esa-sceva/satcom-chunk-collection huggingface dataset:
# Outdoor Node Localization Using Random Neural Networks for Large-Scale Urban IoT LoRa Networks +
###### Abstract  
Accurate localization for wireless sensor end devices is critical, particularly for Internet of Things (IoT) location-based applications such as remote healthcare, where there is a need for quick response to emergency or maintenance services. Global Positioning Systems (GPS) are widely known for outdoor localization services; however, high-power consumption and hardware cost become a significant hindrance to dense wireless sensor networks in large-scale urban areas. Therefore, wireless technologies such as Long-Range Wide-Area Networks (LoRaWAN) are being investigated in different location-aware IoT applications due to having more advantages with low-cost, long-range, and low-power characteristics. Furthermore, various localization methods, including fingerprint localization techniques, are present in the literature but with different limitations. This study uses LoRaWAN Received Signal Strength Indicator (RSSI) values to predict the unknown X and Y position coordinates on a publicly available LoRaWAN dataset for Antwerp in Belgium using Random Neural Networks (RNN). The proposed localization system achieves an improved high-level accuracy for outdoor dense urban areas and outperforms the present conventional LoRa-based localization systems in other work, with a minimum mean localization error of 0.29 m.  
IoT; LoRaWAN; RSSI; localization; RNN +
Footnote †: This paper is an extended version of our paper published in 2021 Computing Conference, London, UK, 15-16 July 2021.
## 7 Control Strategy and State Space Equations
### Conduction and Switching Losses Calculation  
\[P_{cd1}=\frac{16 Po(1+D)^{2}D^{2}}{RL(1-D)^{4}}rds1 \tag{64}\]  
\[P_{cd2}=\frac{4 PoD^{2}(1+D)^{2}}{RL(1-D)^{4}}rds2 \tag{65}\]  
\[P_{SW}=Vsw^{2}FsCo \tag{66}\]  
\[P_{SWL1}=\frac{4 Vg^{2}}{\left(1-D\right)^{2}}FsCo \tag{67}\]  
\[P_{SWL2}=Vo^{2}-\frac{16 Vg^{2}}{\left(1-D\right)^{2}}FsCo \tag{68}\]  
\[M_{TL}=\frac{16 Po(1+D)^{2}D^{2}}{RL(1-D)^{4}}rds1+\frac{4 PoD^{2}(1+D)^{2}}{RL( 1-D)^{4}}rds2+\frac{2 Vg^{2}FsCo}{\left(1-D\right)^{2}}+\frac{1}{2}(Vo^{2}- \frac{16 Vg^{2}FsCo}{\left(1-D\right)^{2}}) \tag{69}\]  
Formulas (64) and (65) are utilized for computing power conduction losses in Sw\({}_{1}\) and Sw\({}_{2}\), identified as P\({}_{cd1}\) and P\({}_{cd2}\), respectively. Equation (66) delves into the power switching losses of Sw\({}_{1}\) and Sw\({}_{2}\), denoted as P\({}_{SWL1,2}\), with Co representing the output capacitor of the MOSFET and Fs representing the switching frequency. The cumulative power loss in the MOSFET devices, derived from Equation (69) as the summation of Formulas (64), (65), (67), and (68), is denoted as M\({}_{TL}\), encompassing the total power losses of the MOSFETs.
## 4 Results and Discussion
### First Query: Use of AI on CFD
#### 4.1.6 Keyword Analysis  
The final analysis focused on the co-occurrence of keywords identified in the retrieved publications. Figure 10 presents the co-occurrence graph, generated from the query data, considering keywords with a minimum occurrence of five. This visualization highlights the 500 most frequently occurring keywords, revealing the presence of four distinct clusters: the green cluster, which encompasses terms related to fluid dynamics and its various applications; the blue cluster, which includes keywords associated with artificial intelligence and neural networks, particularly in the context of optimization; the red cluster, which focuses on medical and biomedical applications related to fluid flow; and the yellow cluster, which comprises terms associated with energy and heat transfer.  
Figure 10 shows the strong interconnection between computational modeling and artificial intelligence methodologies, with AI primarily applied for optimization and error correction. Notably, the medical component appears more distantly related to CFD and AI applications, as its keywords, while present in the dataset, exhibit weaker direct associations with the core focus of this study.
# Real-Time Moving Ship Detection from Low-Resolution Large-Scale Remote Sensing Image Sequence
## 3 Experiment and Comparison
### Real-Time Performance Comparisons
\begin{table}
\begin{tabular}{c c c c} \hline
**No.** & **Step** & **Sub-Step** & **Time Consumption** \\ \hline \multirow{3}{*}{1} & \multirow{3}{*}{Preprocessing} & Morphological reconstruction & 0.3389 s \\ \cline{3-4}  & & Adaptive threshold segmentation & 0.1207 s \\ \cline{3-4}  & & Connected domain marker & 0.2220 s \\ \hline \multirow{3}{*}{2} & \multirow{3}{*}{Feature identification} & S-HOG & 0.4245 s \\ \cline{3-4}  & & Binary SVM & 0.1503 s \\ \hline
3 & Association identification & Association identification & 0.4836 s \\ \hline
4 & S-Yolo identification & S-Yolo identification & 0.0153 s \\ \hline
5 & & Total & 1.7553 s \\ \hline \end{tabular}
\end{table} 
Table 4: Time consumption for each step in the actual test process. \begin{table}
\begin{tabular}{c c c c} \hline
**No.** & **Model** & **FPS** & **Single Frame Image Computation Time** \\ \hline
1 & Yolo Tiny V2 [7] & 244 & 5.12 s \\ \hline
2 & Yolo Tiny V3 [8] & 220 & 5.68 s \\ \hline
3 & Proposed & – & 1.75 s \\ \hline \end{tabular}
\end{table} 
Table 5: Comparison of the real-time performance of calculation. During the experiment, the design adopted the SNR \(\geq\) 3 image data. In practical engineering applications, the SNR has a certain random distribution due to the influence of lighting conditions and certain specific areas at the imaging time. Low SNR will lead to the increase in false alarms and the decrease in accuracy. Figure 10 shows the Receiver Operating Characteristic Curve (ROC) of the proposed method under 6 different signal-to-noise ratios. Noise is added to the test data set to test the robustness of the method. In the ROC curve, the closer the curve is to the top left, the better the method. Any point on the curve represents the choice of different binary thresholds under the current signal-to-noise ratio. The vertical axis \(R_{c}\) represents the percentage of all targets that are correctly classified as true. \(F_{PR}\) (False positive rate) is the proportion of real ships that are correctly classified as noise in all noise samples. SNR3.0, SNR2.5, and SNR2.0 curves were very steep and close to each other. The other curves decrease with the decrease in SNR, among which the black curve (SNR = 0.5) had the lowest score.
# Evaluation of the Acoustic Noise Inside the Main Steam Line of a BWR/5 Nuclear Reactor
## 4 Results
### Discussion of the Results  
The results obtained using the proposed methodology converge with the theoretical, numerical, and experimental results reported in the open literature [1; 2; 3; 4; 5; 6]. This methodology is useful as an initial estimate of the acoustic loads and the resonant frequency. The different models are compared in Table 3[6; 25]. The main advantage of the proposed methodology is its low requirement for computing resources [6; 18; 19; 25; 26].  
To obtain dynamic similarity, the Strouhal number was kept constant between the real model and the scale model (Table 2). This parameter ranges between 0.23 and 0.4, depending on the velocity considered in the evaluation. A low Strouhal number could indicate that the system is less susceptible to being in a resonant frequency (\(S_{t}=0.23\)). On the other hand, higher Strouhal numbers would shift the analyzed system to a higher frequency. In some nuclear power plants, the acoustic resonances have been reported with the Strouhal numbers at around 0.33 (Table 4). It corresponds to an oscillation frequency in an extended power uprate and generates significant vibrations in piping and associated components [1; 2; 4; 5; 6; 10].  
It is important to develop numerical models that predict the acoustic noise in the piping systems of nuclear facilities. In these cases, the computational cost can be a limiting factor. For this reason, the aim is to simplify the problem. Therefore, reliable results can be obtained before implementing a more comprehensive model that covers all the scales of turbulence and fluctuations over a wide range of frequencies. For the analysis considered in this paper, the acoustic resonance was at 1560 Hz and the acoustic power range was between 97 dB and 114 dB, when the ratio between the branch and mainline was 1:8.  
Figure 19: Numerical acoustic power evaluated with a scale of 1:8.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Features and** & **CAA** & **Acoustic** & **Harmonic and** & **Broadband** & **FSI Coupling** \\
**Limitations of** & **Computational** & **Anagocaustics** & **and FWH** & **Modal Analysis** & **Noise Modeling** & **[This Work]** \\ \hline Computation cost & Too high & High & Moderate & Low & Low \\ \hline Solution & Transient & Transient & Steady state & Steady state & Steady state \\ \hline Accuracy & Excellent & Best & Good & Limited & Good \\ \hline \hline \end{tabular}
\end{table}
Table 3: Different models for the prediction of the resonant frequency.  
When a system is in acoustic resonance, the amplitude of the acoustic waves increases considerably, generating very high-pressure peaks at specific points, as shown in Figure 11, Figure 17, and Figure 18. Damping is essential to avoid severe structural damage. Effective damping can be achieved by adding Helmholtz resonators, acoustic silencers, perforated elements, or damping chambers.
## 1 Introduction
The study presented here expands on a recent work where HNSWs were used to determine the elastic modulus of concrete cylinders fabricated with three different \(w/c\) ratios, namely 0.42, 0.45, and 0.50 [38]. In the present work, we use some of the findings from [38] to predict water in excess in short concrete beams made with \(w/c=0.42\) but corrupted with water. Two conditions were simulated. The first one consisted of standing water in formworks prior to pouring concrete, whereas the second condition consisted of sprinkling water above the fresh concrete during casting and surface finishing. These two conditions may reflect adverse weather in the field. The objective of the study was the development of a system that, unlike the UPV method, can predict localized deterioration conditions associated with poor quality \(w/c\) ratios. Three HNSW transducers, referred with the descriptor P1, P2, and P3, were used to quantify the elastic modulus of the beams. Owing to the novelty of the transducers design it was decided to assemble and test three of them in order to demonstrate and quantify the repeatability of the design and to quantify any variation of the results associated with differences in the assembly. The findings were then compared to the results of a conventional UPV test in order to evaluate and prove advantages and eventually limitations of the proposed approach. With respect to ultrasonic-based NDE, the HNSW-based approach: (1) exploits the propagation of HNSWs confined within the particles of the chain; (2) employs a cost-effective transducer; (3) does not require any knowledge of the sample thickness; (4) does not need access to the back-wall of the sample; (5) provides point-like information rather than information about the average characteristics of the whole sample. The method also differs significantly from the Schmidt hammer. In fact, the hammer can be used to test hardened material but the HNSW approach can also characterize fresh concrete and cement as demonstrated in [39; 40]; only one parameter, the rebound value, is used in the Schmidt hammer test, while multiple HNSW features can, in principle, be exploited to assess the condition of the underlying material. The hammer may induce plastic deformation or microcracks into the specimen, while the HNSW approach is purely nondestructive. The paper is organized as follows. The next section describes the concrete samples, the design of the HNSW transducers, and the test protocols adopted throughout the study. Section 3 presents the experimental results associated with the concrete cylinders and it is largely excerpted from reference [38] in order to provide a comprehensive knowledge in support of the findings of Section 4 that presents the results associated with the short beam. The latter represents the core novelty of the present paper. Finally, Section 5 summarizes the findings of the project and provides some suggestions for future studies.
## 4 Results and Discussion
### Dataset Description and Evaluation Metrics  
To validate the method proposed in this study, we used a dataset released by [PERSON] et al., who captured data directly for 10 days from the Universidaa del Caucau Unicauca network in 2019. The dataset comprises a total of 113 features and samples from 1249 users classified into three classes: high, medium, and low consumption.  
OTT data are well-represented, and analyses were performed for a total of 56 applications, including typical OTT services (e.g., Netflix, YouTube, Twitch, and Spotify). The traffic flow was analyzed for each, and features were extracted, as shown in Table 2[12].  
The number of samples in each dataset class differed from those in real situations. There were 406 high-consumption samples, 333 medium-consumption samples, and 510 low-consumption samples, which were readjusted to balance the classes. We used an analytical approach that shows good performance, even if training is performed to reflect real-world data. We used the SMOTE method to adjust the above-described open dataset according to the proportions of an actual situation [56]. We set the number of samples for each class to 5610 for low, 566 for medium, and 812 for high.  
Recall, precision, and F-measure were considered as evaluation metrics and were calculated based on true positive (TP), false positive (FP), and false negative (FN) results. In this study, data composed of three classes were classified, and we will first look at the concepts of TP, FP, and FN using examples of a high-consumption group. TP refers to the rate at which the sample belonging to the actual high class is predicted to be high. FP and FN are values indicating an error, FP indicates that a sample that actually belongs to low or medium class is incorrectly classified as high class, and FN indicates that a sample that belongs to high is incorrectly classified as low or medium class. TP, FP, and FN of medium and low classes can also be obtained in the same manner. Recall is equivalent to TP, a numerical value that indicates correct classifications. Precision is the probability of the data belonging to that class. F-measure shows the accuracy by finding the harmonic mean of the precision and recall; see Equation (7):  
\[Recall=\frac{TP}{TP+FN}\,\ \ \ Precision=\frac{TP}{TP+FP}\,\ \ \ F-Measure=\frac{2\cdot Recall \cdot Precision}{Recall+Precision}. \tag{7}\]
# An Efficient Causal Group Communication Protocol for Free Scale Peer-to-Peer Networks
## 3 Results
### Protocol Composition
#### 3.1.3. Correctness Proof  
To show that our algorithm ensures the causal delivery (correctness), we provide a correctness proof. In order to do the proof as simple as possible, we focus on the part of the internal group where the message dependencies are represented on a bit level. In this proof we will use the following notations:  
* \(m_{k}\): message with \(SN=k\).
* \(p_{j}\): peer with identifier \(j\).  
We show that with this information we ensure the causal order.  
**Theorem 1**.: _For each set bit in \(m_{l}.DV[k]\), \(k\) identifies a message from the same group or TT contains (in, out) at position id such that (\(k=out\)), where (id, in) identifies a message \(m_{k}\), which has IDR with \(m_{l}\)._  
**Main steps of the proof.** The proof is composed of three lemmas and a proposition. The lemmas are intermediate results necessary for our proof:  
* Lemma 1 shows that if \(m_{l}.DV[k]\) is set, then the message with \(SN=k\) causally precedes message \(m_{l}\).
* Lemma 2 shows that if \(m_{l}.Last=k\), then message with \(SN=k\) causally precedes message \(m_{l}\).
* Lemma 3 indicates that the message \(m_{k}\) has an immediate dependency relation with the other message \(m_{l}\) if and only if bit \(m_{l}.DV[k]\) is set.
* Proposition 1 shows that through the bits structure \(h(m)\) attached to messages sent and the causal information at the super peer, we ensure the causal order (Theorem 1).  
**Lemma 1**.: If \(ml.DV[k]\) is set then message \(m_{k}\to m_{1}\)  
**Proof:** By Line 2 in Listing 4, we have that \(m_{l}.DV[k]\) is set if and only if \(DV[k]\) at peer \(p_{j}\) is set when the sending of \(m_{l}\) is carried out by \(p_{j}\). By using Line 4 in Listing 6, we have that \(DV[k]\) is set only after the delivery of \(m_{k}\). In addition, the super peer does not make any modifications to the \(DV\) of a message.  
**Proposition 1**.: _If \(m_{l}.DV[k]\) is set and \(m_{k}\) is a message from the external group then \(TT\) contains \((in,k)\)._  
Proof.: By Line 19 of Listing 15, we have that \((in,k^{\prime})\) is inserted into \(TT[m_{k^{\prime}}.id]\) only after the delivery of message \(m_{k^{\prime}}\) at the super peer where \((m_{k^{\prime}}.id,in)\) identifies a message in the external group. The corresponding internal message has \(SN\) equals to \(k^{\prime}\). In the delivery of \(m_{k^{\prime}}\) at \(p_{i}\) with \(SN=k^{\prime}\), we have (by Line 4 of Listing 6) that \(DV[k^{\prime}]\) is set. We know by Lemma 3 that if \(m_{l}.DV[k^{\prime}]\) is set then \(m_{k^{\prime}}.\lambda m_{l}\). On the reception of message \(m_{l}\) sent by \(p_{j}\) with \(m_{l}.DV[k^{\prime}]\) set at the super peer, by Lines 5-21 of Listing 14, we have that \(TT\) contains an element \((in^{\prime},out^{\prime})\) where \(out^{\prime}=k^{\prime}\) at position \(m_{k^{\prime}}.id\) which identifies a message \((m_{k^{\prime}}.id,in^{\prime})\) in the IDR protocol of the external group.
# DGSS-YOLOv8s: A Real-Time Model for Small and Complex Object Detection in Autonomous Vehicles
## 5 Conclusions  
Accurate perception with high processing speed in surrounding environments, especially detecting small, occluded, or irregularly shaped objects, remains a critical bottleneck for safe and efficient autonomous driving systems. Effectively addressing these challenges requires balancing high detection accuracy with stringent computational constraints. To tackle challenges including object occlusion and non-standard geometries in demanding traffic environments, this study presented DGSS-YOLOv8s, an upgraded version of the YOLOv8s detection framework. The core contribution lies in the synergistic integration of four innovative elements: (1) the DCNv3_LKA_C2f component strengthens the recognition of non-standard object geometries; (2) the Optimized-GFPN architecture boosts multi-level feature integration; (3) the Detect_SA mechanism expands contextual understanding with high efficiency; and (4) the Inner-Shape IoU metric enhances localization for occluded and truncated objects. Comprehensive evaluations on the BDD100K and KITTI datasets demonstrated the framework's advancements. The model showed mAP@50 improvements of 2.4% (BDD100K) and 4.6% (KITTI) over baseline YOLOv8s, with mAP@50:95 increasing by 1.2% and 9.8%, respectively. Significant progress appeared in vulnerable road user detection, including pedestrian recognition on BDD100K (mAP@50: 45.6% to 49.6%) and cyclist detection on KITTI (mAP@50: 81.2% to 87.3%). Notably, the solution enhanced small object detection (AP-small up by 9.7% on KITTI) while achieving high processing speeds  
Figure 11: Grad-CAM visualization of KITTI.  
Figure 10: Grad-CAM visualization of BDD100K.  
exceeding 100 FPS (103.1 FPS on BDD100K, 102.5 FPS on KITTI). Visual analysis further corroborated component contributions. Future research will continue to optimize the algorithm structure and evaluate its generalization on diverse, unseen real-world datasets to further enhance detection performance in autonomous driving scenarios.  
Conceptualization, S.C.; methodology, K.Y. and S.C.; software, S.C.; validation, L.C.; formal analysis, K.Y. and S.C.; investigation, K.Y.; resources, L.C. and K.Y.; data curation, K.Y.; writing--original draft preparation, S.C.; writing--review and editing, K.Y.; visualization, S.C.; supervision, L.C.; project administration, L.C.; funding acquisition, K.Y. and L.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Shanghai Municipal Commission of Science and Technology, and the project name 'Scenario Factory, a working platform for the generation and processing of autonomous driving simulation scenarios'. The grant number is 220H1053500.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflicts of interest.
# Glove Prototype for Feature Extraction Applied to Learning by Demonstration Purposes
## 3 Theoretical Concepts  
### Hand Model  
A simple skeletal dexterous hand model was used to understand the data that the glove outputs and later visualize in a simulator. It consists in a fairly accurate anatomical model but with some constraints and assumptions. Of the five fingers, four are very similar, and the thumb is slightly different; the index, middle, ring, and pinky fingers consist of four segments connected by joints.  
Starting near the wrist, the metacarpus is the largest and the one visible on the back of our hands.It has little to no movement; therefore, it is assumed not flexible, and because the four of them form, it is rigid and flat in this model. Connected to the metacarpus, the phalanges start with the proximal phalanx, which is the only phalanx capable of moving laterally. The intermediate and distal phalanges only rotate in one axis. All phalanx movement will be taken into account in the model. However, the sensors used are capable of estimating all kinds of rotation, and due to errors, they can estimate physically impossible finger orientations. So some assumptions can be made about these physical limitations and improve the estimation. The thumb behaves like three phalanges with the same properties and capabilities mentioned before but anatomically is composed by a metacarpus, proximal phalanx, and distal phalanx, so it differs from the other fingers by lacking an intermediate phalanx, and the metacarpus behaves like a proximal phalanx.  
Not always can we move our muscles independently. One common example is trying to move just one finger such as the ring finger and realize the pinky finger also moves. This study [34] reveals not only the fact but measures how much they move independently. Grasping this concept, one might suggest the same happens to our distal phalanx (tip of the finger). Although some individuals are capable of moving the distal phalanx independently, this work will assume it is not common in normal daily hand activity. The independence of the distal phalanx of the index, middle, ring, and pinky fingers will be considered negligible; therefore, their orientation estimation will be a function of the proximal and intermediate phalanges.  
The model shown in Figure 1 is very similar to the one proposed in paper [35] with some minor differences. The paper also used 2 IMUs per finger but instead tracked the proximal and distal interpolating the intermediate [35], while in this application, the interpolated phalanx is the distal. In the paper, the model does not consider the metacarpal thumb movement, while in this application, the movement of all three phalanges is tracked.