esa-sceva/satcom-chunk-collection huggingface dataset:
# Object Detection in Remote Sensing Images by Combining Feature Enhancement and Hybrid Attention
## 5 Conclusions  
Considering the various objects with large-scale variations, arbitrary orientations, as well as background noises in remote sensing images, this paper proposes an object detection method (FEHA-OD) for remote sensing images, which focuses on feature enhancement and hybrid attention. It proposes FEFN with different receptive fields to reorganize and fuse feature maps, which further improves the multi-scale feature representation and addresses the large-scale variations. Moreover, to address the problem of background noises, which may overwhelm the features of small objects, the HAM module is designed to increase the positioning accuracy of objects, as well as to obtain the informative channels. It can suppress background noises and relatively enhance the small object features. Furthermore, this paper regresses the box parameters for objects with arbitrary orientations by using box boundary-aware vectors in the prediction module. Furthermore, a multi-task loss function is used to combine the above modules, and the final detection result is determined. Experiments on public dataset DOTA show that the proposed method achieves 75.02% mAP, showing an improvement of 2.7% mAP compared with BBAvectors. The robustness and adaptability of FEHA-OD is also verified by the experimental results in a variety of complex scenarios. The optimization of computational speed will be the subject of our future work.  
Conceptualization, J.Z. and H.W.; methodology, J.Z. and Z.Z.; software, T.W. and Z.Z.; validation, J.Z., T.W. and Z.Z.; formal analysis, H.W.; investigation, T.W.; resources, T.W.; data curation, Z.Z.; writing--original draft preparation, T.W.; writing--review and editing, J.Z.; visualization, Z.Z.; supervision, J.Z.; project administration, H.W.; funding acquisition, J.Z. All authors have read and agreed to the published version of the manuscript.  
National Nature Science Foundation of China (No. 61876014). the Fundamental Research Funds for Central Universities (3122020044).  
No applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Publicly available datasets were analyzed in this study. This data can be found here: [[[https://captain-whu.github.io/DOTA](https://captain-whu.github.io/DOTA)]([https://captain-whu.github.io/DOTA](https://captain-whu.github.io/DOTA)), accessed on 15 June 2022].  
Figure 10: Visualization results of our method.
_J. Low Power Electron. Appl._**2014**, \(4\), 252-267; doi:10.3390/jlpea4030252  
Journal of  
_Low Power Electronics_  
_and Applications_  
**ISSN 2079-9268**  
www.mdpi.com/journal/jlpea  
_Article_  
**39 fJ/bit On-Chip Identification of Wireless Sensors Based on Manufacturing Variation**  
**[PERSON] *, [PERSON] and [PERSON]**  
Department of Electrical & Computer Engineering, University of Virginia, 351 McCormick Rd.,  
Charlottesville, VA 22904, USA; E-mails: [PERSON]DDRESS] (B.H.C.);  
[EMAIL_ADDRESS] (T.N.B.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]  
_Received: 8 July 2014; in revised form: 18 August 2014 / Accepted: 4 September 2014 / Published: 12 September 2014_  
**Abstract:** A 39 fJ/bit IC identification system based on FET mismatch is presented and implemented in a 130 nm CMOS process. ID bits are generated based on the \(\Delta V_{T}\) between identically drawn NMOS devices due to manufacturing variation, and the ID cell structure allows for the characterization of ID bit reliability by characterizing \(\Delta V_{T}\). An addressing scheme is also presented that allows for reliable on-chip identification of ICs in the presence of unreliable ID bits. An example implementation is presented that can address 1000 unique ICs, composed of 31 ID bits and having an error rate less than \(10^{-6}\), with up to 21 unreliable bits.  
**Keywords:** chip identification; low-power electronics; radio-frequency identification; wireless sensor networks; PUF
## 4 Discussion
### Diurnal Relationships and Sun-Sensor Geometry  
The diurnal patterns presented in this study (Figures 6 and 7) are likely to be controlled by both physiological and optical (directional) factors. Although the tower mounted optical system used in this study was oriented in a so called \"hot spot\" region of the canopy, when the solar angle varies through the day, so too will the fractions of sunlit and shaded foliage in the sensors field of view. The influence of directionality will therefore inevitably generate differing proportion of SiF coming from the scene viewed by the spectrometer. Understanding and decoupling the influence of both structure and physiological response from SiF retrievals is a much needed next step, and one that models have thus far been used to understand ([PERSON] and others, 2017; [PERSON], 2017). The passage of clouds may add orders of magnitude difference to the retrieved SiF (Figure 7D) via a significant change in the measured irradiance and subsequent depth of the O\({}_{2}\)-A band ([PERSON] and others, 2017). In the results presented here the peaks of SiF and PPFD do not match, which is likely due to be dominated by the changes in solar geometry. Indeed the hourly fraction of sunlit and shaded leaves within the sensors FOV will be varying, as will the presence of multiple scattering within the canopy. Future work should undoubtedly focus on such shadow fraction changes, along with the impacts on SiF, in this ecosystem.  
The influence of sun-sensor geometry on solar induced fluorescence itself is receiving increasing attention ([PERSON] and others, 2017; [PERSON], 2017) with evidence from satellite retrieval analysis and indeed modelling studies mounting. The have reported noticeable angular influences each generating differing ranges of SiF values, which is similar to the effect of sun-sensor geometry on canopy reflectance data ([PERSON] and others, 2017; [PERSON], 2017). A bowl shaped response has been reported from backward to forward scattering directions consistently in ground and model based observation ([PERSON] and others, 2017; [PERSON], 2017) (though not evergreen canopy specific). While it is not possible to disentangle the impact of sun-sensor geometry explicitly here, its influence remains highly probable.
## 1 Introduction
### Metrics and Evaluations  
To evaluate the model, the most commonly accepted metrics for evaluating deep learning models was used: Precision (Prec.) (Equation (2)), Recall (Rec.) (Equation (3)), F1 score (F1) (Equation (4)). These metrics are obtained as follows:  
\[Precision(Prec.)=\frac{TP}{FP+TP} \tag{2}\]  
\[Recall(Rec.)=\frac{TP}{FN+TP} \tag{3}\]  
\[F1 score(F1)=\frac{2\times TP}{2\times TP+FP+FN} \tag{4}\]  
where  
* True Positive (TP): DGA classified correctly in its class;
* False Positive (FP): DGA from another class misclassified in a given class;
* False Negative (FN): DGA of one class misclassified into another class.  
To evaluate the effectiveness of the proposed model and the application of NLP techniques to domain names, an additional model derived from the main model, which will be referred to as Model 1, was tested on the same dataset. One model without the embedding layer in the input will be referred to as Model 2. The basic structure of the two tested models is presented in Table 3. For better visualization, the Figure graphically presents the results obtained by the models compared in each of the metrics side by side. Additionally, five other models from recent works were trained and tested on the same dataset: the Bilbo model presented in [35], a model using the pre-trained transformer DistilBERT, a lighter and faster version of the BERT transformer proposed in [36], as well as simple CNN, CNN+LSTM, and RNN models proposed in [28].  
The structures of the models tested for comparison were obtained from the GitHub repositories of each author, cited as supplementary material in their works. This direct comparison between the results is important to demonstrate the robustness of the proposed model and its alignment with the state-of-the-art research developed in this regard. Table 3 presents the basic structure of each of the works and models compared on the same training and test dataset.  
To evaluate the effectiveness of applying the proposed class increment technique, Model 1 was subjected to training to classify among 60 DGA families and then a new family was added to the model using the proposed technique, and metrics in each of the training steps were noted. The metrics of the same Model 1 trained exclusively with the reduced dataset were also noted to compare results. This evaluation was repeated three times using three different DGA families as incremental classes, with different numbers of examples in the dataset, namely Metastealer with 80,000 examples, Suppobox with 10,350 examples, and Darkshell with 1049 examples in the dataset.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **Structure** \\ \hline Model 1 (Proposed) & CNN+Embedding+ReLU+MaxPooling \\ Model 2 (Proposed) & CNN+ReLU+MaxPooling \\ Bilbo [35] & Embedding+CNN+LSTM \\ DistilBERT [36] & DistilBERT+DNN \\ CNN [28] & CNN Simple \\ CNN+LSTM [28] & CNN+LSTM Simple \\ RNN [28] & RNN Simple \\ \hline \hline \end{tabular}
\end{table}
Table 3: Tested models and their basic structure.
# Cyber-Physical Attack Detection and Recovery Based on RNN in Automotive Brake Systems
## 2 Sensor Attack Detection
### Data Acquisition  
In a proving ground of Korea Intelligent Automotive parts Promotion Institute (KIAPI), we drove Hyundai Motor's Avante MD to acquire experimental data. The distance of the proving ground is 400 meters, and the vehicle was driven straight. A driver fully stamps down on the pedal of the accelerator until the vehicle reaches 60 km/h, which is the speed limit in two-lane urban roads. If the vehicle arrives beside a traffic cone, it is stopped by stepping on the brake pedal fully. Twenty-five datasets are gathered through 25 trials. They are separated into 15, 5, and 5 sets for training, validation, and testing, respectively. The training datasets are employed to be learned in RNN models and set both weighted values and threshold values of vehicle speed estimation algorithms. The validation datasets are exploited to validate a trained RNN model and tune hyper-parameters of the RNN model. We obtain the four wheel speed, brake pressure, and vehicle velocity data from CAN messages at 100 Hz using OBD-II. Vehicle velocities are generally calculated based on the four wheel speed data. Unfortunately, we only know final vehicle velocities calculated as Hyundai Motor does not release its own calculation algorithm for the vehicle velocities. In addition, obtained wheel speed data are expressed to the third decimal place, while vehicle velocity data are displayed as an integer only. Therefore, there are some errors between the final vehicle velocity data and the wheel speed data which are input to our algorithm as shown in Figure 4. We ignore these errors and compare how well our system estimates the vehicle speed with other algorithms.  
Figure 4: Plotting wheel speed and vehicle speed data.  
Figure 3: Selected RNN architecture. BN: Batch Normalization.
## 8 Specific Groups
We propose a DXA-scan (including DXA-based vertebral fracture assessment where available) in all patients at diagnosis of the neuromuscular disease and subsequently at every one to two years, depending on the overall BMD trajectory (and more often in the patient is on glucocorticoid therapy as in boys with DMD). A digital X-ray radiogrammetry of the hand to measure the BHI is a feasible alternative when it is not possible to perform a DXA scan (movement during measurement, metallic implants, contractures, scoliosis) or age < 3 years (no normative values for DXA available for this age group). In order to detect vertebral compression fractures, a lateral thoracolumbar spine x-ray should be performed when DXA-based vertebral assessment is not possible. Children using Ketogenic Diet  
Treatment with a ketogenic diet (KDT) is a nonpharmacologic intervention for intractable childhood epilepsy. The diet consists of a high fat (mostly 80% fat), adequate protein, and a low-carbohydrate percentage that mimics a state of fasting. In intractable childhood epilepsy, treatment with a ketogenic diet can lead to a beneficial effect on seizure control, regardless of age or seizure type [102]. The ketogenic diet is associated with skeletal demineralization with a reduction of BMD between 0.16-0.6 z-score/year [68; 103; 104]. This results in an increased incidence of bone fractures in up to 21% of the children during a follow-up period of 6 years or more [105]. The ketogenic diet results in a high \"acid load\" via the ketone bodies, alterations in vitamin D levels, and lowering growth factors [105]. This also causes failure to accrue bone at a normal rate and increased bone loss due to increased bone absorption [106]. Prophylactic supplementation with calcium and vitamin D was considered mandatory for all children on a ketogenic diet, but this may be insufficient in preventing ongoing declines in BMD Z-scores [68; 103]. Moreover, during long-term follow-up of KDT, an increased incidence of kidney stones has also been found, mostly due to hypercalciuria. Even frank hypercalcemia has been described along this spectrum [106]. Therefore, prophylactic supplementation of calcium and vitamin D is challenging. Screening with DXA scans to evaluate for a diminished BMD is recommended in children on KDT for over 2 years by 12 of the 25 [48%) centers participating in the International Ketogenic Diet Study Group [102]. The main recommendation of the recent report of this study group was that screening with DXA scans should be standard. Interventions should be considered in case of abnormal results. DXA scans should be repeated a year afterward [102]. These findings support the need to monitor bone strength in individuals on KDT, by following parameters, such as calcium intake, vitamin D deficiency, and activity levels. Developing kidney stones remains a major complication of KDT and has to be taken into account when assessing a patient on the diet [68].
## 3 System Resource Optimization Algorithm
### Utility Function  
After determining the transfer probability, the integrated cost function considering the energy consumption and delay in the model is introduced into the MDP algorithm. First, the total utility function is defined as follows:  
\[q(s,a)=\varphi_{1}f(s,a)-(1-\varphi_{1})g(s,a), \tag{44}\]  
where \(f(s,\,a)\) and \(g(s,\,a)\) are the reward functions for task completion within the deadline and the integrated cost function of the tasks, respectively. \(\varphi_{1}\) represents the weight factor of the reward function and the cost function in the utility function. The cost function consists of energy consumption cost and delay cost, which is given as follows:  
\[g(s,a)=\varphi_{2}T_{0}(s,a)+(1-\varphi_{2})E_{0}(s,a), \tag{45}\]  
where \(\varphi_{2}\) is the weight factor in the cost function for the delay and energy consumption costs. The transmission delay and energy consumption cost functions are determined by the offloading decision. The total utility function is related to the cost and reward functions, and the total utility function can be obtained by Algorithm 1.  
```
1:Set \(B\), \(h_{0}\), \(\sigma\), \(\tau\), \(T_{\text{deadline}}\), \(E_{\text{deadline}}\), \(\delta_{u}\), \(P_{u}\), \(\delta_{\text{uav}}\), \(P_{uav}\), \(\delta_{\text{G}MEC}\)
2:Initialize \(Z\), \(d\), \(E_{u\sim c}\), \(E_{\text{level}}\)
3:Step 1:Calculate \(N_{z}\), \(E_{u}\), \(T_{u}\), \(T_{\text{uav}}\), \(E_{\text{autv-z}}\), \(T_{\text{G}MEC}\), \(E_{\text{G}MEC\cdot Z}\), \(E_{u\sim\tau}\), \(E_{\text{autv-z}}\)
4:Step 2:Calculate the award function
5:If (\(E_{u\sim c}<E_{\text{deadline}}\) & \(T_{u}<T_{\text{deadline}}\))
6:then,\(f(s,\,O_{u})=R_{\text{out}}\)
7:else
8:\(f(s,\,O_{u})=0\)
9:endif
10:If (\(E_{\text{autv-z}}<E_{\text{deadline}}\) & \(T_{\text{autv-z}}<T_{\text{deadline}}\))
11:then,\(f(s,\,O_{A})=R_{\text{outv}}\)
12:else
13:\(f(s,\,O_{A})=0\)
14:endif
15:If (\(T_{\text{G}MEC\cdot Z}<T_{\text{deadline}}\))
16:then,\(f(s,\,O_{A})=R_{\text{o}C\text{-}MEC}\)
17:else
18:\(f(s,\,O_{A})=0\)
19:endif
20:Step 3: Normalized utility function
21:\(E_{0}=[E_{k,0},\,E_{\text{autv-z},0},\,E_{\text{G}MEC\cdot Z,0}]\), \(\text{T}_{0}=\{T_{u,0},\,T_{\text{autv-z},0},\,T_{\text{G}MEC\cdot Z,0}\}\)
22:Step 4: Total utility function
23:\(q(s,a)=\varphi_{1}f(s,a)-(1-\varphi_{1})\left[\varphi_{2}\ T_{0}(s,a)+(1- \varphi_{2})\ E_{0}(s,a)\right]\)
```  
**Algorithm 1** Reward function algorithm
# Ear Centering for Accurate Synthesis of Near-Field Head-Related Transfer Functions +
## 5 Conclusions  
We proposed new spherical-wave translation operators for ear centering to be used in the synthesis of head-related transfer functions for sound sources located close to the head. The new operators translate the reference position and maintain fixed ear positions; they are therefore consistent with the definition of HRTFs. Our formulations rely on complex-valued spherical basis functions, which allows for more precise evaluations.  
We contrasted the performance of our proposal and the existing plane-wave translation operators. Synthesis accuracy increased consistently with our proposal. Enhancements were observed across distinct spherical resolutions and up to frequencies above the range of operation determined by the input spherical grid.  
We also addressed the open question of whether direct PW and SW translation operators are interchangeable when encoding HRTF datasets in the far field. We found that an accurate synthesis requires using the same type of translation operator in both the direct and inverse stages of ear centering.  
Figure 7: Comparison of performance with synthesis error (20) in dB (\(P=252\), \(q=5\), \(N_{\text{g}}=14\)). (a) synthesis with NEC. Panel; (b) synthesis with direct and inverse SEC; (c) synthesis with direct PEC and inverse SEC.  
Extensions to this work might consider validations using bottom-up auditory models to simulate localization tasks. Subjective tests could also provide more insight into the validity of the suggested approach.  
All authors contributed equally to the conceptualization, methodology, validation, analysis, and writing of this research. All authors have read and agreed to the published version of the manuscript.  
This research was partially supported by JSPS KAKENHI Grant Nos. 19H04145 and 22H00523.  
Not applicable.  
The data used as input to generate the results reported in this manuscript can be found at [[https://cesardsalvador.github.io/download.html](https://cesardsalvador.github.io/download.html)]([https://cesardsalvador.github.io/download.html](https://cesardsalvador.github.io/download.html)). The last day of access was 4 August 2022.  
The authors wish to thank the SI audio algorithm team for fruitful discussions. The authors also wish to thank [PERSON] for his insights and constructive debates.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 3 System Design Requirements and Challenges
### Recognizing the Elevator Hall Occupancy  
Throughout the day, the numbers of people in different elevator waiting areas within a building vary, representing the future flow of people that each elevator needs to transport. Therefore, accurately capturing the pedestrian flow in each waiting area is crucial for elevator scheduling and to help users choose which elevator to wait for. Moreover, calculating the number of people in the waiting areas and presenting these data to prospective elevator users allows them to assess whether to proceed to wait for that elevator. However, in this problem, we face the challenge of determining which individuals are actual elevator users waiting for their rides, as the waiting areas may also be traversed by individuals who do not intend to use the elevator. Including all individuals in the area would result in a discrepancy between the number of people detected through image recognition and the actual number of elevator users, causing potential user misjudgment and hindering the effectiveness of assisting users to choose less crowded elevators.
# Improved Patch Packing and Refining Segmentation for the V-PCC Standard
## 5 Conclusions  
This article aims to address some limitations in V-PCC, such as severe time consumption and excessive occupancy map size. In response to the significant issue of time consumption, we have analyzed and enhanced the refining segmentation process, which contributes significantly to the overall time consumption. We propose the VS-RS algorithm, which bypasses some voxels that do not require refining segmentation. Only voxels with non-uniform distribution of projection planes require refining segmentation. Regarding the issue of excessive size in the occupancy map generated by the original patch packing algorithm and the problem of the VS-RS potentially generating additional fine patches. This paper also proposes the DAPP to perform patch packing more reasonably. It contains a novel patch sorting method, placement area selection method, and patch positioning method. Patches can be placed in a compact manner. The novel solutions still generate compliant V-PCC streams and can be decoded correctly by any V-PCC decoder. The experimental results show that our method achieves an average BD Rate gain of \(-1.39\%\) and \(-1.58\%\), respectively, compared to the V-PCC benchmark, according to the D1 and D2 distortion standards. Simultaneously, we achieved a \(31.86\%\) reduction in encoding time and a \(20.14\%\) decrease in the size of the occupancy map. This method also improves the quality of point cloud reconstruction and preserves more details. In addition, when compared with the data from recently published papers, our method has made significant improvements in various evaluation indicators. There are several intriguing exploration topics for the future, including (1) enhancing image-filling algorithms, (2) maximizing the utilization of occupied image data for video compression, and (3) integrating deep learning techniques into V-PCC. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Test Point Cloud**} & \multicolumn{4}{c}{**D1 (\%)**} & \multicolumn{4}{c}{**D2 (\%)**} & \multicolumn{4}{c}{**Time Reduction**} \\ \cline{2-11}  & [26] & [25] & [35] & **Ours** & [26] & [25] & [35] & **Ours** & **Rate (\%) (Ours)** \\ \hline \multirow{2}{*}{
\begin{tabular}{c} Loot \\ Redandblack \\ Soldier \\ Longdress \\ Dancer \\ \end{tabular} } & 0.05 & 0.01 & 0.03 & \(-0.39\) & 0.01 & 0.03 & 0.03 & \(-0.54\) & 22.30 \\  & \(-0.01\) & \(-0.06\) & 0.03 & \(-2.55\) & \(-0.08\) & \(-0.05\) & 0.03 & \(-2.99\) & 26.97 \\  & \(-0.04\) & 0.08 & \(-0.06\) & \(-2.86\) & 0.04 & 0.04 & \(-0.05\) & \(-2.26\) & 31.95 \\  & \(-0.13\) & 0.07 & 0.04 & \(-2.48\) & \(-0.09\) & \(-0.13\) & 0.04 & \(-3.07\) & 39.62 \\  & \(-0.02\) & \(-0.11\) & 0.05 & 0.06 & 0.02 & \(-0.01\) & 0.05 & \(-0.10\) & 43.34 \\  & \(-0.04\) & 0.00 & 0.06 & \(-0.10\) & \(-0.01\) & 0.24 & 0.07 & \(-0.49\) & 26.95 \\ \hline \multicolumn{1}{c}{average} & \(-0.03\) & 0.00 & 0.03 & \(-1.39\) & \(-0.02\) & 0.02 & 0.03 & \(-1.58\) & 31.86 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Comparison of Geom.BD-rate coding performance with related works.