.e., downlink and uplink measurements. Figure 4 presents types and examples of EMF exposure evaluation categories and common metrics.
## 1 Introduction
### Estimation of Annual Heat Gain and Cooling Load  
The degree-hours or degree-days process is one of the most common techniques for estimating the annual transmitted load for external walls, which is utilized to determine the amount of energy needed to cool or heat. For a base temperature \(\mathrm{T_{b}}\) and daily mean outdoor air temperature \(\mathrm{T_{o}}\), the annual cooling degree-days (CDDs) is calculated by  
\[CDD=\sum_{days}\left(\mathrm{T_{o}}-\mathrm{T_{b}}\right)^{+} \tag{1}\]  
The \(CDD\) values employed in this study have been calculated for a base temperature of 18.3 \({}^{\circ}\)C [34].  
The heat loss in the exterior wall's unit surface is measured by [35]  
\[q=U\times\Delta T \tag{2}\]  
where \(U\) is the overall heat transfer coefficient and \(\Delta T\) is the difference between the outside air temperature and the inside ambient temperature. Hence, using \(U\) and the degree-day value, the annual heat gains and losses through a unit surface can be determined by [35], as follows:  
\[\mathrm{Q_{year,c}}=86,400\times CDD\times U \tag{3}\]  
where  
\[\mathrm{U}=\frac{1}{\mathrm{R_{o}}+\mathrm{R_{i}}+\mathrm{R_{\mathit{in}}}+ \mathrm{R_{\mathit{in}}}.} \tag{4}\]  
\(R_{\mathit{in}}\) is the thermal transfer resistance of wall layers without insulation, and \(R_{o}\) and \(R_{i}\) are the heat transfer coefficients of the outside and inside environments, respectively. \(R_{\mathit{in}}\), is the thermal resistance of the insulation materials, which is measured by [35], as follows:  
\[R_{\mathit{in}}.=\frac{x}{k} \tag{5}\]  
\(x\) and \(k\) are the thickness and thermal conductivity of the insulation materials, respectively. The non-insulated wall layer's total resistance \(R_{w,t}\) is calculated by [35], as follows:  
\[R_{w,t}=R_{o}+R_{w}+R_{i} \tag{6}\]  
therefore, the total heat transfer coefficient can be expressed by  
\[U=\frac{1}{\mathrm{R_{w,t}}+\mathrm{R_{\mathit{in}}}.} \tag{7}\]  
Accordingly, the annual heat losses, \(Q_{\mathit{years,c}}\), and gains, \(Q_{\mathit{years,h}}\), occurring in the unit surface are determined by using \(U\) and the cooling or heating degree-day values, \(CDD\), \(HDD\), as follows [35]:  
\[\mathrm{Q_{\mathit{year,c}}}=86,400\times U\times CDD \tag{8}\]  
\[\mathrm{Q_{\mathit{year,h}}}=86,400\times U\times HDD \tag{9}\]  
The annual energy needed for cooling (\(E_{\mathit{year,c}}\)) and heating (\(E_{\mathit{year,h}}\)) is measured by [35]  
\[\mathrm{E_{\mathit{year,c}}}=\frac{86,400\times CDD}{\left(\mathrm{R_{w,t}}+ \mathrm{R_{\mathit{in}}}.\right)\times COP} \tag{10}\]  
\[\mathrm{E_{\mathit{year,h}}}=\frac{86,400\times HDD}{\left(\mathrm{R_{w,t}}+ \mathrm{R_{\mathit{in}}}.\right)\times\eta} \tag{11}\]  
where \(COP\) is the coefficient of performance of the cooling system (dimensionless) and \(\eta\) is the Efficiency of the combustion system.
# Intelligent Office Lighting Control Using Natural Light and a GA-BP Neural Network-Based System
## 4 Simulation
### Estimated Energy Savings  
This section presents an analysis of energy savings achieved by implementing the intelligent lighting control system in both the day and night modes. The LPD value is an important metric for evaluating energy consumption in lighting systems, defined as follows:  
\[LPD=\frac{P_{M}}{S}, \tag{11}\]  
where P\({}_{\text{M}}\) is the total power of lighting fixtures, and S is the office area.  
In the absence of a dimming system, independent of natural light availability and occupancy levels, the total power rating of the office is as follows:  
\[P=6\times 40=240\;\text{W}, \tag{12}\]  
With the control system, the power is increased as follows:  
\[P_{i}=(K_{1}+K_{2}+K_{3}+K_{4}+K_{5}+K_{6})\times 40, \tag{13}\]  
Substituting the dimming factor in day mode into Equation (13), we obtain the following:  
\[P_{1}=(0+0.40+0.97+0+0.46+0)\times 40=73.2\;\text{W}, \tag{14}\]  
Similarly, in night mode, substituting the dimming factor matrix into Equation (13), we obtain the following:  
\[P_{2}=(0.34+1+1+1+0.22)\times 40=182.4\;\text{W}, \tag{15}\]  
For a known P, P\({}_{1}\), and P\({}_{2}\), office area S = 48 m\({}^{2}\). Using Equation (11), without any control system, LPD = 5 W/m\({}^{2}\). With control system, LPD = 1.53 W/m\({}^{2}\) in day mode, and LPD = 3.8 W/m\({}^{2}\) in night mode.  
Figure 9: 15 June 2023 night at 9 o ’clock, sunny day, with blinds, algorithm optimization, indoor 0.75 m space illumination.  
The implementation of the intelligent lighting control system not only enhances comfort and uniformity in lighting but also demonstrates significant energy savings, as evidenced by the decreased LPD values across both day and night modes.
## 4 Experiments and Analysis  
### Datasets and Implementation Details  
In the scenario of weather adaptive detection, we used Cityscapes [26] as the source dataset. Cityscapes is a semantic segmentation dataset consisting of 2975 training images, 500 validation images, and 1525 testing images. Each image was annotated at the pixel level and could be used for target detection tasks after conversion. The images in the dataset were all urban scenes of different cities under normal weather. We used Foggy Cityscapes [26] as the target dataset, which was created by adding synthetic fog into the Cityscapes dataset; therefore, the annotation information was exactly the same as the original Cityscapes dataset.  
Our method was implemented on Pytorch 1.8.1. All our models were trained and tested using NVIDIA RTX3090 GPUs. We used the Adam optimizer for training and \(3\times 10^{-4}\) as the initial learning rate with the cosine \(l_{r}\) schedule. The learning rate of the last epoch decayed to 0.12 of the initial learning rate. The number of total epochs was 80. The size of the input image of our model was very large: the long side of the image was 1504 pixels, which led to the batch size being only 4.
# In Situ Study of Surface Morphology Formation Mechanism During Laser Powder Bed Fusion
## 3 Results and Discussion
### Surface Morphology Evolution
#### 3.2.2 Formation of Transitional Surface  
The overall morphology of the transitional surface is shown in Figure 7(b1,b2), characterized by significant differences in surface roughness on the same printed layer, where the first half exhibits a rough surface with poor quality, while the subsequent part of the surface is relatively smooth. Figure 11 presents the statistical trends of the melt pool temperature and high-temperature area over time for two transitional surface samples during the processing under SED conditions of 62.5 Jmm\({}^{-2}\) and 70 Jmm\({}^{-2}\). Analyzing the temperature curves and melt pool temperature image trends, it can be observed that as the printing process proceeded, the continuous accumulation of heat within the sample led to an increase in the melt pool temperature and a significant expansion of the high-temperature area [24; 32]. This thermal accumulation effect increased the degree of metal powder melting, expanded the liquid phase area of the melt pool, and promoted the transition of the sample surface from a balling state to a more uniform surface state, thereby forming a transitional surface with significant differences in surface morphology between the front and back regions [24]. After multi-layer stacking, the transitional surface often formed into an undulating surface (Figure 8b). With the continuous accumulation of heat, the area of the smooth region in the transitional surface increased layer by layer, potentially expanding to cover the entire  
Figure 10: Formation process of local features on reticulated surface: (**a**) layer-by-layer aggregation of spherical solidification particles, (**b**) principle of edge connection between adjacent spherical solidification particles, and (**c**) formation process of reticulated structures and local large-scale agglomerated protrusions.  
printed layer surface. However, due to the insufficient surface quality of the base layer, the interlayer bonding quality of such samples was relatively low, and the surface of the top layer exhibited distinct undulation characteristics, resulting in a surface quality inferior to that of a uniform surface.
# Improving Polar-Coded SCMA System by Information Coupling and Parity Check
## 6 Performance Evaluation
### Complexity Analysis  
For a PC-SCMA system, the complexity (CB-based) can be given by \(\overline{l}\big{(}O\big{(}J_{d}M^{d_{f}}\big{)}+\overline{L}O\big{(}N_{m}\log_{2} (N_{m})\big{)}\big{)}\), where \(\overline{l}\) is the average number of iterations of JIDS, \(d_{f}\) is the degree of the SCMA function nodes and \(\overline{L}\) is the average list size per iteration of CB (including feed-back decoding of PIC polar code). Additionally, the list size of the SCAN algorithm is treated as 1. Thus, the complexity of PC-SCMA system can be represented by the average number of iterations \(\overline{l}\) and the average list size \(\overline{L}\), e.g., the complexity of all kinds of PC-SCMA systems for \(K=100\) and \(R=1/2\) over AWGN channel is summarized in Table 3.  
On the basis of JIDS algorithm, at a low \(E_{b}/N_{0}\), since the high block error rate, a large number of CBs are decoded with large list size. From Table 2, compared with the uncoupled PC-SCMA with SCL decoder, PIC PCCA (or CA)-PC-SCMA has the higher complexity. Especially, the PIC CA-PC-SCMA system has the highest complexity among other PC-SCMA systems. At a high \(E_{b}/N_{0}\), the probability that most CBs can be decoded with a smaller list size in the feed-forward decoding process increases. Therefore, the complexity of the PIC PCCA (or CA)-PC-SCMA system decreases gradually. In particular, with the help of parity check for the base code, PIC PCCA-SCMA can be decoded with fewer outer-loop iterations. Thus, with SCL decoder, the complexity of PIC PCCA-PC-SCMA is lower than that of uncoupled PC-SCMA. By the way, although the PC-SCMA presented in [17] has the lowest complexity, its performance is far from that of other PC-SCMA schemes.