esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results
### Experimental Series 2: Comparison with Other Models  
To further show the effectiveness of OCATC, we provide a comparison between OCATC and state-of-the-art methods, as shown in Table 6.  
From Table 6, we observed that OCATC gives highly competitive results compared with state-of-the-art methods over all datasets. OCATC shows higher performance for DatasetC when compared to the previous study in [37] on this dataset. Thus, OCATC has a better advantage in selecting the suitable classifier, FS method, and the number of features for all datasets, especially for DatasetC. Moreover, the selected number of features by OCATC for all datasets is lower than those in other methods, which helps to achieve better results and saves computation resources. Selecting the correct number of features by discarding redundant and irrelevant features improves the classifier's training and performance. Furthermore, SVM-based classifiers were selected by most of the previously conducted studies and by OCATC. This proves the ability of OCATC to automatically select the most suitable classifier for each dataset without human intervention. Overall, OCATC yields the best results compared to single classifiers and state-of-the-art approaches. During the analysis, we showed the importance of adopting PSO as an optimization  
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Study**} & \multicolumn{5}{c}{**Previous Study**} & \multicolumn{5}{c}{**OCATC**} \\ \cline{3-10}  & & **Classifier** & **FS Method** & **FS** & **Accuracy** & **Classifier** & **FS Method** & **FS** & **Accuracy** \\ \hline DatasetA & [37] & SVM & SVM-FRM & 4000 & 0.974 & LSVM & RFF & 876 & 0.975 \\ \hline \multirow{3}{*}{DatasetB} & [48] & NB, SVM, KNN & \multirow{3}{*}{Chi2} & \multirow{3}{*}{1000} & \multirow{3}{*}{0.933} & \multirow{3}{*}{LSVM} & \multirow{3}{*}{PCA} & \multirow{3}{*}{436} & \multirow{3}{*}{0.936} \\  & [49] & NB, SVM, KNN & & & & & & & \\  & [40] & SVM & ImpCHI & 900 & 0.905 & & & & \\ \hline DatasetC & [37] & SVM & Chi2 & 3000 & 0.875 & RF & IG & 36 & 0.972 \\ \hline \multirow{3}{*}{DatasetD} & [43] & LR & Chi2 & 1\% & 0.865 & \multirow{3}{*}{SVC} & \multirow{3}{*}{SVD} & \multirow{3}{*}{289} & \multirow{3}{*}{0.967} \\  & [36] & SVM & BPSO-KNN & 2967 & 0.931 & & & & \\ \cline{1-1}  & [42] & PNs & Chi2 & 1\% & 0.90 & & & \\ \hline DatasetE & [1] & DNN & Word embedding & 128 & 0.974 & SVC & Chi2 & 942 & 0.975 \\ \hline \hline \end{tabular}
\end{table}
Table 6: OCATC results compared to state-of-the-art methods.  
algorithm to solve the problem of TC and select the optimal configuration that maximizes the classification performance. Overall, the proposed method obtained better results in comparison with other similar methods, which proved the highest ability of the proposed method in resolving complicated text problems.
## 2 Results  
Construction and Characterization of an E. coli Triple Mutant Strain Deficient in Phosphofructokinase (Genes pfkA, pfkB) and Glucose 6-Phosphate Dehydrogenase (zwf)  
_E. coli_ K-12 W3110 (wild-type) strain LJ110 [44] was chosen as the parent strain. In order to block glycolysis at the step of F6P and also to prevent a glycolytic bypass through the HMP, we successively deleted the genes _zwf_, _pfkB_, and _pfkA_ encoding activities of glucose 6-phosphate dehydrogenase and both phosphofructokinases [45; 46], respectively, by Ared recombineering [47]. For details of strain construction see Section 4 and Table 1 for genotypes of strains. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Strain** & **Genotype** & **Origin/Reference** \\ \hline \multirow{2}{*}{DH5a} & _F\({}^{*}\)- 680d, lacZAM15, _Ala(LZYA-argF)_ U169, _mCA1, andA1_, & [49] \\  & _hsd8127_ (_r\({}^{*  
The growth phenotype of the _E. coli_ LJ110 wild type (wt) and the triple mutant GL3 (\(\Delta p\
ot\)&A::FRT \(\Delta p\
ot\)&B::FRT \(\Delta z\
ot\)wf::FRT), was then compared on various media (Luria Bertani medium, LB, minimal medium (MM) with different single carbon sources; Table 2). We chose sugars and sugar alcohols which enter glycolysis above or below F6P. Growth on minimal agar plates was examined over a course of 2 days at 37 \({}^{\circ}\)C. As can be seen from Table 2, the wt strain LJ110 grew well on all tested carbon sources whereas the triple mutant strain GL3 showed several aberrations. While growth on LB was slightly affected, no growth was detected on MM agar plates or in shake flasks with the sole carbon sources D-glucose, mannitol, or sorbitol (all PTS substrates), and neither with lactose, D-galactose, maltose, or D-xylose. Growth on D-fructose was slower and smaller colonies were formed on MM agar plates. Thus, the combination of the three mutations (_pfkA, pfkB, zwf_) led to less growth on sugars entering glycolysis at the level of or above from F6P (see Figure 1). Gluconeogenic substrates as glycerol, D- or L-lactate, and succinate also yielded slower growth; so did D-gluconate and L-arabinose. This lack of growth might be due to intracellular accumulation of F6P or result from regulatory defects. A comparison of growth in liquid MM (shake flasks) of LJ110 wt strain and GL3 on glucose and fructose is given in Figure 2. Whereas wt strain LJ110 grew on glucose (\(\mu\) 0.59 \(\pm\) 0.01 h\({}^{-1}\)) and fructose (\(\mu\) 0.55 \(\pm\) 0.04 h\({}^{-1}\)) with similar growth rates, GL3 grew on fructose as sole carbon source with a \(\mu\) of 0.26 \(\pm\) 0.01 h\({}^{-1}\) and no growth was observed in MM glucose over a range of 96 h. Although D-fructose, as a single C source, enabled GL3 to grow at a reduced growth rate, we decided to use D-fructose as a neutral C source in preculture media for growth experiments to avoid long lag phases in MM subcultures when using other C sources.
# A Security-Enhanced Query Result Verification Scheme for Outsourced Data in Location-Based Services
## 4 Proposed Scheme
### Data Outsourcing Process
#### 4.2.2 Design of Description File Verification Objects
Specifically, in the structure of the verification object \(VO_{w_{i}}\), the verification region is inserted \(l\times|c(ds)_{w_{i}}|\) times and the padding region is inserted \(n_{dummy}=l\times(S-|c(ds)_{w_{i}}|)\) times. Finally, the sum of the inserted times (i.e., the sum of numbers in all counters) for each verification object is \(l\times S\). An example of our verification object is shown in Figure 5. The improved construction process of the verification object is shown in Algorithm 1. Figure 4: The structure of the special verification object. Figure 3: The verification object with a padding region. ```
0: The ciphertext files sets \(C(G)\) and \(C(DS_{w\in W})\)
0: The verification object sets \(VO_{G}\) and \(VO_{W}\)
1:for each \(c(g_{i})\in C(G)\)do
2: Generate a standard Bloom filter with \(m\) bits;
3: Calculate \(prf_{k}(c(g_{i}))\);
4: Calculate \(\{h_{1}(prf_{k}(c(g_{i}))),\ldots,h_{l}(prf_{k}(c(g_{i})))\}\) by using the set \(H\) that composed of \(l\) hash functions;
5:for\(1\leq j\leq l\)do
6: Set the counter \(v_{og_{l}}[h_{j}(prf_{k}(c(g_{i})))]\) to be 1;
7:endfor
8:return\(VO_{g_{l}}\);
9:endfor
10:return The verification object set \(VO_{G}\);
11:for each \(c(ds)_{w_{l}}\in C(DS_{w\in W})\)do
12: Generate a counting Bloom filter with \(n\) counters;
13:for each \(c\in c(ds)_{w_{l}}\)do
14: Calculate \(prf_{k}(c)\);
15: Calculate \(\{h_{1}(prf_{k}(c)),\ldots,h_{l}(prf_{k}(c))\}\) using \(H\);
16:for\(1\leq j\leq l\)do
17: Add the counter \(v_{ov_{l_{i}}}[h_{j}(prf_{k}(c))]\) by 1 in the verification region;
18:endfor
19: Generate \(n_{dummy}=S-|c(ds)_{w_{l}}|\) dummy description files;
20:for each \(c\in c(dummy)_{w_{l}}\)do
21: Calculate \(prf_{k}(c)\);
22: Calculate \(\{h_{1}(prf_{k}(c)),\ldots,h_{l}(prf_{k}(c))\}\) using \(H\);
23:for\(1\leq j\leq l\)do
24: Add the counter \(v_{ov_{l_{i}}}[h_{j}(prf_{k}(c))]\) by 1 in the padding region;
25:endfor
26:endfor
27:endfor
28:return\(VO_{w_{l}}\);
29:endfor
30:return The verification object set \(VO_{W}\);
```  
**Algorithm 1** Improved construction process of verification objects.
## 3 System Implementation for Serialized Track and Trace
### Sequence-Checking Algorithm  
Regular RFID antennas are designed for recognizing several tags simultaneously. In other words, they are designed to read as many tags as possible within the recognition range. However, if the regular antennas are used as they are in the sequencing work, the tags cannot be read in an orderly manner, and sequencing errors cannot be determined accurately. In order to recognize the correct sequence of RFIDs in a row, we used an RFID sequence-checking algorithm which is proposed in [23]. The algorithm is based on the read frequency (i.e., the number of successful reads in a predefined cycle time). Figure 12 summarizes the sequence-checking algorithm for RFID. The basic concept of this algorithm is that as the rear axles loaded onto the dolly sequentially pass through the RFID sequence-checking device, the reading sequence is determined by considering the average of the RFID read times. While a dolly passes through the sequence-checking zone, the reader repeats read cycles, during each of which the reader can read the ID's multiple times (called read count). The read counts of each ID over the repeating read cycles generate a time-domain profile of read counts. Sometimes the rear axles placed in the back of the dolly are read before those placed in the front, but in this case, the recognition frequency (the number of successful reads) is very low. Therefore, the average read time values can clearly determine the sequence of RFID tags. The robustness of this algorithm was verified through experiments in [24].  
Figure 11: The real-time inventory monitoring application.
# Hybrid Satellite-Terrestrial Networks toward 6G: Key Technologies and Open Issues
## 6 Open Issues
### Path Selection and Routing  
Unified routing protocols are necessary to allow integrated routing across various network components for consistency and efficiency. Conventional IP protocols, however, cannot be easily implemented because they were created for static terrestrial networks. If the routing strategy is not adjusted in a timely manner and in accordance with the network's time-varying topology, routing oscillation may result in frequent connection disruptions. Additionally, due to the satellite's extensive coverage, the routing table in the integrated satellite-terrestrial network may be huge, which makes it more difficult to determine the best routing techniques. When choosing the routing paths for various types of traffic, it is necessary to take into consideration the various propagation delays and conditions of terrestrial and satellite links. Low-latency pathways are preferred in order to meet users' QoS requirements.
## 3 Classification
### Communication Technologies
#### 3.6.2 Cellular  
A few efforts [5; 25; 38] have been made to design V2P safety systems using cellular technology. All of the efforts use 3G or Long Term Evolution (LTE) for communication and smartphones as a VRU device. Cellular-based V2P systems typically have a longer communication range due to the use of central infrastructure. However, latency and scalability performance of cellular V2P systems need to be researched further in order to determine their suitability for V2P safety systems. Due to its widespread coverage and high market penetration, cellular system is a good candidate for V2P convenience applications.  
Cellular V2X (C-V2X) is currently under development as part of the proposed 5G architecture. Once fully developed, C-V2X promises to fulfill the requirements of various use cases of V2X communications including V2P [48]. However, we are not aware of any efforts that have used C-V2X for design or evaluation of a V2P system.
# Labeled Multi-Bernoulli Filter Joint Detection and Tracking of Radar Targets
## 3 The S-LMB-GOM Filter
### Update  
The predicted multi-target posterior \(\pi_{+}=\left\{\left(r_{+}^{\ell},p_{+}^{\ell}\right)\right\}_{\ell\in \mathbb{L}_{+}}\) is an LMB distribution. \(\pi_{+}\) has the form of Equation (3) and can be denoted as \(\left\{\left(W_{+}\left(\mathbf{I}_{+}\right),\Pi_{\mathbf{x}\in\mathbf{X}_{+} }\right.p_{+}^{\ell}\right)\right\}_{\mathbf{I}_{+}\subset\mathbb{L}_{+}}\). \(\mathbf{I}_{+}\) is the label set of \(\mathbf{X}_{+}\) and also denotes a possible multi-target hypothesis. \(W_{+}\left(\mathbf{I}_{+}\right)\) denotes the existence probability of the hypothesis. Under the separable likelihood assumption, we can obtain that \(g\left(\mathbf{Y}|\mathbf{X}_{+}\right)=f\left(\mathbf{Y}\right)\prod_{ \mathbf{x}\in\mathbf{X}_{+}}g_{\mathbf{Y}}\left(\mathbf{x}\right)\).  
From Reference [28], the multi-target posterior density \(\tilde{\pi}\) can be written as  
\[\pi=\left\{\left(p_{+}^{\ell},p_{+}^{\ell}\right)\right\}_{\ell\in\mathbb{L}_{+}} \tag{25}\]  
where  
\[\tilde{r}_{+}^{\ell}=\frac{r_{+}^{\ell}\eta_{\mathbf{Y}}\left(\ell\right)}{1-r _{+}^{\ell}+r_{+}^{\ell}\eta_{\mathbf{Y}}\left(\ell\right)}, \tag{26}\]  
\[\tilde{p}_{+}^{\ell}\left(\mathbf{x}\right)=\frac{g_{\mathbf{Y}}\left(\mathbf{x }\right)p_{+}^{\ell}\left(\mathbf{x}\right)}{\eta_{\mathbf{Y}}\left(\ell \right)}, \tag{27}\]  
\[\eta_{\mathbf{Y}}\left(\ell\right)=\left\langle g_{\mathbf{Y}}(\cdot,\ell),p_{ +}^{\ell}(\cdot,\ell)\right\rangle. \tag{28}\]  
Therefore, when the prior is an LMB distribution, the multi-target predicted density and posterior density are both LMB distributions, so the LMB distribution is the conjugate prior for the S-LMB-GOMfilter [27; 28]. Each hypothesized track of the S-LMB-GOM filter is predicted and updated independently so the filter is efficient.
## 4 Discussion
### General Discussion and Future Research Possibilities  
After discussing different Sentinel missions in two different scenarios, it is possible to conclude that mission Sentinel 2 is favorable for medium-to-long-term operations because of its revisit time and spatial resolution. The data of missions Sentinel 3 and 5p are favorable for intra-day and day-ahead operations, as well as for long-term operations such as power network planning and maintenance (Table 2).  
It is worth emphasizing that this research of Sentinel 5p data application in the power network operation, especially in planning and maintenance, is in its beginning stage and will continue. Aerosol contamination research can continue by including weather data in the analysis and researching synoptic situations and local meteorological conditions for periods when a high aerosol concentration was recorded and when a high number of faults was detected. Then, a risk model of the power system outage appearance can be developed. Also, a method for the fusion of in situ air-quality measurements and satellite data could be researched.  
The application of newly obtained correlations and modeling results will be made possible by their integration into contingency analysis calculation. It may be used in the power system maintenance, long-term planning operations, etc. When the suggested research is conducted, results will be discussed appropriately with scientific and industrial societies, as well as with the general audience. Also, similar research could be conducted in other industries, such as the health or food industry, traffic industry, etc.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Operation**Satellite Mission** & **Sentinel 2** & **Sentinel** & **Sentinel** & **Sentinel 5p** \\
**MSI** & **3 LST** & **3 FRP** & **AI/LH** \\ \hline Hourly & \(-\) & \(-\) & \(-\) & \(-\) \\ \hline Intra-day & \(-\) & \(+\) & \(+\) & \(+\) \\ \hline Day-ahead & \(-\) & \(+\) & \(+\) & \(+\) \\ \hline Weekly & \(+\) & \(-\) & \(-\) & \(-\) \\ \hline Monthly and seasonal & \(+\) & \(-\) & \(-\) & \(-\) \\ \hline Long term & \(+\) & \(+\) & \(+\) & \(+\) \\ \hline \end{tabular}
\end{table}
Table 2: The time duration of the power network operations and the possibilities of using Sentinel data (”\(+\)” signify possible use, “\(-\)” signify impossible use).
## 2 Segmentation Model
### Loss Function Construction  
In remote sensing images, buildings vary in shape, size, and appearance, making it challenging for a single loss function to capture multi-scale features effectively. Additionally, buildings in different environments and lighting conditions may be affected by noise, shadows, or occlusions, further complicating edge detection. To address these challenges, this study proposes a hybrid loss function that combines edge consistency loss with binary cross-entropy (BCE) loss. This approach enhances the model's ability to capture building contours of various sizes and shapes while improving the distinction between buildings and their surrounding environment, ultimately increasing segmentation accuracy.  
The specific definition of the loss function is given by the following equation:  
\[\mathrm{Loss_{total}}=\alpha\cdot\mathrm{Loss_{BCE}}~{}+\beta\cdot\mathrm{ Loss_{BCE}} \tag{9}\]  
Figure 4: Edge enhancement attention module.  
Here, \(\text{Loss}_{\text{EC}}\) represents the edge consistency loss, and \(\text{Loss}_{\text{BCE}}\) denotes the binary cross-entropy (BCE) loss. The parameters control \(\alpha\) and \(\beta\), which are the relative contribution of each loss component.
## 3 WPTS Performance Simulation with AMC  
### WPTS without AMC  
Tx and Rx coils were designed on the FR4 substrate with dimensions of 150 \(\times\) 150 \(\times\) 1.6 mm\({}^{3}\), as shown in Figure 5a, where the Tx and Rx coil coupling simulation setup without the AMC is given. The dielectric constant \(\varepsilon_{r}\) of the substrate is 4.4, and the loss tangent \(tan\delta\) is 0.02. The width (\(t\)) and spacing (\(s\)) between the patterns are 2 and 3 mm, respectively. The number of turns is 7. A 59 pF lumped capacitor was used for resonance of approximately 6.78 MHz. To compare the WPTS performance according to the presence of the proposed AMC shielding, only the ferrite was placed behind the Tx coil, as shown in Figure 5b. Its dimension is 528 \(\times\) 528 \(\times\) 1.91 mm\({}^{3}\), as is that used in the AMC design in the next subsection. The Tx coil is positioned 4.5 mm from the ferrite tile. Figure 5c shows that the magnetic field is uniformly distributed in both the target and non-target regions for the WPTS simulation when no backing was used for the Tx coil. When only the ferrite tile was placed, the magnetic field was slightly reduced and not completely eliminated. The PTE performance was verified by varying the distance (\(d\)) between the Tx and Rx coils for both cases by measuring the PTE, defined by \(\left|S_{21}\right|^{2}\), between the coils, as shown in Figure 5d. In Figure 5a, the PTEs of the WPTS at the distances of 10, 15, 20, and 30 cm are 64%, 17.64%, 4.84%, and 0.49%, respectively. The PTEs for the ferrite-only case at the distances of 10, 15, 20, and 30 cm are 20.25%, 2.89%, 1%, and 0.09%, respectively. Owing to ferrite loss, the PTE for the WPTS backed with a ferrite tile worsened by almost half compared with the case presented in Figure 5a.
## 4 Discussion
### Perturbations in the Initial Contact Phase  
The Initial Contact (IC) phase, marking the beginning of the gait cycle, plays a fundamental role in locomotion [34]. It requires good co-ordination, balance, and effective shock absorption capabilities. The perturbation induced in the IC phase affects the entire gait cycle, which is probably the reason why it accounts for the most significant changes observed in the examined parameters.  
The most noteworthy discovery revolved around the alteration in knee joint torques, as the areas showing significant changes covered 55.86% of the gait cycle (Figure 6E). Specifically, the peak knee flexion torque reached 1.15 \(\pm\) 0.29 Nm/kg, marking a staggering 248.48% increase compared to unperturbed gait. It was the most substantial kinetic shift across all perturbations and joints. Additionally, there was a noteworthy rise in peak knee extension torque \(-\)0.66 \(\pm\) 0.21 Nm/kg in the presence of perturbation, compared to the \(-\)0.31 \(\pm\) 0.03 Nm/kg recorded during unperturbed gait. Moreover, the perturbation during the Initial Contact (IC) phase induced substantial alterations in knee kinematics, affecting up to 72.34% of the gait cycle. However, there was only a minor increase of 12.26% in extreme flexion values.  
Perturbations significantly altered the ankle joint angle curve values in 77.38% of the gait cycle area compared to a typical gait pattern. These alterations were most notable in the extremes of plantar flexion, exhibiting a substantial increase from \(-\)23.91 \(\pm\) 9.42 deg to \(-\)4.96 \(\pm\) 2.25 deg. Despite this pronounced shift, the torque exerted by the plantar flexors experienced a notable decrease of 27.72% compared to the torque observed during unperturbed gait.  
Perturbations also significantly affected the kinematic parameters associated with the hip joint. These alterations impacted 61% of the gait cycle, particularly visible during the late stance and all swing phases. Notably, perturbation during the Initial Contact phase induced the most substantial changes in the extreme angular values of the hip joint, with hip flexion increasing by 29.71% and hip extension by 36.62%. In terms of torque, there was a noteworthy 78.69% increase in hip flexion torque.  
It is worth noting that reviews by [PERSON], et al. [15], as well as [PERSON], et al. [11], have underscored that heel strike perturbations are prevalent. This study corroborates this finding, demonstrating that perturbations during the IC phase significantly impacted all examined parameters except torques related to dorsiflexion and hip extension.  
Notably, knee flexion torque exhibited a nearly threefold increase, emphasising the pivotal role of the knee joint and quadriceps muscle in responding to such perturbations. This observation is particularly relevant given that even short periods of immobilisation can lead to rapid muscle weakness [40], potentially heightening the risk of falls triggered by perturbations.
## 7 Numerical Examples
### 3D Analysis of an Exacavation in Layered Rock
Displacements of all tracking points at the middle section (z = 15 m) by the CFEM M-C and CFEM D-P are listed in Table 4. As shown in Table 4, the following information can be obtained: With the release of stratigraphic stress, because of the symmetry of structure and loading, the horizontal displacements of points 1 and 5 are zero. And that vertical displacement of point 1 is the highest. As can be seen from Figure 15, based on the M-C and D-P criteria, \(u_{3}\) of each tracking point are calculated by using four methods: Cosserat Finite Element Method (CFEM) and Traditional Finite Element Method (TFEM), respectively. Figure 15 also indicate that the analytical results are close, especially at the tracking points 3, 4 and 5. Meanwhile, the small plot in Figure 15 represents the variance of \(u_{3}\) calculated using the four methods at each tracking point. The max value of variance is only 0.6554 mm\({}^{2}\). It shows that on the basis of the above two criteria, the four methods were reliable. However, for the positions of the structure with an obvious stress gradient or bending effect, i.e., the top of cave point 1 and the corner of cave point 2, the results of the M-C- and D-P-based Cosserat medium methods are larger. Moreover, the D-P-based Cos-FEA is approximately twice the M-C-based Cos-FEA. Figure 8.0 Contour maps of the maximum principal stress at failure. Figure 8.1 Contour maps of the maximum shear strain at failure. Figure 8.2 Distribution diagram of plastic zones.