esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Video Object Detection: Datasets, Metrics and Methods
## 3 Video Object Detection Methods
### Other Methods
Furthermore, it was found that down-sampling images can obtain better accuracy sometimes. From this point of view, a framework named AdaScale was proposed to adaptively select the input image size. AdaScale predicts the best scale or size of a next frame according to the information of a current frame. One of the reasons for the improvement is that the number of false positives is reduced. The other reason for this is that the number of true positives is increased by resizing the too-large objects to a suitable size for the detector. In [90], the optimal scale (pixels of the shortest side) of a given image is defined with a predefined finite set of scales S (S = {600, 480, 360, 240} in [90]). Furthermore, a loss function consisting of the classification and regression loss is employed as the evaluation metric to compare the results across different scales. The regression loss for the background is expected to be zero. Hence, if the loss function is utilized directly to evaluate the results across different scales, the image scale which contains fewer foreground bounding boxes is supported. In order to deal with this issue, a new metric (the loss function, which focuses on the same number of foreground bounding boxes chosen on different scales) is employed to compare across different scales. More specifically, the number of bounding boxes involved to compute the loss is determined by the minimum number (\(m\)) on all the scales. For each scale, the loss of the predicted foreground bounding boxes on the image is sorted in ascending order and the first \(m\) bounding boxes are chosen. The scale \(m\) with the minimum loss is defined as the best scale. Inspired by R-FCN [19] working on deep features for bounding boxes regression, the channels of the deep features are expected to contain the size information. Therefore, a scale regressor using deep features is built to predict the optimal scale. Evaluated on the ImageNet VID and mini YouTube-BB datasets, Adascale achieved 1.3% and 2.7% mAP improvements with 1.6 and 1.8 times speedup compared with single-scale training and testing, respectively. Furthermore, combined with DFF [28], the speed was increased by 25% while maintaining mAP on the ImageNet VID dataset.
# Energy-Efficient Amplifiers Based on Quasi-Floating Gate Techniques
## 2 Quasi-Floating Gate Transistors
### Application of QFG Transistors to Energy-Efficient Amplifier Design
#### 2.2.4 Dynamic Cascode Biasing  
In class AB amplifiers, cascade transistors may restrict the \(V_{DS}\) voltage of the transistors that generate the large dynamic currents, making these transistors enter ohmic region and limiting slew rate. To avoid this effect, dynamic biasing of the cascade transistors is required. It is typically done as shown on the left side of Figure 4d. A floating battery \(V_{B}\) transfers a level-shifted version of the input signal in A to node B, so that the cascade gate voltage increases for large signals, increasing the \(V_{DS}\) of the input transistor. Again, an efficient implementation of the floating battery can be made by using a QFG cascade transistor, as shown on the right side of Figure 4d. As before, the implemented value of \(V_{B}\) is not fixed; it is the difference between the quiescent cascode bias voltage \(V_{CN}\) and the quiescent input voltage, so it is insensitive to PVT variations. As before, no extra quiescent power or supply voltage requirements are needed.
## 3 Discussion
### Future Work  
Two different domains were provided as examples in the paper. The platform described in this paper is currently available with its appropriate interfaces (expert, practitioner, and end-user) to design, implement, and to use the knowledge objects and their attributes. The examples have shown that it can be used to display object and attribute states in various domains and processes. For example, to perform a diagnosis, to allow stakeholders to monitor the evolution of patients and municipalities, and to make appropriate decisions. This paper demonstrated how to implement temporal CBR within FVSOOMM. This reduces the complexity and computing time of using Lagrange interpolation to compare two fuzzy vector functions from \(\theta(2.n^{2}+2n)\) to \(\theta n\). This is a very important contribution because it simplifies the implementation of object-oriented temporal case-based reasoning. Future work is still required to simplify and enhance the efficiency of the distance algorithm. Additionally, CBR interfaces still need to be developed to take into account the temporal case-based reasoning features that are not currently fully implemented in the current FVSOOMM platform to compare the evolution of cases. Future work can still be done for the improvement of the current platform. It does not yet provide appropriate interfaces to zoom in on the evolution of some relevant component object of clinical events during a specific interval of time. This is future work that would allow a practitioner (e.g., physician or mayor) to discover new events and knowledge from the present case. As an example, in the treatment of a patient, consideration of what could be appropriate to enhance decisions for this situation, such as what would have enhanced the patient's clinical state and what was useless, or worse, a mistake. This kind of feature will be very interesting for assessing what was wrong and what was beneficial in the treatment of such cases, to take advantage of, and to fully appreciate and profit from, previous clinical experiences. The implementation of FVSOOMM is currently being used for pulmonary embolism diagnosis in Burkina-Fasso; however, the success of this is not yet available. Similarly, in the territorial recomposition intercommunicity example application, the mayor can study the impact of past decisions on the municipality evolution. The CBR interface also remains to be designed and developed in future works. In the territorial application, the cases were modeled and made available on a spreadsheet with the appropriate fuzzy functions already computed. The knowledge was designed by [PERSON], who is an expert of the field. Fuzzy vectors were developed; however, the platform does not currently provide an end-user interface to municipalities and urban communities. Future work will provide that interface to make the knowledge available to mayors and stakeholders to help them make appropriate decisions that rely on the evolution of the municipalities that are inside, or outside, their neighbor's urban intercommunalities.
## 2 Preliminaries and Background
### Limited-Memory Warping LCSS Gesture Recognition Method
#### 2.4.4 Rejection Threshold (Training Phase)  
The computation of the rejection threshold, \(\omega_{c}\), requires computing the LM-WLCSS scores between the template and each gesture instance (expected chosen template) contained in the gesture class \(c\). Let \(\mu^{(c)}\) and \(\sigma^{(c)}\) denote the resulting mean and standard deviation of these scores. It follows  
\[\omega_{c}=\mu^{(c)}-h_{c}*\sigma^{(c)}, \tag{10}\]  
where \(h_{c}\) is a real positive in the range \(]0,\frac{\mu^{(c)}}{\sigma^{(c)}}[\).
## 3 Baseline Model Design Based on a DQN
### Reward Function
#### 3.2.1 Operational Safety  
In terms of safety, the spreader needs to avoid collisions with obstacles and successfully complete the lifting task. This paper sets up different safety rewards according to the actual operating conditions and increases the danger zone near the obstacles so that the spreader can avoid obstacles to reach the target point more quickly.  
If the spreader collides with an obstacle, the driver receives a significant penalty to strongly discourage dangerous operations. At each time step, if no collision occurs, a small positive reward is given to the driver, encouraging them to cover as much distance as possible without collisions. The specific design is as follows:  
\[r_{\text{safe}}=\left\{\begin{array}{l}+100,\text{When the spreader collides}\\ +0.01,\text{other}\end{array}\right. \tag{1}\]  
The danger zone is defined as the area where the spreader (whether unloaded or loaded) approaches an obstacle but has not yet collided with it. To help the spreader quickly move away from obstacles, the penalty around obstacles is intensified. However, to avoid trapping the spreader in local situations, the penalties in the danger zone should not be too concentrated and need to be adjusted according to the distance from the nearest obstacle. The reward function for the safety distance is designed as follows:  
\[r_{\text{distance}}=\left\{\begin{array}{l}+5,d_{4}\geq 2m\\ +10(d_{4}-0.5),0.5m\leq d_{4}\leq 2m\\ -20,d_{4}<0.5m\end{array}\right. \tag{2}\]  
where \(r_{distance}\) is the distance between the container and the obstacle at time t. The value of the safety distance is set based on the safety regulations of remote-controlled quay crane operations and the average operating values of skilled drivers.  
The overall operational safety reward function is given by  
\[R_{\text{safe}}=r_{\text{safe}}+r_{\text{distance}} \tag{3}\]
# Automated Data Acquisition in Construction with Remote Sensing Technologies
## 4 Integration of Various RS Technologies
### GPS-Based Systems Integration with Other Sensory Data  
The localization capability of GPS sensors could be integrated with the identification capability of technologies such as barcode and RFID for more accurate tracking of materials on site. Plus, integration of the GPS sensors with other aided sensors could provide more accurate and robust information about the site condition and the resources available in it.
## 2 Materials and Methods
### Implementation  
The design method for segmented linings was proposed to address the inability of monolithic linings to meet crack control requirements. It involves the following key design processes:  
(1) Site investigation and testing to obtain the mechanical properties of the surrounding rock, assessing its suitability for the construction of CAES workings.  
(2) The preliminary design of the working depth, diameter, and internal pressure based on the properties of the surrounding rock.  
(3) The calculation of the maximum crack width for the monolithic lining, followed by an assessment to determine if it meets the crack control requirements. If the requirements are met, the monolithic lining design proceeds. If not, the working depth, diameter, and internal pressure should be optimized.  
(4) After several optimization iterations, if the internal pressure is less than 8 MPa and the working diameter is less than 6 m but the crack control requirements are still not satisfied, a segmented lining should be adopted. The rationale for this is that both small internal pressure and a small diameter result in low economic benefits.  
(5) The design of the number of preset seams, determining the optimal number of seams.  
(6) Based on the optimal number of preset seams, we design the reinforcement ratio of the lining and retractable joints structure, ensuring that the maximum crack width of linings is smaller than the allowable values, thus completing the design of the segmented lining.  
The design process for the segmented lining is illustrated in Figure 6.  
Figure 6: Flowchart of the design process for the segmented lining.
###### Abstract
Meanwhile, fiber content is an important factor affecting the mechanical properties of reinforced soil, which affect the formation of the fiber-soil net [38]. [PERSON] and [PERSON] [39] concluded that the shear strength of BF-reinforced silty soil increased as the fiber contents increased. With a fiber content of 1.5% and a fiber length of 24 mm, the strength had the largest improvement. [PERSON] et al. [40] suggested that the best improvement effect of BF-reinforced clay was obtained at 0.25% fiber content and 12 mm fiber length. The clay had the \"poststrong\" characteristic after the incorporation of fiber. [PERSON] et al. [41] found that the reinforced soil had better mechanical properties with the inclusion of 10% fly ash and 0.6% BF after incorporating 5% cement. The UCS of reinforced soil first increased and then decreased with the increase in fiber contents. The BF could fill particles well to improve strength from SEM images. [PERSON] et al. [37] proposed that the UCS and peak deviator stress of cement-solidified kaolinite obtained the highest value at 0.2% and 0.4% BF content, respectively. A well-interconnected skeletal structure formed by the combination of fiber and cement was the main reason for the increase in strength derived from SEM. [PERSON] et al. [26] considered that the UCS of BF-reinforced geopolymer-stabilized clay increased first and then decreased with the increasing fiber contents and fiber lengths. The best fiber content was 0.4% and the most suitable fiber length was 12 mm. However, most of these studies have paid more attention to the mechanical properties of BF-reinforced cemented clay and neglected the other widely reserved subgrade soil-silty sand. Furthermore, research on fiber-strengthening mechanisms is limited. In summary, more attention should be given to the mechanical properties and the mechanism of BF-reinforced subgrade soil, especially in BF-reinforced cemented subgrade silty sand. Therefore, BF-reinforced cemented silty sand (BFCSM) was selected as the research object. The aim of the paper is to clarify the effect of BF on the mechanical properties of cemented silty sand and reveal the corresponding fiber-reinforced microstructural mechanisms. To better investigate the effects of fiber contents and curing time, a series of UCS tests and STS tests are conducted on BF-reinforced uncemented silty sand (BFUSM) and BFCSM, and a series of developed MNLR models are established to predict UCS and STS. Simultaneously, the corresponding microstructure images are obtained by SEM tests, so as to explore the reinforced mechanism of BF incorporation. Finally, the purpose of this study is to propose an innovative combination of BF with cement to enhance the strength of silty sand and provide a reliable reference for subgrade improvement engineering.
# Two-Layer Security Algorithms to Prevent Attacks on Data in Cyberspace
### Cryptography  
Cryptography is the art of hiding data by scrambling data into an unreadable state that is impossible to decipher [8]. Modern security systems are built on the principles of cryptography, which is used to protect broadcasted information [9]. It also builds trust for transmission in cyberspace. Cryptography is derived from two words; one is \"crypto\", which means \"hiding\", and the other is \"graphy\", which means \"writing\" [10]. Early civilizations employed different techniques to ensure the security of their vital records and information. Cryptography is also known as a cryptosystem [11]. Cryptography is divided into two categories; one is encryption, and the other is decryption [12].  
Whenever data are broadcast using cryptography in cyberspace, they are converted into such a format, which must be in its actual state to be understood [13]. This entire process is called cryptography. The original information is concealed using various methods or algorithms using encryption, and the original and encrypted data are unlrated [14]. Encryption is the process of converting the original data into an unreadable representation [15]. When non-readable data are to be converted into actual data, a set of mechanisms is implemented on this non-readable data, with the help of which the non-readable data are converted into actual data, which is called decryption [16].  
Whenever data are transferred between two points in a network, an attacker uses a man-in-the-middle attack to make every effort to gain access to the data, as shown in Figure 1. When an attacker obtains access to the data, the attacker can modify the original data and re-transmit the file across the network, which can disturb the performance of the entire network and allow misuse of the data [17], but when the data are secure, the attacker is not able to misuse them even after accessing them. The best way to completely secure data is to use a cryptographic algorithm [18]. When developing an effective algorithm for data security in cyberspace, techniques should be applied to mechanisms that make it difficult for an attacker to break data security [19].
# A Review of Mid-Infrared Supercontinuum Generation in Chalcogenide Glass Fibers
## 2 ChG Glasses and Fibers
### Overview of ChG Fibers
#### 2.2.1 Step-Index Fibers  
A typical fiber structure is step-index geometry, which comprises a core with a high refractive index surrounded by a low index cladding. The core and cladding glasses chosen for ChG fiber fabrication must have good fiber-forming performances and similar thermal properties. Bulk ChGs are usually prepared using high-purity elements by melt-quenching [46; 47], as well as microwave radiation [48; 49], or chemical vapor deposition techniques [50]. ChG fibers are usually made via the double crucible method [51; 52; 53], or pre-formed via rod-in-tube [54; 55; 56] or extrusion methods [57; 58; 59]. For years, extensive investigations on ChG glass purification and fiber drawing have been carried out by Russian researchers. The optical attenuations in fibers based on As-S, As-Se, and As-S-Se systems have been reduced to lower than 1 dB/m. In particular, As\({}_{2}\)S\({}_{3}\) multimode fibers have minimal losses: 0.012 dB/m at 3.0 \(\upmu\)m and 0.014 dB/m at 4.8 \(\upmu\)m [60]. In addition to low optical losses, ChG step-index fibers have advantages of high power handling and good flexibility and strength [35; 61].  
The zero material dispersion wavelengths (ZMDW) of ChG glasses are always located at long wavelengths. For example, the ZMDW of As\({}_{2}\)Se\({}_{3}\) glass is \(\sim\)8 \(\upmu\)m, and that of As\({}_{2}\)S\({}_{3}\) is 5 \(\upmu\)m [62]. Depending partly on material dispersion, the ZDWs of ChG step-index fibers also lie in long wavelengths, resulting in a largely normal and steep dispersion in the relatively shorter wavelengths, which reduces the effective optical nonlinearity [63] and distorts the ultrashort optical pulses propagating in fibers [64]. Therefore, fiber dispersion must be carefully engineered with different waveguide dispersion for specific applications, such as the case of wavelength conversion and SC generation [2; 65]. The dispersion properties can be calculated through Sellmeier equation [63]:  
\[n^{2}(\lambda)=1+\sum_{i}\frac{A_{i}\lambda^{2}}{\lambda^{2}-B_{i}^{2}}, \tag{1}\]  
where \(\lambda\) is the wavelength, and \(A_{i}\) and \(B_{i}\) are Sellmeier coefficients. To some extent, these nonlinear processes are also related to the nonlinear coefficient, \(\gamma\), of ChG fibers, which is defined by [63]  
\[\gamma=\frac{2\pi n_{2}}{\lambda A_{eff}}, \tag{2}\]  
where \(A_{eff}\) is the effective mode area. Some typical values are \(\gamma\) = 100 W\({}^{-1}\)km\({}^{-1}\) for As-S fibers with 50 \(\upmu\)m\({}^{2}\)\(A_{eff}\)[66], and \(\gamma\) = 2450 W\({}^{-1}\)km\({}^{-1}\) for As-Se fibers with 40 \(\upmu\)m\({}^{2}\)\(A_{eff}\)[19]. These are both much greater than that of standard SMF 28 fiber (\(\gamma\) = 1 W\({}^{-1}\)km\({}^{-1}\) with 90 \(\upmu\)m\({}^{2}\)) [67]. \(\gamma\) can be dramatically improved by further decreasing the fiber's core diameter [68; 69; 70].
## 2 Related Work
### Segmentation
Based on the examination, this study proposes four transfer learning methods for overcoming the problem of a lack of training samples. It then demonstrates their performance on three representative medical imaging tasks, cervical cancer classification, skin lesion classification, and pressure ulcer segmentation. This study also shows the effective transfer learning method according to the properties of the respective tasks. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Reference** & **Application/ Task** & **Approach** & **Data** & **Metric** & **TL\({}^{*}\)** \\ \hline [PERSON] et al., 2017 [12] & Cervical cancer **classification** & CaffeNet pre-trained with ImageNet, machine learning classifier, three pyramid features: FAL, PHG, PLBP & & \\ \hline [PERSON] et al., 2018 [13] & Cervical cancer **classification** & CNN features, LeNet model & 345 positive/345 negative images randomly selected by NCI & Acc & ✗ \\ \hline [PERSON] et al., 2019 [14] & Cervical cancer **classification** & Faster R-CNN, CNN pre-trained with the ImageNet dataset and data augmentation & & AUC, Sensitivity, Specificity & ✓ \\ \hline [PERSON] et al., 2020 [16] & Cervical **concept object** & YOLO algorithm, GoogleNet & Intel \& MobileCOT dataset, 174 positive/174 negative images by Acc, specificity, sensitivity & ✗ \\  & **detection and classification** & model, data augmentation & NCI & & \\ \hline [PERSON] et al., 2020 [18] & Cervical histopathology & Inception-V3, Xception, VGG-16, Resnet-50, and & & & \\  & & & & \\  & & & & \\  & & & & \\ \hline [PERSON] et al., 2021 [17] & Cervical cancer **classification** & CNN with patch weights learning and spatial regulator & 978 records by NCI & Acc, AUC, equal error rate, ✗ & \\ \hline [PERSON] et al., 2021 [19] & Cervical cancer **classification** & Inception V3, ResNet50, & & & \\  & & & & \\  & & & & \\ \hline [PERSON] et al., 2022 [20] & Cervical cancer **classification** & Taming transformer with pre-trained T2T-WT & Pap smear dataset consisting of a total of 963 LBC images with four classes, SIPAXMeD consisting of 409 images of isolated cells with 5 classes, Herlev consisting of 917 isolated single-cell images with seven classes & Acc, FI-score, H-man, sensitivity, specificity & \\ \hline [PERSON] et al., 2023 [23] & Cervical cancer **classification** & CNN, VGG16 & Melanoma histopathology images & Acc & ✓ \\ \hline [PERSON] et al., 2023 [25] & Cervical cancer **classification** & CerviFormer, cross-attention & Pap smear dataset–Spakxmed dataset and Herlev dataset & Acc, precision, recall, & ✗ \\  & & & & & \\ \hline [PERSON] and Shinde 2023 & Cervical cancer **classification** & Pre-trained feature extractor, resnet50, Google Net & Herlev dataset with 917 images & Acc & ✓ \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Overview of studies using deep learning techniques for medical image analysis. TL\({}^{*}\) denotes transfer learning.