esa-sceva/satcom-chunk-collection huggingface dataset:
# Towards an Indoor Gunshot Detection and Notification System Using Deep Learning
## 3 Materials and Methods
### Firmware
#### 3.2.2 Central Server Software
The primary key of each table is marked using a key sign on the left side of the field name. database tables. If the UserID already exists in the user_tbl, then that user information is edited by updating the row with the data; otherwise, a new user is added by inserting a new row in the table. SQL queries [66] are executed from the software by connecting to the database to accomplish these tasks. Then, for each device listed in the data, the DeviceID is checked in the device_tbl. If the DeviceID already exists in the device_tbl, then the device information is updated with the data; otherwise new device data are added to the table. After that, the user_device_tbl is updated to assign the devices to the user. First, all the rows containing the UserID of the user are deleted. Then, for each device listed in the data, the UserID and the DeviceSN are inserted as rows in the table. In this way, the assignment of devices with the user is maintained whenever the user adds, edits, or removes a device. Emergency responder configuration data: This data string contains each field value of the er_tbl. Once the data arrive at the server: the data are parsed, saved in variables, and stored in the database table. The emergency responder's information is updated if the ERID already exists in the er_tbl, and a new emergency responder is added if the ERID does not exist in that table. Gunshot notification data: These data arrive at the server from the gunshot detector device when a gunshot event is detected. It contains the DeviceSN, and the event date and time. After the data arrive at the server: the location information of the device is queried from the device_tbl using the DeviceSN; a new row is inserted in the event_data_tbl to save the event information in the database; plotted on the map with a marker [67]; gunshot detection message is displayed; a warning sound is generated; FCM push notifications [68] are sent to the smartphones of the users of that device and all the emergency responders. To send the push notifications to each user assigned to the device: the FCM registration tokens for each user of the device are gathered from the user_device_tbl and user_tbl using a multiple table query. Each push notification contains the DeviceSN, the location information of the device, and the event date and time. Searching Gunshot Events  
The software implements a GUI where the user can choose a range of dates and times, a rectangular area on the map, or both, to search for gunshot events. An SQL query is made based on the chosen criteria and the result data are retrieved from the database. Then, the gunshot events from the result data are plotted on the map and the associated location and user information are displayed.
## 5 Conclusions
The ratio of \(\text{SIF}_{canopy}\) to \(\text{APAR}_{chl}\) is also different for different LAD functions with the highest ratio being for planophile plants and the lowest value for erectophile plants. For a given LAI and Cab content, the ratio ranges from 0.0068 \(\pm\) 0.0001 \(\upmu\)m\({}^{-1}\) sr\({}^{-1}\) for planophile plants to 0.0031 \(\pm\) 0.0002 \(\upmu\)m\({}^{-1}\) sr\({}^{-1}\) for erectophile plants at the O\({}_{2}\)-A band and from 0.0047 \(\pm\) 0.0001 to 0.0014 \(\pm\) 0.0002 \(\upmu\)m\({}^{-1}\) sr\({}^{-1}\) for both types of plant at the O\({}_{2}\)-B band. Similarly, based on the field observations, at the O\({}_{2}\)-A band, the ratios are 0.0076 \(\pm\) 0.0036 and 0.0052 \(\pm\) 0.0004 \(\upmu\)m\({}^{-1}\) sr\({}^{-1}\) for gold coin grass with plain leaves and winter wheat with spherical leaves, respectively; the corresponding values at the O\({}_{2}\)-B band are 0.0049 \(\pm\) 0.0030 and 0.0033 \(\pm\) 0.0004 \(\upmu\)m\({}^{-1}\) sr\({}^{-1}\), which corresponds well to the simulated results. These results show that the relationship between \(\text{SIF}_{canopy}\) and \(\text{APAR}_{chl}\) varies according to the plant type, which agrees with the dependence of \(\text{SIF}_{canopy}\) on GPP for specific plant types. However, the influence of the physiological structure of vegetation on the correlation between \(\text{SIF}_{canopy}\) and GPP awaits more detailed investigation in future studies. Therefore, the results of this study clearly show that there is a linear relationship between \(\text{SIF}_{canopy}\) and \(\text{APAR}_{chl}\), but that this relationship varies with the canopy biochemical components and canopy structure. The results also show that the reabsorption and scattering effects that occur during the radiative transfer from the photosystem to the canopy level should be corrected for before the remotely sensed \(\text{SIF}_{canopy}\) is linked to the GPP. The authors gratefully acknowledge the financial support provided by the National Key Research and Development Program of China (2017 YFA0603001), and the National Natural Science Foundation of China (41671349). [PERSON] and [PERSON] conceived and designed the research. [PERSON] conducted the experiments and data analysis, and prepared the manuscript. [PERSON] contributed significantly to the research method and the manuscript revision. [PERSON] and [PERSON] made important contributions to the field experiments. The authors declare no conflict of interest.
###### Abstract  
In this paper, model predictive control (MPC) based on an adaptive neural-fuzzy inference system (ANFIS) is proposed to realize control of an omni-directional service robot in path tracking. The weight of the cost function in a traditional MPC needs to be manually adjusted, and it is difficult to adjust to a satisfactory value. In order to improve the performance and the control accuracy of MPC, a fuzzy system trained by ANFIS is used to adaptively adjust the weight of MPC's cost function to reduce the error in the process of path tracking. The different simulation experiments are conducted to verify the performance of the proposed algorithm. The experimental results show that the distance error of MPC based on ANFIS is reduced by more than 50% under different paths compared with a traditional MPC, and the angle error is reduced by more than 70%. Meanwhile, the stability is increased by around 60%. The results show the feasibility and superiority of MPC based on ANFIS.  
omni-directional chassis; path tracking; model predictive control; adaptive neural-fuzzy inference system; weight of cost function +
Footnote †: journal: Applied Sciences  
Article Path Tracking Control of an Omni-Directional Service Robot Based on Model Predictive Control of Adaptive Neural-Fuzzy Inference System
## 4 Discussion
Table 7 presents a comparative analysis of our model's performance against traditional Doppler ultrasound diagnosis and AI-driven CT-/MRI-based methods [28]. The proposed approach is well suited for resource-limited clinical settings, where CT and MRI scanners may be unavailable due to cost and infrastructure constraints. Doppler ultrasound is widely accessible, portable, and radiation-free, making it a practical diagnostic alternative in such environments. Unlike AI-based CT analysis, which demands high computational power, our FNN-based model runs efficiently on standard workstations, enabling real-world deployment. Future studies will focus on validating its feasibility across diverse clinical settings, including low-resource healthcare facilities. A key limitation of this study is the relatively small size of the original dataset (565 images). While data augmentation was applied to expand the dataset to 3390 images, it is important to note that augmentation does not introduce new patient cases or variations in pathology, which are essential for improving model generalizability. The current dataset may not fully capture the diverse range of lung lesions encountered in real-world clinical settings. To address this limitation, future work will focus on expanding the dataset by incorporating Doppler ultrasound images from multiple clinical centers. This expansion will allow for a broader representation of different lung lesion types, cancer stages, and patient demographics, improving the model's generalizability and clinical applicability. Additionally, future studies will explore automated segmentation techniques to reduce dependence on manual annotation and will use multiple ultrasound devices, further enhancing the robustness of the proposed system.
# Face De-Identification Using Convolutional Neural Network (CNN) Models for Visual-Copy Detection
## 3 Proposed Methods
### Application Scenarios of the Proposed Models  
In this section, we describe the application scenarios of the two de-identification methods proposed in this study. Figure 1 intuitively illustrates the complete scenario of applying the de-identification method. The figure visually illustrates how personally identifiable information within illegal content can be protected by de-identifying it before it is shared over the network, especially on the client side, before it is delivered to media-sharing platforms and cloud storage via uploaders on the client side. On the client side, the device uploading the media detects the facial regions of the media through \"Face Detection\" before uploading the media, and only those regions are de-identified through \"De-identification\" and uploaded through \"Media Uploader\". On the server side, when the de-identified media are delivered through the \"Media Reciever\", features are extracted using the \"Feature Extractor\" to proceed with the visual-copy-detection process and are compared to the features in the database consisting of existing illegal-content features through the \"Copy Detector\", and illegal content is classified through the \"Illegal Contents Classifier\". The classification result is uploaded to the illegal-content database if it is illegal content or to the media-sharing platform or cloud storage where the client wants to upload it if it is legal content. Humans then review the newly uploaded content in the illegal-content database. Features are extracted through the \"Feature Extractor\" used for visual-copy detection and uploaded to the illegal-content-feature database, which is used to classify the newly uploaded content. This approach has the advantage of preserving personally identifiable information even if the content is intercepted during the upload process, protecting the integrity of the data while minimizing the potential spread of illegal content. Sections 3.2 and 3.3 introduce two models that can be applied to the scenarios described in this section.
## 2 Related Work
### Discussion and Desired Design Principles  
We can observe that the majority of schemes that are against selective routing attacks perform a downstream monitoring, which does not allow for a node to ensure monitoring coverage of all its neighbors. We can also observe that the majority of works use the following features to describe the node's behavior: packet dropping rate, packet forwarding rate, packet arrival rate, and number of Ackpackets (i.e., number of received packets). In Section 7, we will show that these features do not prevent the upstream-node effect and, hence, they incur poor detection accuracy and a high false positive rate. Thus, an IDS against the selective routing attack should have the following properties:  
* Effectiveness: the IDS should provide high detection rate while providing low false positive rate. This aim is ensured by using path-free features to describe the node's behavior.
* Full network monitoring coverage: the IDS should be able to monitor the whole network. To achieve this, each node should include downstream and upstream monitoring processes.
* Localized and fully-distributed: the IDS should be localized, i.e., the tasks of each node, e.g., data acquisition and data analysis should be based solely on the knowledge of its local neighborhood. The watchdog technique, in which a node listens promiscuously to packets passing by its neighborhood, is a good example of a localized solution as it does not incur any additional communication overhead. Additionally, the IDS should be distributed across different nodes, and does not rely on a centralized entity to avoid the single-point-of-failure problem.
* Minimized resource consumption: the IDS should be designed in a way that it can be lightweight and implementable in the resource-constrained sensor nodes by using only a small number of resources. Additionally, it is important that the IDS incurs less communication overhead to reduce energy consumption and prolong the node and network lifetime.
# Optical Link Design for Quantum Key Distribution-Integrated Optical Access Networks
## 4 Integration of CV-QKD Protocol in OANs  
Current OANs are primarily implemented by TDM-PONs and are expected to evolve toward TWDM-PONs in the future. Therefore, a QKD system integrated into OANs needs to be capable of operating in a TDMA environment with a power splitter. In the case of DV-QKD, the high cost of SPDs necessitates the adoption of a US transmission structure, inherently limiting the system to serving a maximum of two end users while maintaining a transmission distance of 10 km or more, as shown in Figure 4b. This configuration is feasible only if the launched power of the GPON signal is minimized to meet the receiver sensitivity requirement--a condition unattainable in beyond-10G PON systems. Furthermore, implementing a US-based DV-QKD system necessitates a complex scheduling algorithm, posing additional challenges to practical deployment. In contrast, CV-QKD offers a more viable solution for OANs. Leveraging commercially available off-the-shelf optical receivers, CV-QKD enables TDMA, making it a practical and cost-effective approach for secure quantum communication in OANs. Therefore, this section focuses on CV-QKD and explores its suitability for seamless integration with next-generation OANs.
## 3 Results
### Spatial Analysis  
A key advantage of the ALEXI/DisALEXI modeling framework is the spatial component--producing daily ET estimates over expansive areas that may encompass multiple vineyards, orchards, or other commodities. Figure 10 provides a contextual look at the mapping of ET, as well as E and T, by both models over multiple sites. These maps were created using Landsat-8 data collected during the month of June in 2021 (BAR = 06/16, SLM = 06/18, RIP = 06/18, OLM = 06/18, VAC = 06/09, WOD = 06/09). The red dots indicate the tower's location within the vineyard or orchard. These maps reflect tendencies discussed above, with DisALEXI-PM generally generating lower/higher estimates of E/T than DisALEXI-PT, despite the similarity in the representation of total ET.  
Figure 10: Spatial maps of daily E (**left two columns**), daily T (**middle two columns**), and daily ET (**right two columns**) in mm day\({}^{-1}\) for DisALEXI-PT and DisALEXI-PM (next to one another) for all sites on Landsat-8 overpass dates in June 2021 (specific dates listed in text). The red dot indicates flux-tower location.  
The differences between the DisALEXI-PT- and DisALEXI-PM-derived E, T, and ET for all sites on the same dates are shown in Figure 11. Additionally included are LAI and LST images on the same dates, acquired at the Landsat overpass time. The differences in E are lowest over BAR and VAC and greatest at OLA (differences \(\geq\) 2 mm day\({}^{-1}\) over most of the area). The areas of strongest difference in T and E between the models tend to occur where the LAI is low and the LST is high. The most extreme case is found in the field directly to the west of SLM, where the LAI values are the lowest (\(\sim\)0.5) and the LST values are the highest (\(\sim\)52 \({}^{\circ}\)C). This combination results in low ET for both DisALEXI-PT and DisALEXI-PM (Figure 10, right two columns), with little difference between the two (Figure 11). However, a comparison between E and T suggests total ET from DisALEXI-PT over this field is primarily driven by evaporation, whereas for DisALEXI-PM, it is primarily driven by transpiration. Although no ground-truth instrumentation exists for this field, visual inspection indicates a newly planted vineyard, characteristic of having little vine biomass and drip irrigation installed. If irrigation is substantial and creates a wetted soil, E is likely to contribute a larger fraction to ET, aligning with the DisALEXI-PT results. Although differences in E and T are nearly equal and opposite for most areas surrounding the sites, spatial patterns indicate that DisALEXI-PT produces larger ET estimates over areas of higher LAI and lower LST (except for OLA) on these dates when compared to DisALEXI-PM (Figure 10). In terms of total ET between DisALEXI-PT and DisALEXI-PM, we find little difference between the approaches, with values remaining \(\leq\) 0.75 mm day\({}^{-1}\) in either direction, aligning with the results presented in the prior sections.
# Optimal Deployment of Vector Sensor Nodes in Underwater Acoustic Sensor Networks
## 3 Optimal Deployment of Underwater Sensor Nodes  
The optimization algorithm we used for sensor node deployment was the VFPSO [25], as used in Reference [13]. This algorithm is a hybrid method that combines the advantages of the virtual force algorithm (VFA) [30; 31] and particle swarm optimization (PSO) [32; 33], which was also used in Reference [13]. The PSO can find a global optimal position by comparing the particle position of the current generation to the experiences achieved at previous generations, and the VFA has an outstanding ability to adjust the distance between sensor nodes using attractive and repulsive forces. Therefore, in this case the VFPSO was suitable to search the optimal positions of sensor nodes where the communication coverage rate was maximized while ensuring that the connectivity of the sensor nodes was maintained. A detailed description of the VFPSO algorithm for optimal sensor node deployment is provided in Reference [13].  
In this paper, one hundred sensor nodes were first randomly placed in the targeted area, and the VFPSO algorithm was then applied to the three-channel vertical-pressure sensor array and the single-vector sensor, based on the PS obtained in Section 2. The parameters used in the VFPSO algorithm for the optimal deployment simulation are given in Table 2.  
Figure 6 shows one hundred sensor nodes deployed optimally in the targeted area, and their connectivity, which were displayed on their communication PS. The efficiency of the optimal deployment could then be investigated using the communication coverage rate, which was defined as the ratio of the area of communication coverage by the sensor nodes to that of the targeted area [13]. As a result, the communication coverage rate simulated using the single-vector sensor nodes was estimated to be 95.3%, whereas that of the three-channel pressure sensor nodes was 85.2%. This result was expected because the single-vector sensor had better communication performance compared to the three-channel pressure sensor array, as mentioned in Section 2.3.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Optimal Deployment Parameters** & **Value** \\ \hline Loop number & 50 \\ Sensor node number & 100 \\ Attractive force weight & 0.01 \\ Repulsive force weight & 0.5 \\ Acceleration weight & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The parameters used in the simulation for optimal sensor node deployment.  
Figure 5: Communication PS simulated for the targeted area simulated for (**a**) the three-channel pressure sensor array, and (**b**) the single-vector sensor in the targeted area in February.
## 2 Background and Related Work
### Background
#### 2.1.2 Evaluation Metrics  
Normalized discounted cumulative gain (NDCG) [14] is a measure used to evaluate the quality of ranking models, particularly in recommendation systems. NDCG@5 and NDCG@10 refer to the NDCG scores calculated at the top five and top ten ranked items, respectively. These metrics help assess how well a model ranks the most relevant items at the top of the list, which is critical for ensuring that the most useful recommendations are prominently displayed to the user. Higher NDCG scores indicate better performance in placing relevant items at the top of the recommendation list.
## 5 Experimental Data Analysis  
### Dial Gauge Data Analysis  
To monitor the displacement changes of the surrounding rock in the opening cut, a total of five high-precision dial gauges were arranged within the model. Two gauges (Gauge 1 and Gauge 4) were placed on each side of the opening cut, while two gauges (Gauge 3 and Gauge 4) were located beneath the roof. Additionally, one gauge (Gauge 5) was installed on the bottom. These five gauges specifically monitored the convergence of the two sides of the opening cut, the subsidence of the roof, and the bulging of the bottom. The detailed arrangement is illustrated in Figure 9.  
Figure 8: Final loading condition of the model: (**a**) fracture in the roof, (**b**) cracks in the right-side roof, (**c**) damage condition of both sides, (**d**) roof fall.  
Figure 9: Arrangement of dial gauges.  
After the loading concluded, the cumulative displacement readings from Gauges 1 to 5 were as follows: 14.017 mm, 21.882 mm, 26.260 mm, 9.461 mm, and 0.736 mm, respectively. It is evident that the roof experienced significant subsidence during the compression process, while the sides exhibited varying degrees of damage, with no bulging phenomena observed. Experimental data from dial gauges are shown in Table 3, and cumulative displacement variation curves of the five dial gauges during loading are shown in Figure 10.
# Location-Awareness for Failure Management in Cellular Networks: An Integrated Approach
## 3 Proposed System
### Feature Engineering
#### 3.3.1 Indicators Pre-Processing  
Once the indicators are generated, they should be prepared for further stages. This phase covers the proper parsing and time adjustment between the different metrics while considering the accumulation of data coming from different sources and periodicities.  
Additionally, some indicators might be directly discarded due to different reasons: indicators that are generated from not enough samples to be considered statistically relevant (e.g., metrics whose normalized variance is less than 0.3 indicates lack of relevant information), metrics than are equal to others, or those that are constant or that do not vary enough to be considered for classification.
## 3 Experiment Results and Analysis
### Energy Dissipation  
The area enclosed by the load-displacement hysteresis loop and the energy dissipation factor E was used to investigate the energy dissipation capacity of the two sets of specimens, which can be found in JGJ/T 101-2015 \"Specification for seismic test of buildings\" [27].  
Figure 17 shows the total amount of total energy dissipation in both groups, and it can be observed that the trend of the FW-1 and FW-2 curves is similar. However, the values of specimen FW-2 are larger than that in FW-1.  
The energy dissipation coefficient (\(E\)) is shown in Figure 18 and defined as  
\[E=\frac{S_{ABC}+S_{CDA}}{S_{OBE}+S_{ODF}} \tag{2}\]  
where \(S_{ABC}\) and \(S_{CDA}\) in the numerator are the areas of the hysteresis curves \(ABC\) and \(CDA\), and \(S_{OBE}\) and \(S_{ODF}\) in the denominator are the areas of \(OBE\) and \(ODF\) (as shown in Figure 19). Specimen groups show approximately the same growth trend. In general, the values of FW-2 are larger and faster than those from FW-1. The pendulous Z-panel connector group has better participation in energy dissipation and improves stiffness than the hooked bolt connector.  
Figure 16: Stiffness degradation of FW-1 and FW-2.  
Figure 17: Energy dissipation of FW-1 and FW-2.