esa-sceva/satcom-chunk-collection huggingface dataset:
# The Effectiveness of Unimodal and Multimodal Warnings on Drivers' Response Time: A Meta-Analysis
## 1 Introduction
### Unimodal Warning Systems  
New in-vehicle technologies are constantly developing, especially in driving warning systems. As early as 1992, [PERSON] and [PERSON] discussed about the human factors that had to be taken into consideration when designing warning systems [6]. Many researchers have later conducted plenty of experiments to explore the best unimodal or multimodal (including bimodal and trimodal) sensory modality for delivering warning signals to drivers [7; 8; 9; 10].  
Among the three main unimodal warning systems, unimodal visual, auditory, and tactile warning systems each has their own benefits and drawbacks when it comes to their effectiveness in alerting drivers. Visual warnings contain concrete and directional information, making it easier for drivers to determine the direction of a specific upcoming situation (e.g., a pedestrian stepping off the pavement). However, since the premise of visual information processing is seeing the signal, drivers might fail to see a visual warning signal if it is presented outside of their visual fields. Even if the signals are presented in a potentially noticeable location, being in a complex driving environment would possibly induce drivers' visual overload [11], which further hinders driving safety. Additionally, inattentional blindness, or \"looked-but-failed-to-see-errors\", might occur if the driver is intently focused on the road, resulting in poor sensation and perception of the visual warning signals [12; 13; 14].  
Auditory warnings contain useful information (e.g., notification of steering wheel control and brake request) without taking up drivers' visual resources; yet, two shortcomings of auditory warnings have been recognized. First, inattentional deafness is likely to occur under high workload conditions, in which drivers listen but fail to perceive auditory information [15; 16]. Second, noises in the driving environment, such as background music or the sound of the radio, would impair the efficacy of auditory warnings [17].  
As for tactile warnings, signals are delivered to drivers through direct body contact. The mediums of tactile information transmission include but not limited to steering wheel, dashboard, seat belt, seat, pedal, as well as drivers' clothes and wearable devices [18]. By adjusting the vibrating location, intensity, and size of the contact area, drivers are believed to be sufficiently informed by the tactile warnings with useful information [19; 20; 21]. Yet, the turbulence caused by driving on a rough road and the thickness of drivers' clothing are common exogenous factors which could reduce the effectiveness of tactile warnings [22].
## 4 Experimental Evaluation  
Once the training stage was completed, we established the experimental conditions to evaluate the identification and text prompted verification tasks. We developed five different multitask networks with the architecture and training conditions described in previous sections. For each iteration the subjects that integrated the target clients and impostors sets were randomly chosen. After evaluating the identification and text prompted tasks of each network, we calculated the average and standard deviations of the corresponding metrics. The identification and text prompted verification results are organized according to the SNR value or image transformation associated to a speech signal and face image, respectively.
## 5 Science Mapping Analysis Results
### Conceptual Evolution Map  
Referring to the strategies by [PERSON] and [PERSON] [84], we conducted a conceptual evolution analysis. The period 1999-2008 contained 798 keywords, 328 of which appeared again in the period 2009-2013, while the remaining 470 keywords did not. Thus, the similarity index between the two periods equaled 41%. The period 2009-2013 contained 1429 keywords, 1101 of which were newly emerged. In the period 2014-2018, 557 of these keywords appeared again, while 872 did not. The similarity index between the latter two periods equaled 39%. Given that there were more articles in the period 2014-2018, there were 1951 keywords included, of which 1394 have newly emerged. The importance of keywords was constantly changing across time (Figure 6). With terminology changes, diverse keywords were adopted to depict contents of the NLP-enhanced clinical trial studies, with some new keywords emerging while others vanished. For instance, keywords _NURSING-EDUCATION_, _SHORT-TERM-MEMORY_, _ASSOCIATE-LEARNING_, _ATITITUDE-TO-COMPITER_, _BIOMEDICAL-GRID_, and _CANCER-SCREENING_ appeared only in the first period. However, keywords such as _SEMANTICS_, _SYSTEM_, _ONTOLOGY_, _RANDOMIZED-CONTROL-TRIAL_, _ARTIFICIAL-INTELLIGENCE_, _EVIDENCE-BASED-MEDICINE_, _NATURAL-LANGUAGE-PROCESSING_, and _DECISION-SUPPORT-SYSTEM_ were presented in all three periods. The evolution map depicted in Figure 6 was very dense. Some keywords belonged simultaneously to more than one area, while others were isolated (e.g., _ONTOLOGY-BASED_ and _QUALITY_) or failed to be continuously prominent.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Period** & **Name** & **Articles** & **Citations** & **H-index** \\ \hline \multirow{6}{*}{1999–2008} & _HUMAN_ & 43 & 1889 & 20 \\  & _COMPUTTER-PROGRAM_ & 35 & 1833 & 16 \\  & _SYSTEM_ & 26 & 639 & 17 \\  & _QUALITY_ & 11 & 815 & 7 \\  & _ONTOLOGY-BASED_ & 3 & 86 & 3 \\ \hline \multirow{6}{*}{2009–2013} & _HUMAN_ & 79 & 1703 & 24 \\  & _MEDICAL-REORD_ & 51 & 1132 & 19 \\  & _ARTIFICIAL-INTELLIGENCE_ & 48 & 1366 & 23 \\  & _FACTUAL-DATABASE_ & 46 & 1232 & 18 \\  & _RANDOMIZED-TRIAL_ & 43 & 1157 & 19 \\  & _MEDICAL-APPLICATION_ & 41 & 723 & 14 \\  & _NATURAL-LANGUAGE-PROCESSING_ & 24 & 654 & 13 \\  & _OUTCOME_ & 8 & 104 & 6 \\ \hline \multirow{6}{*}{2014–2018} & _PROCEDURE_ & 106 & 1166 & 20 \\  & _MACHINE-LEARNING_ & 73 & 822 & 17 \\  & _RANDOMIZED-CONTROL-TRIAL_ & 72 & 932 & 17 \\  & _ELECTRONIC-HEALTH-RECORD_ & 69 & 735 & 14 \\  & _ACCURACY_ & 60 & 677 & 14 \\  & _MEDICAL-INFORMATICS_ & 53 & 646 & 14 \\  & _STANDARD_ & 51 & 568 & 14 \\  & _FEMALE_ & 49 & 576 & 13 \\  & _ELIGIBILITY-CRITERION_ & 42 & 662 & 15 \\  & _COMPUTER-ASSISTED_ & 36 & 440 & 11 \\  & _GENETICS_ & 33 & 353 & 10 \\  & _DIABETES-MELLITUS_ & 23 & 264 & 8 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Examples of inclusion and exclusion criteria for the manual verification.
# Multi-Connectivity-Based Adaptive Fractional Packet Duplication in Cellular Networks
## 3 Multi-Connectivity in Cellular Networks
### Carrier Aggregation  
Introduced in the advanced long-term evolution (LTE-A) standard, carrier aggregation (CA) is a means to combine together two or more carrier components (CC) to increase the transmission bandwidth capacity. This increment in capacity is achieved on the DL and the UL, with the former usually higher than the latter. The concept of carrier aggregation was first introduced in Rel 10 of the 3 GPP where a maximum of five carrier components was allowed on the downlink channel.  
Since then, this concept has evolved and allows multiple CA capabilities across different technologies. CA can be inter-band, meaning CC aggregation between different frequency bands, or can be intra-band, which means CC aggregation within the same frequency band. The intra-band is further categorized either as a contiguous or a non-contiguous CA, which is explained below. The CA is technically a MAC-layer split and is implemented on the physical layer.  
* Inter-Band CA: As shown in Figure 0(a), CCs from different frequency bands are combined together.
* Intra-Band Contiguous CA: As per Figure 0(b), CCs from the same frequency band, which are adjacent to each other, are combined together.
* Intra-Band Non-Contiguous CA: As shown in Figure 0(c), CCs from the same frequency band, which are non-adjacent or fairly spaced apart in the frequency domain, are combined together.
# A Survey of Algorithms and Systems for Evacuating People in Confined Spaces
## 2 Evacuation Wayfinding Systems
### Integrated Systems Based on Smartphones and Cloud Computing  
Although the hybrid emergency response systems that integrate on-site WSNs and off-site cloud servers can avoid the problems caused by the limited computing power of WSNs, the disadvantages of the limited battery life-time of the WSNs, as well as the high likelihood of system malfunction during an emergency still remain. Hence, many of the studies have replaced WSNs with smart phones carried by evacuees to build more flexible systems. For example, the work in [31] presented a building fire evacuation system that consisted of radio frequency identification (RFID) sensor tags, mobile phones with RFID readers and a back-end cloud server; since signals from the global positioning system were unavailable inside built environments, RFID sensor tags were used to record the temperature and location information; when a fire breaks out, mobile phones carried by evacuees would periodically sense the RFID signals and upload to the cloud server; the cloud server would then calculate the shortest safe route for each civilian with respect to the distance to the exit and the summed temperature along the route. The research in [32] proposed a smart cloud evacuation system (SCES) to post emergency messages and plans to residents in a built environment; in the front-end, a wireless intelligent sensor network (WISN) that integrated a WSN with smartphones was utilised to collect information; in the back-end, a cloud-based decision-making system was used to analyse the uploaded multimedia information (such as voice, text, images, etc.) and calculate escape paths with a 3D simulator.  
The study in [33] proposed an infrastructure-less evacuation wayfinding system to guide evacuees out of hazardous buildings with the aid of smart phones carried by evacuees and an off-site cloud-based decision support system (CDSS); evacuees could locate themselves by taking a snapshot of pre-deployed landmarks (e.g., door signs) in the vicinity and uploading it to the CDSS for location identification; the CDSS computed congestion-aware paths with the shortest time to exits for evacuees based on their uploaded locations; evacuees were guided to exits in loose groups with the assistance of a combination of the social potential fields algorithm and a cognitive packet network-based algorithm.  
To reduce the likelihood for the battery power of smart phones to drain during energy-hungry communication among smart phones and the CDSS, a power-aware communication protocol was also presented to balance the remaining battery power of smart phones by relaying sensory information via more energy-efficient short-range communication techniques before uploading to the cloud server through 3G. Indeed, there has also been substantial research in optimising the balance between rapid decision-making and the energy consumption needed to achieve fast decisions [34; 35].
## 1 Introduction
Finally, via a performance analysis, the system's effectiveness in protecting crops and ensuring animal safety is evaluated. By adopting this approach, a comprehensive solution safeguards agricultural productivity and fosters a harmonious coexistence between farming communities and wildlife. The significant contributions of the research are as follows:  
* A comprehensive dataset is compiled, comprising high-resolution images of endangered animals near agricultural areas. The images of those species hold significance in maintaining an ecological balance within biodiversity while also posing a threat to agricultural lands and they are primarily collected. * The dataset has been meticulously annotated with bounding boxes and labels, enabling the creation of a robust object-detection model. * An optimized version of the object-detection model, YOLOv8, has been introduced. It demonstrates remarkable reliability in recognizing and identifying animals, even under adverse environmental conditions. Hyperparameters such as batch size, learning rate and epoch number have been fine-tuned to enhance the stability of the model. * The animal-detection model's integration into IoT systems allows for real-time surveillance of agricultural areas, providing swift identification of animal intrusions. * To mitigate potential conflicts, the system activates an alarm when animals approach the fields, prompting them to retreat without resorting to harmful measures. This ensures the safety of both humans and animals. * The cloud-based systems and communication protocol swiftly relay messages to farmers about animal intrusions, enabling prompt response and effective crop protection. This research is organized as follows: Section 2 presents a comprehensive review of the existing research concerning the detection of endangered animals through the utilization of different hybrid methodologies and a proper gap analysis is also performed for a better understanding of the limitations of the state-of-the-art works. Section 3 contains a detailed discussion of the research methodology, including introducing an optimized YOLOv8 object-detection model and integrating our IoT system. Section 4 summarizes the result section of the research employing different performance metrics to assess our technique's robustness by integrating a runtime cost analysis and comparison with state-of-the-art models. Section 5 broadly discusses the proposed system's novelty and Section 6 illustrates the limitations and future scope of the study. Finally, the research is concluded in Section 7.
# _Pat-in-the-Loop_: Declarative Knowledge for Controlling Neural Networks
## 4 Pilot Experiment
### Experimental Set-Up  
We experimented with the Question Classification dataset [35], which contains 5242 training questions and 500 testing questions. We focused on the _coarse grain_ classification problem with 6 target classes: Abbreviation (ABBR), Description (DESC), Entity (ENTY), Human (HUM), Location (LOC), and Numeric (NUM).  
The Pat-in-the-Loop (see Figure 2) used in the experiments has the following configuration. Distributed trees \(\boldsymbol{W}_{dt}\boldsymbol{x}^{T}\) are encoded in a space \(\mathbb{R}^{d}\) with \(d=4000\). The decaying factor of tree kernels is \(\lambda=0.6\). The module \(\mathrm{NN}(\boldsymbol{W}_{dt}\boldsymbol{x}^{T},\boldsymbol{x}^{r})\) is a multi-layer perceptron that combines two multi-layer perceptrons: \(Synt(\boldsymbol{W}_{dt}\boldsymbol{x}^{T})\) and \(Sem(\boldsymbol{x}^{r})\). \(Synt\) exploit syntactic information and its output is 1800. \(Sem\) exploits a Bag-of-Word model of the input with word embedding input of 300 from fastText [36] and output of 180. \(Synt\) and \(Sem\) are concatenated and feed a multi-layer perceptron with two layers: 100 and 6. Finally, \(\boldsymbol{W}_{H}\) has an input dimension of \(d=4000\) and an output dimension of 6 where 6 is the number of output categories required in the question classification dataset [35]. In this case, we have opted for \(\boldsymbol{W}_{H}\), which encodes 6 different characteristics where each characteristic is linked to an output class. Then, the output of \(\mathbf{W}_{H}\) and the output of \(\mathrm{NN}(\boldsymbol{W}_{dt}\boldsymbol{x}^{T},\boldsymbol{x}^{r})\) are concatenated in a single vector that feeds a final linear layer. We used a ReLU activation function among layers. The last activation function is a softmax. The optimizer is Adam [37]. All experiments were run for 20 epochs in Keras [38]. Finally, we used the CoreNLP constituent-based parser [39] for parsing questions.  
We performed a 3-fold cross validation with the training set to accumulate misclassified examples for the human learning loop. [PERSON] inspected these examples by using heat parse tree and encoded the declarative rules in \(\boldsymbol{W}_{H}\) (Table 3). The encoded declarative rules in \(\boldsymbol{W}_{H}\) are encoded from this example (Figure 1) and then injected as rows in matrix \(\boldsymbol{W}_{H}\) as described in Section 3.4 We compared three systems: \(BoW\) that contains only the word embedding used as a bag-of-word; _PureNN_ that is the system without human knowledge; and _HumNN_ that is the full system with [PERSON]'s declarative knowledge.
## 4 Discussion
### Prioritization of Forest Plantations to Improve Connectivity  
Forest plantations may contribute to prioritize connectivity and safeguard local threatened species [56]. We presented a network of corridors under the current afforestation program in Ecuador and identified potential corridors that combine various current land uses (Figure 5). Because they have the potential to improve the impact of forest plantations in terms of biodiversity, these findings are relevant to the National Forest Plantation Program. First, native forest fragments have high conservation value, and plantations can improve their connectivity by incorporating spatial structural complexity for specialist species with dispersal ability across the landscape [54]. Second, even though the technical viability of this multi-species plantation framework needs to be further evaluated, it provides preliminary evidence of contributing to improving corridor networks, ensuring the persistence of species with distinct habitat preferences, sensitivity to native forest replacement, and movement capacity. To avoid clearing of native forests within the potential corridors that could be used in the connectivity network, forest plantation and forest management must include biological corridors as a critical task in their planning [59]. As a result, preservation of landscape elements that make a more significant contribution to landscape connectivity should be a top priority for the sustainable planning of forestry landscapes in Ecuador. By specifying the effects of plantations on the landscape-scale movement of multiple species, the temporal dynamic of natural and planted forestry landscapes must be considered, and focal species should be identified. Additionally, the socio-ecological framework of forestry of rural communities is not well understood. Therefore, by restoring habitat, forestry could contribute to the improvement of threatened species' population viability, connecting the landscape to avoid detrimental effects on animal and plant populations. Promoting sustainable forest plantations may contribute to establishing a link between forest sustainability and conservation biology, as well as the care and protection of endangered and endemic species.  
Figure 6: Corridor in the Manabi province (Ecuador) related to native and reforested forests in two locations (North—A and Central—B) and two scenarios (**A1**,**B1**) Land use scenarios 2018 (LUSES2018), (**A2**,**B2**) Land use scenarios 2018+\(+\)forested (LUSES2018 \(+\) forested). The reforested patches in scenario LUSS2018 \(+\) forested are in grey color. The corridor was divided into six categories: no corridor (0), very low (1–13), low (14–55), medium (56–138), high (139–291) and very high (>292).
# Fire Detection and Geo-Localization Using UAV's Aerial Images and Yolo-Based Models
## 3 Outline of the Proposed Framework  
In this paper, we proposed a fire detection and geo-localization scheme, as shown in Figure 1, where a two-stage framework is shown. The ultimate goal of the fire detection and localization system using UAV images was the timely identification and precise spatial localization of fire-related incidents. The whole process started at the first stage with data collection and integration. Many data sources were used to compile a large dataset of images that represented various visual contexts. The second key component of the proposed framework was the data preparation, which included data augmentation, labeling, and splitting. Then, a third critical component of the system entailed the utilization of innovative YOLO models for object detection, specifically geared towards recognizing three primary classes: fire, non-fire, and smoke. These YOLO models were trained using the labeled images from the data preparation phase in order to identify fire-related objects amidst complex visual contexts. The best performing YOLO model was then used in the second stage of the proposed framework to detect and localize various areas in the images captured by UAVs equipped with high-resolution cameras.  
The final and arguably most vital component of the system revolved around precise fire localization, achieved using advanced stereo vision techniques. This stage comprised several interrelated processes. Initially, a meticulous calibration process was conducted to precisely align the UAV's cameras and establish their relative positions. This calibration was foundational, serving as a cornerstone for the subsequent depth estimation. The depth estimation, achieved through stereo vision, leveraged the disparities between the corresponding points in the stereo images to calculate the distance to objects within the scene. This depth information, in turn, fed into the critical position estimation step, allowing the system to calculate the 3D coordinates of the fire's location relative to the UAV's perspective with a high degree of accuracy. Collectively, this integrated system offered a comprehensive and sophisticated approach for fire detection and localization. By harmonizing the data collection from UAVs, YOLO-based object detection, and advanced stereo vision techniques, it not only identified fire, smoke, and non-fire objects but also precisely pinpointed their spatial coordinates. This precision is paramount for orchestrating swift  
Figure 1: Fire detection and geo-localization proposed framework.  
and effective responses to fire-related incidents, whether in urban or wildland settings, ultimately enhancing the safety of lives, property, and the environment.