esa-sceva/satcom-chunk-collection huggingface dataset:
# Building Energy Retrofit Measures in Hot-Summer-Cold-Winter Climates: A Case Study in Shanghai
## 4 Building Retrofit Measures and Major Affecting Elements in Shanghai
### Building Retrofit Measures in Shanghai
#### 4.1.6 Energy Regeneration Systems for Lifts and Equipment  
The use of energy regeneration systems can improve the energy efficiency of equipment by about 20-30% [69]. These systems include variable-frequency and variable-voltage drive systems. These systems can convert the mechanical energy of gravity-driven motors to electricity and can be applied to lift systems. Moreover, owing to the high population density and frequency of lift usage, a large amount of energy has been saved using this measure. Nonetheless, these systems should be adequately maintained for optimum performance. Figure 8 depicts that energy regeneration systems were mostly applied in office and other buildings.  
Overall, the degree of impact of retrofit measures on energy-saving potential depends on the building-specific information [75; 76] and, particularly, occupant behaviour [77]. Occupant behaviour and movement is a dominant element that affects the operation of all the retrofit measures mentioned above. Although occupant behaviour cannot be quantified like other retrofit measures, however, it has a significant impact on building energy consumption. Furthermore, this measure was suggested as the most significant element for defining the relationship between population variation and retrofit measures in dense cities like Shanghai.  
In general, experts suggest that a \"deep\" or whole building retrofit, including all measures described in Table 3 would yield optimum energy-saving potential [78; 79]. However, there are very few studies regarding \"deep\" or whole building retrofits in Shanghai. [PERSON] et al. reported that 20% energy saving was achieved via deep building retrofit. 5-10% of the energy saved was due to management schemes. Cumulatively, 20-30% of the energy saved was due to lighting, air-conditioning, plugs, power and water heating, and 30-50% was attributed to the upgrade in envelope systems cumulatively [58]. Hence, cities with a high population should highly consider energy-conserving management a necessary retrofit measure.
## 5 Results and Discussion  
### Analysis of the Enhancement Technique  
This section includes PSNR and contrast analysis from the High-Resolution Fundus Images (HRF) Retinal Image Database [25,45] and the Oasis Database of brain MRI [44]. The HRF image database contains 36 images of two pairs (18 images of low quality images and 18 images of high quality). The Oasis brain MRI database contained three groups of 38 patients, each with its own set of 38 images: 38 axial plane images, 38 coronal plane images, and 38 sagittal plane images.
###### Abstract  
Delay and Disruption Tolerant Networking (DTN) is a network architecture created basically to overcome non-continuing connectivity. There has been a great deal of research on this topic, from space communication to terrestrial applications. Since there are still many places on earth where there is no means of communication, the focus of this work is on the latest. A systematic literature review (SLR) was performed to know the main issues and advances related to the implementation of DTN for terrestrial and TCP/IP applications, especially in places where telecommunication infrastructure is lacking. The result is a classification of papers based on key aspects, such as architecture, performance, routing, and applications. A matrix of all the papers about these aspects is included to help researchers find the missing piece and concrete terrestrial solutions. The matrix uses three colors, green, yellow, and red according to the focus, either high, medium, or low, so that it is easy to identify specific papers.  
Delay and Disruption Tolerant Networking (DTN); performance; DTN architecture; DTN routing; Interplanetary Networking Special Interest Group (IPNSIG); Bundle Protocol (BP); systematic literature review (SLR) +
Footnote †: journal: Network  
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Footnote 0: Copyright** © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
## 5 Conclusions  
We mainly study the interference avoidance techniques for dense LEO satellite systems by optimizing the user antenna beam pointing. In this paper, the beam pointing optimization is modeled as a non-convex optimization problem. To solve the problem, the interference situational aware database composed of AoD and AoA of interfering satellites can be constructed and used for a distributed PSO-based beam pointing optimization. Then, the optimal beam pointing of the UT antenna can be obtained by maximizing the received SINR of each UT. Simulation results show that the relative error of the mean SINR obtained by BPO-PSO is 0.51%. So, the proposed method can effectively mitigate the co-channel interference by adjusting the beam pointing of the UT antennas.  
Conceptualization, M.H. and G.C.; software, M.H.; validation, M.H.; formal analysis, M.H., G.C., W.W., X.C. and L.X.; investigation, M.H. and G.C.; data curation, M.H.; writing--original draft preparation, M.H.; writing--review and editing, M.H., G.C., W.W., X.C. and L.X.; visualization, M.H.; supervision, G.C., W.W., X.C. and L.X.; project administration, M.H.; funding acquisition, G.C. and W.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China grant number 62171052 and the Fundamental Research Funds for the Central Universities with the number of No. 24820232023 YQTD01.  
Not applicable.  
The data that support the findings of this study are available from the corresponding author upon reasonable request.  
Author [PERSON] and [PERSON] were employed by the company China United Network Communications Corporation. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 2 Methodology
### Position and Elevation Error  
#### 2.3.1 Position Error  
Ideally, there is a desirably fixed nominal spacing between successive receivers, which we call the ideal position (spacing) \(\Delta X\). However, in practice, receiver spacing might differ from the ideal spacing due to field conditions and human errors (Figure 1). The actual receiver spacing at the \(n\)th receiver (\(\Delta X_{cn}\)) can be calculated from D-GPS measurements acquired in the field as follows:  
\[\Delta X_{cn}=\sqrt{\left(x_{n}-x_{n-1}\right)^{2}+\left(y_{n}-y_{n-1}\right) ^{2}}\, \tag{7}\]  
where \(x_{n}\) and \(x_{n-1}\) are the D-GPS Easting and \(y_{n}\) and \(y_{n-1}\) are the Northing values of the \(n\)th receiver and its neighbor closer to the source. To calculate the position's error at the \(n\)th receiver, Equation (8) is used:  
\[e_{xn}=\Delta X-\Delta X_{cn}\, \tag{8}\]  
which is the difference between the ideal and calculated positions of the \(n\)th receiver.
## 1 Introduction  
Population growth contributes to a surge in the number of vehicles on the road, consequently elevating the risk of accidents stemming from factors such as fatigue, drowsiness, drunkenness, distraction, and road conditions [1]. The daily flow of traffic constitutes a multifaceted process demanding shared attention, swift reaction time, and sound judgment. A significant portion of accidents can be attributed to prevalent causes, including insufficient speed selection and a failure to yield priority [2].  
The pursuit of developing systems to enhance the daily lives of individuals has been a longstanding research objective across various fields. Among the most impactful innovations for humanity are vehicles. To attain complete mastery over their vehicles, individuals require artificial systems to assist them. Numerous studies and solutions have emerged to enhance the road safety of both drivers and pedestrians. These technological advancementsform the basis of advanced driver assistance systems (ADASs) and autonomous driving systems. ADASs are designed to aid drivers and vehicles in identifying potentially hazardous traffic situations and responding promptly and accurately [3]. The growth of safety and comfort-enhancing technologies in the automotive sector has led to a pronounced focus on the study and development of these systems. They have evolved into a pivotal area of research due to their capacity to enhance the overall driving experience. While various technologies contribute to the creation of these systems [4], camera-based solutions offer significant cost advantages along with the ability to process and analyze images using the latest computer vision technologies [5]. External environment sensing involves the collection of information about the driving environment [6], compassing nearby vehicles, pedestrians, traffic signs, traffic lights, and various objects [7]. The design of such systems must consider various factors, including weather, road, and lighting conditions, as these elements can significantly impact the perceptual quality of environmental information [8].  
Over the past decade, there has been a noteworthy emphasis on the development of traffic surveillance systems, directed towards enhancing safety through the real-time monitoring of the road environment. The availability of multiple sensing modalities, such as radar, LIDAR (light detection and ranging), and cameras, coupled with the increase in computing power, has empowered engineers to devise increasingly sophisticated solutions for real-time computer vision-based assistance systems [9]. Significant strides in vehicular sensor technology have been documented, enabling the execution of various simple and complex tasks, including object detection [10], localization [11], tracking [12], and activity recognition [13] across a diverse array of applications.
# Hierarchical Active Tracking Control for UAVs via Deep Reinforcement Learning
## 3 Methodology
### Auxiliary Segmentation and Motion-in-Depth Loss  
While the RL controller's architecture is designed to have a powerful spatial and temporal representational capacity, the RL controller is still difficult to properly train using the model-free RL method since the input state is so highly dimensional; this training process is very data inefficient. Moreover, the perception layer in the RL controller may not learn the correct spatial and temporal features of the dynamic target guided by such a highly abstract reward signal.  
To accelerate the training process and build a good representation of the perception layer in the RL controller, supervised learning is combined with the RL method. Additionally, two auxiliary losses are added in the training process, namely, the segmentation loss and the motion-in-depth loss. Then, supervised learning assists the RL training process by training additional auxiliary losses. The auxiliary losses introduced can generate denser signals that facilitate the learning of spatial and temporal representations.
# Constructing More Complete Control Flow Graphs Utilizing Directed Gray-Box Fuzzing
## 2 Motivation
### Motivating Example  
Figure 1 shows a simple example to explain the impact of indirect jumps, in which subfigures (a,b) give its source code and CFG, respectively. In function \(func\), \(foo\) is a pointer to a function and may point to different targets when the input is different. If \(a>b\), \(foo\) points to \(sub\) (line 11), otherwise it points to \(add\) (line 14). An indirect jump occurs when \(foo\) is used to call a function (line 16).  
In Figure 1b, the dash lines, \(d\to e\) and \(d\to f\), represent the indirect jump relations. It is difficult to resolve the relations only by static analysis, which leads to incomplete CFGs. If a security analysis is based on the incomplete CFGs, the analysis results may be inaccurate. Assuming that there is a vulnerability in function \(add\) (line 5), we try to use flow sensitive analysis on CFGs to discover this vulnerability. If the CFG does not contain the indirect jump \(d\to e\), the bug hidden in the function \(add\) cannot be found.  
For this example, if we use the popular static analysis tool, such as IDA Pro, to construct the CFG, we will be unable to resolve the indirect jump addresses. The existing dynamic and hybrid approaches find it difficult to effectively generate test cases that exercise indirect jumps. The state-of-art hybrid approach uses a CGF-based approach to generate test cases. However, because this approach is undirected and unsustainable (in Section 1) in test case generation, it still cannot resolve indirect jumps effectively.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline
**Project** & **Version** & **Description** & **LoC** & **\# \(F\)** & **\# \(C\)** & **\# \(I\)** & \(R_{i}\)** \\ \hline libxml2 & 2.9.10 & Xml parser & 496 k & 4087 & 41,365 & 1904 & 6.56\% \\ JasPer & 2.0.16 & Picture handler & 33 k & 756 & 8010 & 32 & 0.40\% \\ libming & 0.4.8 & Swf parser & 96 k & 1612 & 23,912 & 523 & 2.14\% \\ FreeType & 2.10.0 & Library to render fonts & 219 k & 1769 & 17,337 & 659 & 3.66\% \\ libpng & 1.6.37 & Prg handler & 97 k & 1053 & 11,301 & 75 & 0.66\% \\ ImageMagick & 7.0.8.6 & Picture handler & 553 k & 6068 & 133,021 & 372 & 0.28\% \\ libjpeg-turbo & 1.5.1 & image codec & 91 k & 620 & 16,080 & 1469 & 8.37\% \\ libtiff & 4.1.0 & Tiff processor & 138 k & 1127 & 17,408 & 220 & 1.25\% \\
**Average** & - & - & 216 k & 2136 & 33,554 & 782 & 2.92\% \\ \hline \end{tabular}
\end{table}
Table 1: The distribution of indirect jumps in real programs.
# An End-to-End Deep Learning Image Compression Framework Based on Semantic Analysis
## 4 Experiments
### Visual Quality Evaluation
The calculation of SSIM is shown in Formula (12):  
\[SSIM(x,y)=\frac{(2\mu_{x}\mu_{y}+c_{1})(2\sigma_{xy}+c_{2})}{(\mu_{x}^{2}+\mu_ {y}^{2}+c_{1})(\sigma_{x}^{2}+\sigma_{y}^{2}+c_{2})} \tag{12}\]  
where \(\mu_{x}\) is the average value of \(x\), \(\mu_{y}\) is the average value of \(y\), \(\sigma_{x}^{2}\) is the variance of \(x\), \(\sigma_{y}^{2}\) is the variance of \(y\), \(\sigma_{xy}\) is the covariance of \(x\) and \(y\), and \(c_{1}\) and \(c_{2}\) are two variables used to maintain stability. SSIM analyzes the similarity of each pixel in the two compared images, and we average the sum to get the result. In our proposed SI-SSIM, we use \(L_{i}\) in Formula (10) as the weight of each pixel's similarity and finally obtain the similarity based on semantic importance by averaging the weighted summation, which is shown in Formula (13). The reliability of SI-SSIM is guaranteed by verifying the Spearman rank-order correlation coefficient (SROCC), Kendall rank-order correlation coefficient (KROCC), Pearson product-moment correlation coefficient (PLCC) and root mean square error (rMSE) of SI-SSIM and SSIM. \[\textit{SI-SSIM}(x,y)=\sum_{i=1}^{N}L_{i}\times\left(\sum_{(x,y)\in i}SSIM(x,y)\right) \tag{13}\]  
Figure 6: Image produced by different compression methods at similar compression ratios. From the left to the right: original, JPEG, BPG, George’s, and ours. ’B’ in multi-structure structural similarity (MS-SSIM)-B refers to the blue block, ‘G’ in MS-SSIM-G refers to the green block. Similarly, we compared the SI-SSIM index of JPEG, BPG, George's, and the proposed method on the Kodak dataset. The results are shown in Figure 7b, in which it can be found that the proposed methods are superior to the other two methods.
## 5 Use Cases
### Measurement Setup  
To execute and evaluate the described use cases, we set up the environment depicted in Figure 4. One Linux based computer hosted the LDAF server. It was connected to the Internet to access the APIs of external (cloud) applications (e.g., the Ethereum get client for BC or any cloud API according to the application needs). It was also connected to a local IP network for communication with IoT end devices. If necessary the local IP network could be replaced by non-IP access, e.g., by integrating the LDAF server into LoRaWAN access point or network server.  
In the blockchain use case, the end devices were five physical instances of the Swether device, as well as several emulated instances. For emulation only the specific sensing and actuation hardware was eliminated, otherwise they ran exactly the same application software as the Swether, and thus producing the same network load. The emulated instances were applied to create a number of concurrent clients while estimating scalability of the system. Beside the LDAF server, in the BC use case another server was applied, to host the Ethereum get client. It had permanent Internet connection to keep the get client synchronized with public the Ethereum network. For DNS and binary transfers to the IoT devices, corresponding HTTP request were launched from the end devices to the LDAF server, when needed.  
The entire communication traffic from/to LDAF server was captured and analyzed with Wireshark.  
Resource Consumptions at the LDAF Server  
During the operation we estimated the system requirements imposed to the LDAF server. Because the CPU and memory requirements were so low, we installed the LDAF server on a Raspberry Pi (RPi) 3 B as well. We tested it with up to 100 concurrent clients using the blockchain service in LDAF. The memory consumption was independent from the number of clients and was \(\sim\)5%, i.e., 50 MB.  
Figure 4: Measurement setup.  
The CPU load was varying. During the block processing (the Ethereum network creates a new block approximately every 20 s) the 1 s average was approximately 3-6%, otherwise \(\sim\)1%. LDAF service implementation assures that the number of clients subscribed to the same service, does not significantly affect the CPU load. We would like to point out that a systematic resource monitoring of the server resources was not our research objective. The observed values can serve as indication and are application-specific and indication and are application-specific.
## 3 Materials and Methods
### Model Architecture
#### 3.2.3 Reliability-Aware Modality Gating  
To address the inherent noise in social media data--such as blurred images or misspelled text (e.g., \"fload\" instead of \"flood\")--an adaptive gating mechanism is introduced. This mechanism dynamically regulates the weights assigned to different modalities (text and image in our case), ensuring the model can effectively leverage the more reliable modality while downplaying the less reliable one.  
For the adaptive gating mechanism, the computation commences with the derivation of a gating coefficient g, which quantifies the relative importance of cross-modal interaction features. These features, namely the T \(\rightarrow\) I attention feature \((Attn_{t})\) and the I \(\rightarrow\) T attention feature \((Attn_{i})\), are generated by the BCA module (Section 3.2.2). \(Attn_{t}\) encodes the alignment of textual semantics with visual content, while Attn\({}_{i}\) captures the correspondence of visual content to textual semantics, thus capturing cross-modal interaction information from distinct directions.  
The gating coefficient g is calculated using a two-layer neural network. In the first layer, a ReLU activation is employed to introduce non-linearity, and in the second layer, a sigmoid activation constrains g to the range [0,1]. Mathematically, this is expressed as (Equation (13)):  
\[g=\sigma(W_{2}\cdot ReLU(W_{1}\cdot[Attn_{t};Attn_{i}]+b_{1})+b_{2}) \tag{13}\]  
Here, \(W_{1}\in\mathbb{R}^{512\times 1024}\) and \(W_{2}\in\mathbb{R}^{1\times 512}\) represent weight matrices, and \(b_{1}\in\mathbb{R}^{512}\) and \(b_{2}\in\mathbb{R}\) are bias terms. The sigmoid function \(\sigma\) ensures that g resides within the interval [0,1], where values of g closer to 1 indicate a higher reliability of the text modality, and values nearer to 0 suggest a greater trustworthiness of the image-related modality.  
Subsequent to the determination of g, the fused feature \(F_{\text{fused}}\) is computed through a weighted combination of Attn\({}_{t}\) and Attn\({}_{i}\) (Equation (14)):  
\[F_{fused}=g\cdot Attn_{t}+(1-g)\cdot Attn_{i} \tag{14}\]  
This fused feature integrates complementary modality-specific information, with the integration weights dynamically adjusted according to modality reliability. The design of this mechanism ensures that the model can effectively prioritize reliable modalities (e.g., clear text over blurred images) and robustly extract informative cues even from noisy inputs.
## 3 Results and Discussion
To simulate the specific insulation, we need to have the physical, optical and thermal properties of that material as described in Figure 9:3 Due to lack of availability of these properties, for this specific reason (to show the model's capability) the available Ph-77 material was used as north wall insulation. Figure 17 shows the comparison between the heating demand of the multi-span greenhouse with north wall insulation and without insulation (transparent wall). The conditions are the same as those in aforementioned analyses. The overall results indicate that a 5% reduction in heating demand during the whole winter season was achieved using north wall insulation. The reduction of the heating demand purely depends on the material used for the north wall. Moreover, the specific energy saving is different as location and available solar radiation are different and the insulation material can also affect energy savings. Other similar studies for greenhouse design parameters, conducted by [PERSON] and [PERSON] [7], and [PERSON] et al. [4], also confirmed this trend. Greenhouse roof geometry has a huge influence on the annual energy demand of the greenhouse, as solar radiation received by the greenhouse influences its internal temperature [12]. Two multi-span greenhouse roof geometries (venlo and wide-span) were selected in order to compare their annual energy demands. The designs were selected according to South Korean standards for multi-span greenhouses. The results of both greenhouses' annual energy demand are shown in the Figure 9:4 The results indicate that during the winter season the venlo type greenhouse requires 25% less heating energy than the wide-span. In contrast, during the cooling season, the wide-span greenhouse requires 35% less cooling energy than the venlo type. This is due to the fact that the solar energy gain of the greenhouse with the wide-span roof geometry is less than that of the venlo type greenhouse. Figure 19 shows the monthly solar energy gain of both greenhouses. The venlo type greenhouse received more solar energy than that of the wide-span greenhouse. The solar gain inside the greenhouse was different due to the fact that the wide-span greenhouse has a lower angle of incidence--which causes low  
Figure 9:5 Comparison of monthly heating demand of the greenhouse with and without north wall insulation. transmission--than that of the venlo type greenhouse with high angle of incidence--which causes high transmission. A study was conducted by [PERSON] et al. [17] with the same type of greenhouse but the outcomes of the study show the wide-span greenhouse needs less annual energy than that of venlo type greenhouse. In that study, the greenhouse dimensions were not the same, so comparisons cannot be made. In our study, we used the same greenhouse dimension with but with a different roof geometry as shown in Figure 9:6
# Real-Time Tunnel Deformation Monitoring Technology Based on Laser and Machine Vision
## 5 Discussion
### About Diffraction Rings of Laser Spots  
In order to improve a laser transmitting effect, a set of optical lens were used, and this produced additional diffraction rings, as shown in Figure 25a. Precisely because of the dusty environment, the diffraction effect decreased through the dust [40], and this had advantages in the image process.