esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Works
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Ref.** & **Year** & **Data Source** & **Language** & **Prediction** & **ML Approaches** & **Accuracy (\%)** & **F1-Score (\%)** \\ \hline
[3] & 2019 & Questionnaire (D1) & English & 5 Levels of Depression & [RFT, XGBoost, LR, SVM] & D1: [76.34, 83.87, 59.22, 76.50] & - \\  & & & & & & 59.22, 76.50] & - \\  & & & & & & 72. [82.05, 84.02, 86.45, 85.44] & & \\ \hline
[4] & 2020 & Twitter & Bengali & Depression & [DT, RF, SVM, LR, NB, KNN] & [90.0, 90.3, 90.1, 90.3, 90.2] & [90.1, 90.3, 90.3, 90.2] & [90.3, 90.3, 90.2] \\ \hline
[5] & 2020 & Twitter & English & Depression & [SVM, LR, RF, GBDT, XGBoost] & D1: [91.2, 92.7, 94.4, 96.0, 96.4] & D1: [89.9, 91.6, 93.5, 96.1, 95.8] & D2: [80.07, 78.4, 77.9, 91.1, 86.4] & D2: [80.07, 78.4, 77.9, 81.1, 88.7] \\ \hline
[6] & 2021 & Twitter & English & Depression & [RF, SVM] & [77.0, 73.0] & - \\ \hline
[8] & 2022 & Twitter & Arabic & Depression & [SVM, RF, LR, KNN, AdaBoost, NB] & RF: [82.39] & RF: [82.53] \\ \hline
[9] & 2020 & Twitter & English & Depression & SVM [H, M, L] & [86; 91; 86] & [84; 85; 85] \\ \hline
[11] & 2019 & Twitter & Bangla & Depression & GRU & 75.7 & - \\ \hline
[15] & 2019 & Twitter + Facebook & English & 6 Level of Depression & SVM-NB & 74 & - \\ \hline
[16] & 2019 & Twitter + Patient & \multirow{2}{*}{Arabic} & \multirow{2}{*}{Depression} & [RF, NB, AdaBoostM1, Liblinear] & [83; 75.6, 55.2, 87.5] & [82.8, 75.6, 53.2, 87.5] \\  & & & (PHQ-9) & & & & \\ \hline
[17] & 2019 & Twitter & English & Depression & [MNB, SVR] & [78; 79.7] & - \\ \hline
[18] & 2021 & Twitter & English & Depression & Multi Model + TF-IDF feature: [LR, LDA, GNB] & [90.3, 90.4, 87.9] & [90.2, 90.3, 87.8] \\ \hline
[19] & 2020 & Twitter & English & Depression & RF & 84.7 & 66.7 \\ \hline
[20] & 2021 & Twitter & English & Depression & [NB, RF] & - & [94.87, 99.89] \\ \hline
[21] & 2022 & Twitter & English & Depression & GBC & 91 & 89 \\ \hline
[22] & 2020 & Twitter & English & Depression & [LSTM, CNN] & [93; 95] & - \\ \hline
[23] & 2023 & Twitter & English & Depression & [SVM, RF] & [59; 57] & [54; 53] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of recent studies using traditional machine learning approaches to detect mental disorders from different data sources.
## 3 Example Forgetting in Federated Learning
### Example Forgetting of FedAvg  
To verify the issue exists in federated learning, example forgetting events in federated learning and centralized learning are compared. We measure example forgetting events in centralized learning as follows. We randomly batch the training data. During batch training, a batch of data A, is fed forward to check if a model can correctly predict the target. The margin error between the target and prediction is calculated and saved for future reference. After the model is updated by another batch B, we once again feed forward batch A. If the model predicted correctly for batch A previously but incorrectly after the batch B update, or if the new margin error is larger than the previous, a forgetting event has occurred.  
Since personalized approaches have more than one model to track, FedAvg is chosen. However, a global model is never trained but overwritten in FedAvg. Therefore, we define example forgetting anew. An example forgetting event in federated learning is that a global model losing knowledge of a data sample which was previously learned to a local model before aggregation. Therefore, finding a forgetting event is different from centralized model training. We evaluate the local training dataset on the local model before communication then evaluate the same dataset on the global model. If an example is successfully classified by a local model but misclassified by a global model, an example forgetting event has occurred. When a global model is aggregated, the knowledge learned in each local model is aggregated as well, resulting in an inevitable information loss. Hence, the newly distributed local models are more likely to forget trained examples. We compared forgetting events in federated learning to centralized learning. We recorded the number of forgetting events for MNIST [20], FMNIST [21], SVHN [22], and CIFAR-10 [23] with DenseNet-121 model [24] as shown in Table 1. In federated learning, compared to centralized learning, forgetting events increased by 1989% in MNIST, 472% in FMNIST, 166% in SVHN, and 271% in CIFAR-10. In particular, there was a huge difference in MNIST. The reason is that in the case of the MNIST dataset, which is relatively easy to classify compared to other datasets, example forgetting rarely occurs during centralized learning. In the case of federated learning, the redistributed global model shows drastic forgetting events. The phenomenon seems proportional to the classification difficulty of the dataset. For fair comparison, all hyperparameters are fixed, but 200 epochs were given in centralized learning, and 40 rounds of 5 epochs are given to each of the 5 clients in federated learning, so that the total epochs for each client are set to be the same as the epochs of centralized learning.
# Enhancements and Challenges in CoAP--A Survey
## 4 Enhancements in CoAP
### Congestion Control Mechanisms in CoAP
#### 4.1.4 Four-State Estimator Scheme  
Since CoCoA is unable to distinguish which retransmission the received ACK belongs to, it performs the RTO calculations based on the original start time which causes idle delays between successive transmissions. To overcome this issue, the authors in [23], have proposed a 4-state estimator scheme to increase the granularity of backoff. The scheme works on the principle of reacting less on the losses occurred in the beginning when packet is transmitted and react more as more losses are observed. Each transmission is termed as a state where each transmission corresponds to state 1, 2, 3, or 4. Each state number corresponds to number of retransmissions. When a new transaction is carried out, the transaction state is considered to be 1. This state is increased each time by one as retransmission is carried out. Similarly, whenever a packet is transmitted successfully without any retransmissions, the state is decreased by one. This way the backoff values are set accordingly. Optimization of backoff values can be performed by state values within the transaction as well as across multiple transaction.  
A variable backoff factor is calculated for each value of state. For the backoff values and the formulas used to calculate these values; reader is referred to [23]. Unlike CoCoA's two estimators (weak and strong), the proposed scheme has four levels of estimators. As the losses are increased and more transmissions are failed, higher percentage of backoff is added to the RTO calculation. The paper employs the following equation (Equation (2)) for the calculation of overall RTO:  
\[RTO_{overall}=w*RTO_{obtained}+(1-w)*RTO_{overall} \tag{2}\]  
The parameter \(w\) here is the weight to the obtained RTO.
# A Review of Video Object Detection: Datasets, Metrics and Methods
## 3 Video Object Detection Methods
### Flow-Based
As a result, feature propagation to obtain the features of the non-key frames improved the inference computation speed. The feature aggregation method on the key frames used a small fully convolutional network to obtain the weight maps on each localization, which was different from the method in [22]. The Impression Network achieved 75.5% mAP accuracy at 20 fps on the ImageNet VID dataset. Figure 3: DFF (deep feature flow) framework [28]. Besides Impression Network, in [69], another combination method (THP) was introduced. Noting that all of the above methods utilized fixed interval key frames, this method introduced a temporally adaptive key frame scheduling to further improve the trade-off between speed and accuracy. Fixed interval key frames pose a difficulty to control the quality of key frames. With temporally adaptive key frame scheduling, the fixed interval key frames were adjusted in a dynamic manner according to the proportion of points with poor optical flow quality. If it was greater than a prescribed threshold T, it would indicate that a current frame had changed too much compared with the previous key frame. The current frame was then chosen as the new key frame and the feature maps were obtained from it. According to the results reported in [69], the mAP accuracy was 78.6% with a runtime of 13.0 and 8.6 fps on the GPUs NVIDIA Titan X and K40 (NVIDIA, California, USA) respectively. With a different T, the mAP slightly decreased to 77.8% at faster speeds (22.9 and 15.2 fps on Titan X and K40, respectively). Compared with the winning entry [114] of the ImageNet VID challenge 2017, which was based on feature propagation [28] and aggregation [22], an mAP of 76.8% at 15.4 fps was achieved on Titan X, and a better performance in terms of both the detection accuracy and speed was obtained in [69]. Similarly, THPM [27] provided a light weight network architecture for video object detection. A light image object detector is utilized on key frames. The state-of-the-art lightweight Mobilenet [115] is utilized as the backbone network. Feature maps from key frames are propagated to non-key frame for detection by a light flow network. A flow-guided gated recurrent unit (GRU) module is provided to aggregate features effectively between key frames. On the ImageNet VID dataset, THPM achieves 60.2% mAP at speed of 25.6 fps on mobiles (e.g., HuaWei Mate 8 produced by HUAWEI TECHNOGIES CO., LTD, China).
## 2 Beam-to-Column Connections  
Before describing the proposed solutions, the principles of the calculation are briefly summarized. First of all, the design-bending moment \(M_{d}\) has to be known. In this case, the feasibility study is conducted assuming \(M_{d}=110\) kNm. If we assume an overstrength factor of 1.5, then the positive moment strength of the beam that belongs to the sub-assemblage results in \(M_{Rd}=165\) kNm.  
For every \(i\)-th solution, the design sliding force is calculated for the corresponding internal lever arm \(z_{i}\):  
\[F_{d,i}=\frac{M_{d}}{z_{i}}, \tag{1}\]According to Eurocode 3 [23], the slip resistance of the connection, \(F_{s,Rd,i}\), can be calculated as:  
\[F_{s,Rd,i}=0.7\,f_{ub,i}\cdot A_{res,i}\cdot n_{b,i}\cdot n_{s}\cdot\mu, \tag{2}\]  
where \(A_{res,i}\), \(n_{bi}\), and \(f_{ub,i}\) are the transversal area and number of bolts, and their ultimate strength, respectively, while \(n_{s}\) and \(\mu\) are the first, the number of planes where friction develops and the second, the slip factor. It is noteworthy to remark that this feasibility study is conducted by adopting metal-to-metal contact as first approach. However, it is known that the international code ASCE07-22 [24] does not allow this type of contact in friction dampers; conversely, the introduction of friction pads is prescribed. In this regard, the authors are currently developing further insights for analyzing best-performing friction materials. Some preliminary results can be found in [PERSON] et al. [25].  
This study assumes \(n_{s}=2\) and \(\mu=0.4\). Moreover, it has to be noted that Equation (3) is the function of the preloading force of each bolt, \(F_{pc,i}=0.7\,f_{ub,i}\cdot A_{res,i}\).  
With the aim of limiting the preloading force in the range 30-60% of the code-compliant value, a coefficient \(\mathrm{t_{s,i}}\) can be introduced for setting the stress level of each preloaded bolt [26]. If \(t_{s,i}\) is assumed as the ratio between the sliding force reported in Equation (1) and the code-compliant slip resistance of Equation (2), then the effective preload design value is:  
\[F_{pc,d,i}=t_{s,i}F_{pc,i}, \tag{3}\]  
Based on these calculation criteria, the following sections present three different structural solutions for BCCs characterized by different mechanical performances in terms of stability of the cyclic behavior and efficacy of their dissipation capacity. They are named BCC-1, BCC-2, and BCC-3, respectively. Table 1 reports the values of the design sliding force, the stress-level coefficient, and the effective preload for the proposed solutions.
###### Abstract  
The aim of this study was to evaluate the implant survival rate, marginal bone loss, and surgical and prosthetic complications of implants placed through sinus floor elevation and tilted implants engaged in basal bone to bypass the maxillary sinus. Sixty patients were enrolled for this study. According to the residual bone height of the posterior maxilla, the sample was divided into three groups of 20 patients: Group A (lateral sinus floor elevation), Group B (transcrestal sinus floor elevation), and Group C (tilted implants employed to bypass the sinus floor). Follow-up visits were performed one week after surgery, at three and six months, and then once a year for the next 4 years. The outcomes were the implant survival rate, marginal bone loss, and surgical and prosthetic complications. Although Groups A, B, and C demonstrated implant survival rates of 83.3%, 86.7%, and 98.3%, respectively, the statistical analysis showed no statistically significant difference between groups. Statistically significant differences between groups were also not found concerning marginal bone loss, as recorded by intra-oral X-ray measurements during follow-up examinations. Regarding complications, it was not possible to perform a statistical analysis. To reduce possible surgical risks, implant placement in basal bone could be preferred.  
p 2022 20  
Although tilted implants might be the least risky choice [9], the following prerequisites should always be provided: adequate bone volume in the retrocanine area for implant placement at least 10 mm in length and combination with an axial implant [19; 20].  
In addition, implant placement in basal bone should always be considered in the presence of any conditions that could represent a possible contraindication to sinus augmentation, such as sinusitis, including allergic rhinitis, polyp, cyst, or tumor in the maxillary sinus, and a history of sinus surgery [21; 22].  
The aim of this retrospective clinical study was to evaluate and compare the implant survival rate (first outcome), marginal bone loss (second outcome), and surgical and prosthetic complications of implant prosthetic rehabilitation through implants placed via sinus floor elevation techniques (lateral approach and osteotome-mediated technique) and tilted implants engaged in basal bone to bypass the maxillary sinus.
# Enhancement of Ultrasonic Guided Wave Signals Using a Split-Spectrum Processing Method
## 4 Experimental Validation
### SNR Calculation  
In order to quantify the improvements shown by the proposed technique, the SNR enhancement was calculated as:  
\[\text{SNR}=20\times\text{ log}_{10}\left(\frac{\text{S}}{\text{N}}\right) \tag{6}\]  
where S is the maximum amplitude of the defect's reflection, and N is the root mean square (RMS) value of the noise region around the defect. The SNR of the unprocessed signal was 7.8 dB when the defect size was 2% CSA, and 13.25 dB when the defect size was 3% CSA. The results presented in Table 2 show that the SSP algorithms enhanced the SNR by 34.9 dB and 42.9 dB for these cases respectively. Furthermore, a comparison of the amplitude of the pipe-end reflection and the defect reflection was undertaken in order to evaluate how well the SSP maintains the amplitude of the signal of interest. The results indicate that when the size of the defect was 3% CSA or greater, there were no significant amplitude changes that occurred for the proposed method.  
Figure 9 illustrates the result of the unprocessed signal (blue trace) and the SSP algorithm (red trace) for the above experiment when there was no defect (baseline), up to when the defect size was 8% CSA. The results clearly illustrated that the performance of the proposed technique massively improved the SNR of the GWT response compared to the unprocessed data, achieving around 30 dB  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Signal** & **2\% Defect** & **3\% Defect** \\ \hline SSP & 34.9 dB & 42.9 dB \\ Unprocessed & 7.8 dB & 13.3 dB \\ \hline \hline \end{tabular}
\end{table}
Table 2: Signal-to-noise ratio (SNR) enhancement of experimental signal.  
Figure 8: Zoom in around the defect area from 0.5% CSA up to 8% CSA.  
improvement for 44 kHz. However, as mentioned earlier, the comparison commenced when the defect size was at least 2% CSA. Hence, in order to clarify it, a dotted line was added at 2% CSA.
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Cases**} & **CY\({}^{*}\)[28]** & **Yankee Rowe [29]** & **Zion [30]** & **Kori-1 [13]** & **IAEA [31]** & **Wolsong (p.w.)** \\ \hline \multirow{2}{*}{**Methods**} & **RESRAD v6.2** & **RESRAD v6.2** & **RESRAD v7.0** & **RESRAD v7.2** & **IAEA** & **RESRAD v7.2** \\  & & & & & & **Standard** & \\ \hline \({}^{108\text{m}}\)Ag & 2.60 \(\times\) 10\({}^{-1}\) & 2.70 \(\times\) 10\({}^{-1}\) & - & - & - & 2.56 \(\times\) 10\({}^{-1}\) \\ \({}^{14}\)C & 2.10 \(\times\) 10\({}^{-1}\) & 2.04 \(\times\) 10\({}^{-1}\) & - & 8.24 \(\times\) 10\({}^{0}\) & 2.50 \(\times\) 10\({}^{1}\) & 2.07 \(\times\) 10\({}^{0}\) \\ \({}^{60}\)Co & 1.40 \(\times\) 10\({}^{-1}\) & 1.48 \(\times\) 10\({}^{-1}\) & 1.74 \(\times\) 10\({}^{-1}\) & 2.10 \(\times\) 10\({}^{-1}\) & 2.50 \(\times\) 10\({}^{0}\) & 1.73 \(\times\) 10\({}^{-1}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of DCGL results (Bq\(\cdot\)g\({}^{-1}\)) for various radionuclides derived with a regulatory dose limit of 0.25 mSv-year\({}^{-1}\). The comparisons indicated relatively similar results for most of the radionuclides. In particular, the results of major radionuclides, such as \({}^{14}\)C, \({}^{60}\)Co, \({}^{134}\)Cs, \({}^{137}\)Cs, \({}^{63}\)Ni, and \({}^{90}\)Sr, were relatively consistent with the values derived from Kori-1 [13]. Furthermore, there were no data in the other decommissioning cases for comparing the DCGL values for \({}^{93m}\)Nb and \({}^{93}\)Zr. However, the radionuclide \({}^{93m}\)Nb showed a significantly higher DCGL value compared to the IAEA standard. As explained above, this is considered to be the effect of the considerably high Kd value of \({}^{93m}\)Nb, hindering the spread of the contamination, together with a relatively short half-life. However, although the Kd values of \({}^{93m}\)Nb and \({}^{93}\)Zr are similar to each other, the DCGL of \({}^{93}\)Zr obtained in the present work indicated a relatively decreased tendency in comparison to the IAEA standard. It might be caused by a considerably longer half-life of \({}^{93}\)Zr (t\({}_{1/2}\) = 1.53 \(\times\) 10\({}^{6}\) years) compared with that of \({}^{93m}\)Nb (t\({}_{1/2}\) = 16 years). A longer half-life means a larger number of radionuclides required to provide the same radioactivity. Due to the limited retention capacity of contaminated, unsaturated, and saturated zones, the DCGL of \({}^{93}\)Zr cannot be as high as \({}^{93m}\)Nb. Nevertheless, it is expected that a more reliable DCGL value of various radionuclides could be derived by using the Kd value directly obtained from the practical adsorption test for Wolsong NPP site soil in the future.
## 2 Dynamical Models
### Bicircular Restricted Four-Body Problem  
The bicircular restricted four-body problem extends the Earth-Moon circular restricted three-body problem by incorporating the Sun's gravitational perturbation. In the framework of the BR4 BP model, the Sun is assumed to revolve in a circular orbit of radius \(L\gg 1\) around the Earth-Moon center of mass in the same plane as the Moon does. The direction to the Sun in the CR3 BP rotating reference frame is determined by \(\theta_{S}\)--the phase of the Sun (Figure 2). The equations of spacecraft motion in the BR4 BP model have the same nondimensional form as in the CR3 BP if we modify the effective potential by adding  
\[\Omega_{S}(x,y,z,t)=\frac{Gm_{S}}{r_{3}(t)}-\frac{Gm_{S}}{L^{2}}(x\cos\theta_ {S}(t)+y\sin\theta_{S}(t)), \tag{5}\]  
where \(Gm_{S}=3.289005596145305\times 10^{5}\) is the dimensionless Sun's gravitational parameter,  
\[r_{3}(t)=\sqrt{(x-L\cos\theta_{S}(t))^{2}+(y-L\sin\theta_{S}(t))^{2}+z^{2}} \tag{6}\]  
is the distance from the spacecraft to the Sun. The phase of the Sun grows linearly over time: \(\theta_{S}(t)=\theta_{0}+\omega_{S}(t-t_{0})\). For \(L=389.17\), which approximately corresponds to the ratio of the Sun-Earth and Earth-Moon distances, the rotation rate of the Sun is determined as follows:\[\omega_{S}=\sqrt{\frac{1+m_{S}}{L^{3}}}-1\approx-0.9253. \tag{7}\]  
By analogy with the CR3 BP, the planar case is derived by setting \(z\equiv 0\).
## 5 Conclusions  
In our research, we built the \(RSEI\) evaluation model, evaluated the EEQ of the study area across four temporal benchmarks (1995, 2000, 2010, and 2019), and analyzed the spatial-temporal dynamics of the EEQ from 1995 to 2019. Based on the selected indicators (_NDVI_, _WET_, _NDBSI_, and _LST_), the evaluation results of the study area can almost reflect the actual conditions of the eco-environment. The key findings of this study can be summarized as follows:  
1. The EEQ of the study area first declined and then recovered from 1995 to 2019. Most areas were of the moderate and good grades, and the overall level of the EEQ was good. _NDVI_ and _WET_ had a positive relation with the EEQ: the contrary was observed for _LST_ and _NDBSI_.
2. The spatial distribution of EEQ was clustered rather than random, with a strong positive correlation. The spatial distribution of kernel density of the EEQ for each grade showed an obvious multi-core and multi-level pattern. In particular, the hot spots of kernel density of the moderate-grade areas were located in the towns within the Tingjiang River Basin, and those of the good-grade areas were distributed in the surroundings of the study area during the four periods (1995, 2000, 2010, and 2019).
3. The distribution of the EEQ areas for each grade was almost in a southwest-northeast direction. Compared with the distributions of other grade areas, the central trend, directional trend, and discrete level of the moderate- and good-grade areas were almost consistent. These results can help managers to better understand the eco-status of the study area and to produce more comprehensive eco-environment protection and restoration policies.  
Methodology, J.C.; software, J.C.; validation, J.C., G.L. and Z.C.; data curation, J.C.; writing--original draft preparation, J.C.; writing--review and editing, G.L. and Z.C.; visualization, J.C.; supervision, G.L.; funding acquisition, Z.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (No. 42277013) and the Program for Cultivating Innovative Team, Fujian Normal University, China (Y0720409B06).  
Not applicable.  
The original contributions presented in this study are included in the article; further inquiries can be directed to the corresponding author.