esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Indoor Localization Method Based on Image Retrieval and Dead Reckoning
## 3 Pedestrian Dead Reckoning  
Pedestrian dead reckoning (PDR) is an independent positioning system that estimates locations of pedestrians using accelerometer, magnetometer and gyroscope. It consists of four modules: step detection, stride length estimation, heading calculation and location update. The workflow of PDR is shown in Figure 1.  
After filtered to reduce noise and aligned over time, the sensor data are assigned to different modules. The step detection module makes a judgement of whether a step occurs according to the acceleration values. If a step does occur, the system runs the stride length estimation module and heading calculation module to estimate the step length and heading angle at this step. Then, a new location update process is performed. The specific location update method is demonstrated as the following equations:  
\[\left\{\begin{array}{l}x_{k+1}=x_{k}+s\_{k}\times\cos\varphi_{k}\\ y_{k+1}=y_{k}+s\_{k}\times\sin\varphi_{k}\end{array}\right. \tag{1}\]  
where \(x\) and \(y\) are the coordinates of pedestrians, \(s\_{k}\) is the stride length and \(\varphi\) is the heading. The subscript denotes step \(k\). We can derive that, under the condition that the initial position is known, locations can be estimated continuously according to Equation (1). The principles of step detection, stride length estimation and heading calculation are illustrated in the next sections.
# Approximating the Temporal Neighbourhood Function of Large Temporal Graphs
## 2 Definitions and Notations
### Temporal Paths  
A _temporal path_\(\mathbb{P}\) in a temporal graph \(G=(V,E)\) from a node \(u\in V\) to a node \(v\in V\) is a sequence of temporal edges \(e_{1}=(u_{1},v_{1},t_{1}),e_{2}=(u_{2},v_{2},t_{2}),\ldots,e_{k}=(u_{k},v_{k},t_ {k})\) such that \(u=u_{1}\), \(v=v_{k}\), and, for each \(i\) with \(1<i\leq k\), \(u_{i}=v_{i-1}\) and \(t_{i}\geq t_{i-1}+1\). The _length_ of a temporal path is the number of temporal edges it contains. For example, referring to the temporal graph of Figure 2, \((1,4,1),(4,2,5),(2,5,11),(5,3,12)\) is a temporal path of length 4. On the contrary, \((1,4,1),(4,2,5),(2,3,2)\) is not a temporal path, since the appearing time of the second edge is larger than the appearing time of the third edge. The _starting time_ (respectively, _ending time_) of a temporal path \(\mathbb{P}\), denoted by \(\sigma(\mathbb{P})\) (respectively, \(\eta(\mathbb{P})\)), is equal to the appearing time of the first (respectively, last) edge in the path. For instance, if \(\mathbb{P}=(1,4,1),(4,2,5),(2,5,11),(5,3,12)\), then \(\sigma(\mathbb{P})=1\) and \(\eta(\mathbb{P})=12\). Given a time interval \(\mathbb{I}=[t_{\alpha},t_{\omega}]\) and two nodes \(u\) and \(v\), we will denote by \(\mathcal{P}(u,v,\mathbb{I})\) the set of all temporal paths \(\mathbb{P}\) from \(u\) to \(v\) such that \(t_{\alpha}\leq\sigma(\mathbb{P})\leq\eta(\mathbb{P})\leq t_{\omega}\). Among all temporal paths between two nodes in a given time interval, in this paper, we will distinguish the ones that allow us to arrive as early as possible.  
**Definition 1**.: _Given a temporal graph \(G=(V,E)\), two nodes \(u\) and \(v\) in \(V\), and a time interval \(\mathbb{I}=[t_{\alpha},t_{\omega}]\), a path \(\mathbb{P}\in\mathcal{P}(u,v,\mathbb{I})\) is said to be an **earliest arrival path** if \(\eta(\mathbb{P})=\min\{\eta(\mathbb{P}^{\prime}):\mathbb{P}^{\prime}\in \mathcal{P}(u,v,\mathbb{I})\}\)._  
For example, the left part of Figure 3 shows the earliest arrival path from node 1 to node 3 in the time interval \([1,12]\) in the temporal graph of Figure 2: this path consists of the temporal edges \((1,4,1)\), \((4,5,3)\), and \((5,3,4)\), its starting time is 1, and its ending time is 4. The right part of the figure, instead, shows an earliest arrival path from node 1 to node 3 in the time interval \([2,12]\): it consists of the temporal edges \((1,4,6)\) and \((4,3,9)\), its starting time is 6, and its ending time is 9 (note that another earliest arrival path from node 1 to node 3 in the same interval is the one consisting of the temporal edges \((1,5,7)\), \((5,4,8)\), and \((4,3,9)\)).  
Figure 2: An example of temporal graph with five nodes and 12 temporal edges (labels on edges represent appearing time), and the corresponding link stream representation (labels on the left represent nodes, labels above represent time instants).
# Physical-Mechanical Properties of Cupola Slag Cement Paste
## 2 Materials and Methods
### Compressive Strength  
Figure 16 shows the evolution of the compressive strength as a function of age for the three mortars designed at the ages of 2, 7, 28, 60, and 90 days.  
Figure 16: Evolution of the compressive strength as function of time.  
Figure 15: Influence of the mix proportions on the workability, (**a**) corresponding to 20% replacement, (**b**) 50% replacement, (**c**) all cases sorted by replacement percentage, (**d**) equations used to obtain each variable represented.  
In Figure 16, it is possible to appreciate that for the age of 2 days, the results corresponding with M-50 are about twice the reference values, while those for M-20 are about three times the reference values. The importance of these values at an early age is that it is possible to handle the tubes without breaking the coating. For this reason, on the basis of the results observed, the replacement of the cement would reduce the waiting times until the pipes can be handled. At the age of 7 days, the reference and M-50 results are almost equal, while the M-20 results are approximately 50% higher than the reference results. For the ages of 28 and 60 days the values for the reference and the M-50 continue to be quite similar, while the difference with the M-20 is reduced. This is because M-20 practically maintains its strength constant while the reference continues to grow slowly. At the age of 90 days the reference mortar has a slightly higher compressive strength than M-50, although it seems that it has already reached a stable value and therefore this difference will not increase. When comparing the results of M-20 with the reference mortar, the M-20 is still higher, approximately 20%.  
From analysing Figure 16 it is possible to see how the fit to a logarithmic function is better for early ages, especially in the case of having an M-20 and CEM III. This effect can be justified because the mortar has reached its maximum strength values and does not continue to evolve. This may be because OPC at 28 days has almost completely reacted, which is why the mechanical properties of those mortars with CEM I practically do not evolve beyond 28 days.  
It is also possible to appreciate that the early age strengths are better in the case of using a combination of CEM I +CSF than in case of using CEM III. This is of great interest because it is likely that the pipes will need to be handled during the first few days of aging and, due to these better mechanical properties, pipes made with these new cements will be less likely to break. Based on this observation it could be concluded that the increase in the setting time of cement with 50% CEM I + 50% CSF does not have a major impact.
# Methodological Framework to Collect, Process, Analyze and Visualize Cyber Threat Intelligence Data
## 5 Proposed Solution and Methodology
### Feeds Collection  
Steps 7 and 8 must be fed with treated and standardized data in order to provide threat information and analytics to the user. However, before that, it is necessary to correctly collect IOCs from different Internet sources--such as forums, sites, archives (PDF, word, freetext, etc.) and feeds. Among these, feeds usually are the most desirable information collection source, because many cyber threat vendors or specialists release their threat research and hunting data in this manner. Feeds are a set of indicators of a specific threat type, e.g., malicious IP addresses that should be blacklisted on firewalls or phishing URLs that must not be accessed by common users. In this study, seven known feeds were used to acquire threat information and IOCs. All information is listed in Table 2.  
Furthermore, it is possible to add manual Feeds or IOCs on the tool as long as data are written in STIX/TAXII format.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Feed Name** & **Brief Description** \\ \hline Blocklist.de [30] & Lists of malicious IPs to be blacklisted \\ \hline Firehol Blacklisted IPs [31] & Blacklist of malicious IPs that should be blocked on servers directly connected to the Internet. \\ \hline Mirai Security [32] & List of the last 1000 likely IPs of machines infected with Mirai Botnet ransomware. \\ \hline Openphish [33] & List of phishing URLs \\ \hline Pan-unit42 [34] & List of malicious masked URLs \\ \hline Vxvault [35] & List of malicious and downloadable.dll and.exe files. \\ \hline Zerodott1 [36] & List of blockchain mining bot domains to be blocked by a network admin. \\ \hline \hline \end{tabular}
\end{table}
Table 2: List of imported feeds.
## 4 Discussion
evaluated trunk and pelvic movement variability in transfemoral amputees using a C-leg system and found that they clearly demonstrated increased kinematic variability in trunk and pelvic movements, indicating that their gait pattern was affected compared with the healthy controls [44]. [PERSON] et al. reported that the vaulting strategy is widely used by people who have undergone transfemoral amputation through their gait analysis results, and the patients in the study showed power generated by the sound ankle at a mid-stance above 0.15 W/kg [45]. This generated power was shown to be a criterion for the vaulting clinical gait strategy of this population during level walking [46]. [PERSON] et al. stated that individuals who have undergone transfemoral amputation may be at a greater risk of sound knee cartilage or other tissue damage, providing insight into dynamic loading characteristics during walking in young, active individuals exposed to unilateral TFA [47]. According to our results, in the evaluation of the underfoot pressure of the sound leg, the maximum pressure point shifted to the forefoot, and it was observed that the forefoot and midfoot were more active during walking compared with the control group. These results showed that gait compensation and plantar flexion in the sound limb are used more forcefully during the gait cycle, which may affect the biomechanics of the knee joint. A larger sample size for the TFA group could be considered in future research to confirm and expand upon these results. Future work could also include uneven surfaces and the effects of walking uphill and downhill. The use of plantar pressure can provide preliminary information about weight transfer between both extremities during the walking phase. Based on our clinical experience in studying transfemoral amputation patients, orthotic insole approaches may be considered according to the results obtained to balance the weight transfer under the foot of the sound limb. Additionally, data may be used for guidance during prosthetic training. On the prosthetic side, considering factors such as prosthetic length, prosthetic alignment, and socket compatibility would be useful in prosthetic rehabilitation and walking training.
# Vulnerable Road User Detection for Roadside-Assisted Safety Protection: A Comprehensive Survey
## 3 VRU Detection Models
Thus, the different characteristics of VRUs make them vulnerable in open traffic environments, highlighting the importance of accurate detection and classification of VRU subclasses. Various challenging scenarios and the lack of the number and variety of VRUs in general datasets all increase the difficulty of the detection model development. In the following introduction to VRU detection, existing studies are categorized into single-sensor and multi-sensor approaches. Multi-sensor studies are further classified based on sensor modality and fusion strategies, as shown in Figure 3. To provide a comprehensive overview, this paper presents a summary Table 2 highlighting VRU-related detection models, offering readers a clear understanding of the key methodologies and trends in the field. Figure 2: Ratio of detection models. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Methods** & **Year** & \multicolumn{2}{c}{**Dimensions**} & \multicolumn{2}{c}{**Sensors**} \\ \cline{3-6}  & & **2D** & **3D** & **Camera** & **LiDAR** & **RADAR** \\ \hline Faster R-CNN [17] & 2015 & ✓ & ✓ & & \\ SSD [18] & 2016 & ✓ & ✓ & & \\ RetinaNet [19] & 2017 & ✓ & ✓ & & \\ FPN [20] & 2017 & ✓ & ✓ & & \\ YOLO series [21; 22; 23; 24] & - & ✓ & ✓ & \\ CornerNet [25] & 2018 & ✓ & ✓ & \\ CenterNet [26] & 2019 & ✓ & ✓ & \\ ViT [27] & 2021 & ✓ & ✓ & \\ DETR [28] & 2020 & ✓ & ✓ & \\ Swin Transformer [29] & 2021 & ✓ & ✓ & \\ PVT [30] & 2021 & ✓ & ✓ & \\ FCOS3D [31] & 2021 & ✓ & ✓ & \\ DETR3D [32] & 2022 & ✓ & ✓ & \\ PETR [33] & 2022 & ✓ & ✓ & \\ BEVDepth [34] & 2023 & ✓ & ✓ & \\ BEVHeight [35] & 2023 & ✓ & ✓ & \\ CBR [36] & 2023 & ✓ & ✓ & \\ CoBEV [37] & 2023 & ✓ & ✓ & \\ VelofCN [38] & 2016 & ✓ & ✓ & ✓ \\ PIXOR [39] & 2018 & ✓ & & ✓ \\ VoxelNet [8] & 2018 & ✓ & & ✓ \\ SECOND [40] & 2018 & ✓ & & ✓ \\ PointPillars [41] & 2019 & ✓ & & ✓ \\ CenterPoint [42] & 2021 & ✓ & & ✓ \\ PillarNet [43] & 2022 & ✓ & & ✓ \\ PointRCNN [44] & 2019 & ✓ & & ✓ \\ PV-RCNN [45] & 2020 & ✓ & & ✓ \\ PV-RCNN++ [46] & 2023 & ✓ & & ✓ \\ MV3D [47] & 2017 & ✓ & ✓ & ✓ \\ AVOD [48] & 2018 & ✓ & ✓ & ✓ \\ Cont Fuse [49] & 2018 & ✓ & ✓ & ✓ \\ SACNet [50] & 2019 & ✓ & ✓ & ✓ \\ FuseSeg [51] & 2020 & ✓ & ✓ & ✓ \\ BEVFusion [52] & 2023 & ✓ & ✓ & ✓ \\ CoBEVFusion [53] & 2023 & ✓ & ✓ & ✓ \\ BroadBEV [54] & 2024 & ✓ & ✓ & ✓ \\ MVXNet [55] & 2019 & ✓ & ✓ & ✓ \\
3D-CVF [56] & 2020 & ✓ & ✓ & ✓ \\ VPF-Net [57] & 2022 & ✓ & ✓ & ✓ \\ PointAugmenting [58] & 2021 & ✓ & ✓ & ✓ \\ PointFusion [59] & 2018 & ✓ & ✓ & ✓ \\ PI-RCNN [60] & 2020 & ✓ & ✓ & ✓ \\ EPNet [61] & 2020 & ✓ & ✓ & ✓ \\ MSMPusion [62] & 2023 & ✓ & ✓ & ✓ \\ PointPainting [63] & 2020 & ✓ & ✓ & ✓ \\ DeepFusion [64] & 2022 & ✓ & ✓ & ✓ \\ TransFusion [65] & 2022 & ✓ & ✓ & ✓ \\ SupFusion [66] & 2023 & ✓ & ✓ & ✓ \\ CenterFusion [67] & 2021 & ✓ & ✓ & ✓ \\ CRAFT [68] & 2023 & ✓ & ✓ & ✓ \\ RCBEV4d [69] & 2023 & ✓ & ✓ & ✓ \\ CRN [70] & 2023 & ✓ & ✓ & ✓ \\ RCBEVDet [71] & 2024 & ✓ & ✓ & ✓ \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Overview of generic deep learning (DL)-based vulnerable road user (VRU) detection methods.
# Radar Voxel Fusion for 3D Object Detection
## 3 Methodology
### Simulated Depth Camera  
To simplify the software fusion scheme and to lower the cost of the sensor setup, lidar and camera sensor could be replaced by a depth or stereo camera setup. Even though the detection performance of stereo vision does not match the one of lidar, recent developments show promising progress in this field [34]. The relative accuracy of stereo methods is higher for close range objects, where high accuracy is of greater importance for the planning of the driving task. The nuScenes data set was chosen for evaluation since it is the only feasible public data set that contains labeled point cloud radar data. However, stereo camera data are not included in the nuScenes data set, which we use for evaluation.  
In comparison to lidar data, stereo camera data are more dense and contain the color of objects in its data. To simulate a stereo camera, we use the IP-Basic algorithm [35] to approximate a denser depth image from the sparser lidar point cloud. The IP-Basic algorithm estimates additional depth measurements from lidar pixels, so that additional camera data can be used for the detection. The depth of these estimated pixels is less accurate than that of the lidar sensor, which is in compliance with the fact that stereo camera depth estimation is also more error-prone than that of lidar [36; 37].  
Our detection pipeline looks for objects in the surroundings of up to 50 m from the ego vehicle so that the stereo camera simulation by the lidar is justified as production stereo cameras can provide reasonable accuracy in this sensor range [38; 39]. An alternative approach would be to learn the depth of the monocular camera images directly. An additional study [40] showed that the state of the art algorithms in this field [41] are not robust enough to create an accurate depth estimation for the whole scene for a subsequent fusion. Although the visual impression of monocular depth images seems promising, the disparity measurement of stereo cameras results in a better depth estimation.  
\begin{table}
\begin{tabular}{c c c} \hline
**Data Set Split** & **Training Samples** & **Validation Samples** \\ \hline nuScenes & 19,659 & 4278 \\ Rain & 2289 & 415 \\ Night & 4460 & 788 \\ \hline \end{tabular}
\end{table}
Table 1: Training and validation splits for different environment conditions. The table only considers samples in which at least one car is present in the field of view of the front camera.
## 3 Network Model and Problem Statement  
### Network Model  
We assume initially all the sensor nodes are randomly deployed in an urban area that consists of streets running east-west and avenues running south-north. Distances between neighboring streets (or avenues) are randomly chosen, they are not equally reported. The lengths of streets and avenues are also randomly chosen within a certain range. Furthermore, from south to north, streets are numbered 0,1,2,..., and from west to east, avenues are numbered 0,1,2,...., as can be seen from Figure 1. Additionally, the addresses which are used to locate the places where people will stay for the defined activities are randomly distributed on the roads. Each address is associated with the activities people can do at the place, e.g., a restaurant, an office, _etc._ All the sensor nodes are homogeneous and have a unique ID number. The maximum transmission range of all the nodes is fixed to R. A number of static sink nodes are randomly deployed in the network.  
The mobility of all sensor node is assumed to follow the agenda based mobility model depicted in [20] where each sensor node carries a unique agenda that describes its whole days journey. Each item of the agenda indicates when and where the node will be. A node moves only according to its agenda and it moves from the current location towards the next one in its agenda by using [PERSON]'s Algorithm. Besides, each sensor node has a home from where the node always starts and to where it returns before midnight.
## 4 Discussion
### Role of Genes Co-Regulated with the PTI-Marker FRK1  
The closely related TFs MYB7 and MYB32 are similar to MYB4 transcriptional regressors (Figure SSA) [62], implying an extensive functional redundancy explaining why a single _myb4_ knock-out mutant has no significant effect on this crosstalk. The anthocyanin repressor MYBL2 [63; 64] was only weakly co-regulated with the FRK1. Therefore, it might be interesting to generate and analyze a triple _myb4/myb7/myb32_ KO mutant to see if transcriptional suppression of FPGAs is mediated by these MPs together.  
The bHLH78 TF (AT5C48560) and WRKY9, as well as another bHLH TF (AT1G05710) with unknown functions, and WRKY39, were identified by ATTEDII analysis (Figure S5B,C). WRKY9 and bHLH78 are linked via WRKY36, which is co-regulated with the FRK1 (Table S2). WRKY36 usually acts to suppress HY5 transcription, but in response to UV-B, this TF interacts with UVR8 thereby releasing from the HY5 promoter [65]. WRKY9 is reported to regulate suberin production thereby forming another protective barrier and enhancing salt tolerance [66]. The biclustering analysis identified several negative and positive regulators belonging to the NAC and WRKY TF families (Figure S3). The negative regulators induced during PTI are potentially involved in the suppression of the FPGAs, where they could bind to FPG promoters, or alternatively, regulate the TFs forming MBW complexes as indicated in the lower part of Figure 3. However, the negative regulators among them could also suppress the expression of TFs forming positive regulating MBW complexes in the upper part of Figure 3. NAC2 can be induced by salt, abscisic acid (ABA), the ethylene precursor ACC, and the synthetic auxin NAA [67], while NAC003 has been reported to be a negative regulator in vascular stem cell maintenance [68]. Interestingly, NAC053 (NTL4), a positive regulator, functions in regulating drought-induced leaf senescence [69] and programmed cell death under heat stress conditions [70]. NAC053 is furthermore transcriptionally regulated under mitochondrial stress conditions and probably binds to a mitochondrial dysfunction motif (CTTGNNNNNNA[AC]G), the motif TG[TG]CGTA or a Proteasome-Related _cis_-Element with the consensus core TGGGC [69; 71; 72], which was described as _FORCA_ Motif that responds to the crosstalk between defense and light signaling and found in all \"early FPGAs\" [5; 73]. WRKY6 has been shown to negatively regulate Phosphate1 (Pho1) expression in response to low phosphate (Pi) stress, resulting in increased anthocyanin production [74; 75], but also low nitrogen availability can induce flavonoid production [76; 77]. Moreover, WRKY75 RNAi plants were also susceptible to Pi stress as indicated by the higher accumulation of anthocyanin during Pi starvation, suggesting that this negative regulator might suppress FPG or APG expression [78].
# Enhancing Smart City Safety and Utilizing AI Expert Systems for Violence Detection
## 2 Methodology  
### Dataset  
In this study, data collection was the first part, in which the data were collected from numerous resources, with around 735 images collected from the internet source [28] and around 1365 images collected from Kaggle [29; 30]. However, the data we gathered from the above sources were not efficient for training our model, so we proposed a novel stable diffusion approach for generating more image data as shown in the top-left part of Figure 1. The next subsection explains image-to-image diffusion model to generate synthetic images.  
Image-to-Image Stable Diffusion Pipeline Method  
The image-to-image stable diffusion model [31] was first developed by StabilityAI, CompVis, runway, and LAION. This model generates synthetic or new images using text prompts and clips. Model idea addition associates the noise with the images and follows the Markov chain, continuing to do this step-by-step. After the lapse of time steps, images are converted totally into noise and the noise approaching [31; 32] method is halted. This is known as noising or forward diffusion. Now, another diffusion concept is introduced here, and in this process, a complete noise image is used to generate a new image using the reverse method. Here, time steps are subtracted gradually from the image. This process is known as denoising or reverse diffusion [33].  
1. Forward diffusion (noising) \[\mathbf{x_{0}}\rightarrow\mathbf{x_{1}}\rightarrow\mathbf{x_{2}}\rightarrow\cdots \ \mathbf{x_{T}}\] Take a data distribution \(\mathbf{x_{0}}\)-\(\mathbf{p(x)}\) and turn it into noise using the diffusion \(\mathbf{x_{T}}\)-\(\mathbf{N}\) (\(\mathbf{0}\), \(\sigma^{2}\mathbf{I}\)).
2. Reverse diffusion (denoising) \[\mathbf{x_{T}}\rightarrow\mathbf{x_{T-1}}\rightarrow\mathbf{x_{2}}\rightarrow\cdots \ \mathbf{x_{0}}\] Take a data distribution \(\mathbf{x_{T}}\)-\(\mathbf{N}\) (\(\mathbf{0}\), \(\sigma^{2}\mathbf{I}\)) and turn it into noise using the diffusion \(\mathbf{x_{0}}\)-\(\mathbf{p(x)}\).  
Figure 2 shows the sample images generated using stable diffusion to resolve the dataset limitation issue in the object detection system. The total dataset for training our proposed model that was generated using stable diffusion and includes real data collected through a self-generated source, from the internet, and from Kaggle is shown in Table 1. Further, the dataset was annotated using labelImg. labelImg is an annotation tool that is used to annotate or label the data to train the YOLO model. For each class, 700 images were real and 300 images were generated images. Then, the final dataset was divided into ratios for training at about 70% and validation sets at about 30%.