esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Optimized Joint Signal Processing Schemes
### Improved Communication Signal Detection
\tag{44}\]  
Combining the signal model in (42) with the probability distribution function of (43) and (44), the importance weight of each symbol sample for SMC [48] can be represented as \[\rho_{g}^{i}(m,k)_{j}= \rho_{g-1}^{i}(m,k)_{j}p\left[\hat{f}_{g}^{i}(m,k)|x_{g-1}^{i}(m,k)_{ j}\right]\] \[= \rho_{g-1}^{i}(m,k)_{j}p\left[\hat{f}_{g}^{i}(m,k)|\mathbf{d}_{g}^{i}(m, k),x_{g-1}^{i}(m,k)_{j}\right] \tag{45}\] \[\times p\left[\mathbf{d}_{g}^{i}(m,k)\right],j=1,2,\cdots,\varepsilon\]  
where \(x_{g-1}^{i}(m,k)_{j}\) is the sample extracted based on subcarriers, and \(p\left[\hat{f}_{g}^{i}(m,k)|\kappa_{g-1}^{i}(m,k)_{j}\right]\) is the prediction distribution of \(\mathbf{f}_{g}^{i}(m,k)\) under the condition of the particles \(x_{g-1}^{i}(m,k)_{j}\). According to the comparison results of the importance weight, the \(L_{sub}\) state combinations and symbol information corresponding to all subcarriers of each OFDM symbol can be obtained. Then, the posteriori probability corresponding to \(l\)th symbol vector can be expressed as  
\[\mathbf{P}(m,k)_{l}=p\left[\hat{f}^{i}(m,k)|\mathbf{\bar{d}}^{i}(m,k)_{l}\right]p \left[\mathbf{\bar{d}}^{i}(m,k)_{l}\right] \tag{46}\]  
where \(\mathbf{\bar{d}}^{i}(m,k)_{l}\) is the \(l\)th symbol vector. We arrange them in descending order by the posteriori probability \(\mathbf{P}(m,k)_{l}\). In addition, after \(N_{c\_sub}\) consecutive detections, the validity of the combination with the largest posterior probability on each subcarrier is also checked sequentially. To reduce the complexity, we use a combination method to check the legality of all combinations without using a lookup table, which can be written as  
\[\gamma=\sum_{k=1}^{K}C_{\mathbf{A}_{k}-1}^{k} \tag{47}\]  
where \(\mathbf{A}\) denotes the vector storing the indexes of all activated subcarriers. According to (47), this paper regards the state combination of \(\gamma<C_{N_{c\_sub}}^{K}\) as legal. Therefore, we can effectively check the legality of all combinations and discard all illegal status combinations. Finally, the legal state combination with the highest posterior probability is selected as the final MIMO-OFDM-IM subblock estimation.
## 3 Optimization of Distribution Scheme
### Optimization of Switch Times Control
On the contrary, Figure 9c uses the alignment E1 of Figure 3 without changing the locking scheme. The number of conversions and switching time nodes of Figure 9c are similar to those of Figure 9b. Since scheme 1 is the preferred scheme for the whole system. By comparing the two options, Figure 9b with phase-locked scheme 1 with the alignment E1 is the better choice when the frequency domain is restricted to the range of [7 MHz, 22 MHz]. Figure 10a-c show the comparison in the frequency domain at 5 MHz-25 MHz. In Figure 10a, only three frequency switches are made in five years, and the offset frequency is too close to the upper bound of the frequency domain. Figure 10b performs a reduction operation on the frequency domain to achieve a lower offset frequency by appropriately reducing the duration of each segment. Figure 10c changes the choices of alignment pattern to alignment C2. The number of analyses for the above tests should be kept between 6 and 13 as much as possible. In this case, the best solution is obtained by using scheme 1 and reducing the reasonable frequency domain appropriately. In order to fully explore the possible frequency domain range settings, we traversed three additional phase-locking schemes and six alignment patterns based on the above reasonable intervals for the number of conversions. The results are shown in Figure 11.  
Figure 11: Number of Conversions in Each Frequency Domain of The Scheme 3. Figure 10: Solution Optimization Comparison of [5 MHz, 25 MHz]. Figure 9: Solution Optimization Comparison of [7 MHz, 22 MHz]. The green parts show the frequency bands for which a complete frequency distribution scheme can be generated. The number of transitions is all kept within a reasonable range of 6-13. Yellow can generate complete frequency distribution schemes, but the switch is more than 13. The red color indicates the inability to generate a complete scheme. The phase-locking scheme 2 in Figure 11a is consistent with scheme 4 in Figure 11c, with an acceptable minimum frequency domain of [6 MHz, 19 MHz]. Scheme 3 accepts a narrower frequency domain range, specifically [6 MHz, 17 MHz]. It is also the minimum frequency domain for a reasonable number of conversions.
## 2 Materials and Methods
### STIL Ranging Data Processing
\(\quad\) Intrinsic Delay Calibration and Environmental Delay Calibration  
To obtain an accurate delay value \(\tau\), it is necessary to perform on-site experiments by calibrating detection points at known distances during actual measurements:  
\[t_{range}(k,m)=\Delta t_{trigger}(k)+\Delta t_{streak,echo}(k,m,n_{echo}(m))+\tau \tag{8}\]  
The calibration of the intrinsic delay \(\tau_{ins}\) is performed by imaging a fixed target and measuring the system's delay parameters, which are solely dependent on the system's characteristics. Typically, this calibration is required only once. In contrast, the environmental delay \(\tau_{env}\) must be accurately calibrated during actual detection, based on the specific conditions at the current detection point. The target distance is then calculated by combining these delay parameters with the actual measured echo streak time \(t_{streak,echo}\) and pulse time \(\Delta t_{pulse}\). To analyze the ranging accuracy of the system, we introduce the standard deviation of errors to statistically quantify the ranging accuracy:  
\[\sigma_{range}=\sqrt{\frac{1}{n}\sum_{k=1}^{n}\left(t_{range}^{*}(k,m)-\overline {t_{range}^{*}}\right)^{2}} \tag{9}\]  
where \(n\) denotes the number of ranging pulses directed at the same target. By averaging the ranging values for a target at a fixed distance and calculating the standard deviation, the system's ranging accuracy can be evaluated. This method isolates the effects of delay calibration on measurement accuracy, focusing specifically on the ranging errors associated with the TDC unit and streak image processing.
## 2 Materials and Methods
### Determination of Edible Film Characteristics
#### 2.3.1 Physical Properties
Moisture content of obtained films was measured according to the following Equation (1):  
\[Moisture\ content\ (\%)=\frac{M_{i}-M_{f}}{M_{i}}\times 100 \tag{1}\]  
Opacity  
The opacity was determined using a UV-vis Spectrophotometer (Cary 50 Bio, Melbourne, Australia) as reported by [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] [36] with some modifications. Film strips (10 mm \(\times\) 50 mm) were placed in a quartz cuvette to measure the light absorbance at 560 nm. Film samples were measured in triplicate. Calculation of opacity was based on the absorbance divided by the film thickness as the following equation:  
\[Opacity=\frac{Abs_{560}}{T} \tag{2}\]  
where \(Abs_{560}\): the absorbance at 560 nm and \(T\) was the thickness of the obtained film (mm). Colour  
The colour attributes were measured using a colorimeter (CR-300, Konica Minolta, Tokyo, Japan) according to a previous method [25]. The instrument was calibrated using a white colour plate as a background. Difference measurements of an individual film were performed by lightness (L), red-green (a), and yellow-blue (b). The total colour difference (\(\Delta\)E) was directly provided by the instrument. Chroma and Hue angle, were established by Equations (3)-(5) respectively. Mean of ten different measurements was taken as the colour result of an individual film. \[Chroma=\sqrt{a^{2}+b^{2}} \tag{3}\]  
\[\text{Hue angle}=\text{Degrees (arctan (b/a)) if a}>0 \tag{4}\]  
\[\text{Hue angle}=180^{\circ}+\text{Degrees (arctan (b/a)) if a}<0 \tag{5}\]
## 3 Results and Analysis
### Accuracy Assessment  
Refer to the relevant RS saline soil-related research [17], the categories of different salinization levels are classified according to the soil electrical conductivity value: the salt content of non-saline soil is less than 2 mS-cm\({}^{-1}\); The salt content of light saline soil is 2\(\sim\)4 mS-cm\({}^{-1}\); the salt content of moderately saline soil is 4\(\sim\)8 mS-cm\({}^{-1}\); the salt content of severely saline soil is 8\(\sim\)16 mS-cm\({}^{-1}\); the salt content of extremely severe saline soil is \(>\)16 mS-cm\({}^{-1}\). As a result, this study measures the electrical conductivity of the soil sampling data based on this standard, classifies the salinization, and determines the accuracy of the RS-based salinity monitoring index models S1 DI1, SIDI2, SIDI3, and SIDI4. The results are shown in Table 4 and Figure 5.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{\
\begin{tabular}{c} **Correct Sample** \\ **Classification** \\ \end{tabular} } & \multicolumn{2}{c}{**Sample Classification Error**} & \multirow{2}{*}{
\begin{tabular}{c} **Overall** \\ **Accuracy \%** \\ \end{tabular} } \\ \cline{3-3} \cline{5-6}  & & **Non-Salt** & & **Light** & **Total** & **Accuracy \%** \\ \hline S1 DI1 & 111 & 3 & 1 & 4 & 96.52 \\ S1 DI2 & 110 & 4 & 1 & 5 & 95.65 \\ SIDI3 & 109 & 4 & 2 & 6 & 95.78 \\ SIDI4 & 109 & 5 & 1 & 6 & 95.78 \\ \hline \end{tabular}
\end{table}
Table 4: Model accuracy.  
Figure 5: The fitness between the model and the measured conductivity.  
It can be seen in Table 4 that, using the remote sensing salinity monitoring index model to invert the soil salinity of cotton fields in the Alar reclamation area, the S1 DI1 model has the largest number of correctly classified sample points, while the S1D3 and S1 DI4 models have relatively few correctly classified sample points. The overall accuracy of the S1 DI1 model, S1 DI2 model, S1 DI3 model, and S1 DI4 model is 96.52%, 95.65%, 95.78%, and 95.784%, respectively. Among the sample classification errors, the S1 DI1 model, S1 DI2 model, S1 DI3 model, and S1 DI4 model sample point classification errors were mainly caused by non-saline soil.  
In Figure 5, it can be seen that the fitting degrees of the S1D1 model, S1 DI2 model, S1 DI3 model, and S1 DI4 model are R\({}^{2}\) = 0.8335, R\({}^{2}\) = 0.8310, R\({}^{2}\) = 0.8296, and R\({}^{2}\) = 0.8025, respectively. Moreover, it can be observed that the S1 DI1 model, S1 DI2 model, S1 DI3 model, and S1 DI4 model are positively correlated with the measured conductivity.  
In summary, the S1 DI1 model constructed by S1 and GRNDVI has an overall classification accuracy of 96.52% and a fitting degree of R\({}^{2}\) = 0.8335 between the model and the measured conductivity. According to the above analysis, the S1 DI1 model is more suitable for extracting information on soil salinization in cotton fields in the Alar reclamation area.
# C-V2X Centralized Resource Allocation with Spectrum Re-Partitioning in Highway Scenario
## 1 Introduction
The RC values generated in an RSU can provide the capability to advance knowledge when the vehicles need new resources. In this way, assuming stationary sub-channels and transmission power, vehicles do not need to send requests to RSU for new resources. Thus, reducing signalling costs can be achieved. Furthermore, we propose the fractional frequency reuse and soft frequency reuse concepts [20] to be used by an RSU to specify the vehicles' broadcast resources. In other words, frequency partitioning is used to grant vehicles with resource allocations needed to broadcast CAM messages. The given bandwidth is divided into two bands used by two adjacent RSUs. In our proposal, the full frequency reuse technique (FFR) partitions the bandwidth into two separate bands for two adjacent RSUs to guarantee that the vehicle's broadcast does not overlap. In this case, the frequency reuse distance means that another single RSU coverage area is located between two RSUs using the same band. Moreover, in the partial frequency reuse technique (FFR), further re-partitioning of the band into two sub-bands (inner and outer bands) takes place for central and edge users, respectively, and the sub-bands are distributed inversely with respect to the near RSUs that use the same band. As a result, the partial frequency reuse technique promises encouraging results as compared with the full frequency reuse technique. Moreover, both techniques provide performance advantages over the system in which a regular single band (SB) is applied for all RSUs.
## 5 SMS on Screen  
In a mobile network, all short messages are sent to the _Short Message-Service Center_ [SM-SC; see (8) in Figure 1] before they are delivered to the destinations (typically mobile handsets). By integrating MOD-SDP and SM-SC, the IPTV-CS can display an MOD user's short messages on the TV screen. Figure 7 shows the message flow of SMS on TV screen. At the initialization phase (Step 0 of Figure 7), the IPTV-CS creates a SOCKET connection with the SM-SC and specifies the mobile phone numbers of the MOD users who subscribe to this service (and therefore, the short messages for the MOD users will be forwarded to the STBs/TVs). These mobile phone numbers are saved in the MOD user database of the SM-SC. When a short message is sent from a mobile user [UE2; (10) in Figure 1] to the MOD user [UE1; (3) in Figure 1] with the STB [(11) in Figure 1], the following steps are executed:  
**Step 1:**: The short message is first delivered to the SM-SC through the GSM-MAP protocol [11].
**Step 2:**: The SM-SC confirms UE2 the receipt of this short message, and decodes it [12] to retrieve the called ID.
**Step 3:**: If this phone number is found in its MOD user database, the SM-SC forwards the message to the IPTV-CS through the SOCKET interface. This SOCKET message contains the calling ID, the called ID and the message content.  
Figure 6: Message flow of the weather information.  
**Step 4:** The IPTV-CS encapsulated the short message in an SNMP message sent to the target STB [(11) in Figure 1]:  
snmpset-c private-v 2c [STB IP] [OID] \"[text]:this is a short message from 0928123456 [xywh]:50,50,400,80[timeout]:300\"  
where private is the SNMP community name, STP_IP and OID are the IP address and the object identifier of the target STB, text is the tag for the message, xywh is the tag for the position and size of the message window, and timeout is the elapsed time to display the message on the TV screen.  
The above message flow can be added some additional parameters such that when a short message arrives at the STB, the text is not shown on the TV screen (to enhance privacy).
## 3 Materials and Methods
### Optimization of the QuEChERS Procedure
#### 3.5.2 Selection of Cleaning Sorbent  
It is imperative to perform the clean-up procedure during extraction to enhance the sensitivity, specificity, and purification degree [38; 39]. Notably, the selection of the sorbent type is crucial, as it significantly impacts the clean-up procedure [38; 39]. C18, PSA, and GCB are commonly used adsorbents for QuEChERS purification [40; 41]. Therefore, the OTA clean-up effect of C18, PSA, and GCB was evaluated in this study. PSA, a weak anion exchange agent known for removing polar interferences, possesses excellent recovery and reproducibility when used for extracting various compounds with diverse properties [42]. C18 is employed for eliminating fats and non-polar compounds [43]. GCB exhibits a superior ability to remove pigments from grape samples compared to other sorbents, thereby yielding a more refined extract [12]. In this study, graphite powder was also used as a non-traditional purification agent. The chemical properties of graphite powder are relatively stable at room temperature. It is insoluble in water but soluble in acid, alkali, and organic solvents. Thus, 150 mg of anhydrous magnesium sulfate was combined with 30 mg of PSA, 30 mg of GCB, 30 mg of C18, and 80 mg of graphite powder, respectively, and used in the steps taken to purify the grape samples.  
As shown in Figure 7, small OTA peak areas were obtained when GCB was used as the adsorbent. This can be attributed to the strong affinity between OTA and GCB, because the closer mycotoxin is to a planar structure, the stronger its affinity for GCB [44]. Conversely, large OTA peak areas were obtained when C18, PSA, and graphite powder were used as the adsorbents and when \(\text{ACN}/\text{formic acid}\) (\(99/1,\,v/v\)) was the extractant. However, C18 exhibited a limited ability to remove pigments, resulting in samples with a relatively dark color. Moreover, PSA is costly compared to graphite powder, thus making graphite powder a cost-effective alternative. Therefore, graphite powder was selected as the cleaning sorbent, and 150 mg of anhydrous magnesium sulfate and 80 mg of graphite powder were used in the purification procedure.
# Legal Judgment Prediction via Heterogeneous Graphs and Knowledge of Law Articles
## 4 Experiments
### Ablation Experiments  
We designed the relevant ablation experiments which used the same comparison metric. First, we designed experiments for the text graph types by subtracting one of the text graphs in turn. Table 7 shows the results of this experiment and we can see that the impact of the graph without co-occurrence graph experiment was the largest, which indicates that the co-occurrence graph contained more information about the features that could be used in this task. The impact of the missing Cosine graph, Euclidean graph, Manhattan graph and Chebyshev graph on the results was less than that of the PMI graph,  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Accuracy**} & \multicolumn{2}{c}{**Precision**} & \multicolumn{2}{c}{**Recall**} & \multicolumn{2}{c}{**F1**} \\ \cline{2-9}  & **Max** & **Min** & **Max** & **Min** & **Max** & **Min** & **Max** & **Min** \\ \hline Law Articles\({}_{big}\) & 96.53 \(\uparrow\) & 96.50\(\uparrow\) & 85.77\(\uparrow\) & 85.73\(\uparrow\) & 80.25\(\uparrow\) & 80.21- & 81.43\(\uparrow\) & 81.37\(\uparrow\) \\ Law Articles\({}_{small}\) & 81.50 \(\uparrow\) & 81.21 \(\uparrow\) & 78.12 \(\uparrow\) & 77.95 \(\uparrow\) & 72.52 \(\uparrow\) & 72.41 \(\downarrow\) & 73.85 \(\uparrow\) & 73.80 \(\uparrow\) \\ Charges\({}_{big}\) & 96.46\(\uparrow\) & 96.39\(\uparrow\) & 88.08\(\uparrow\) & 88.04\(\uparrow\) & 83.19\(\uparrow\) & 83.06\(\uparrow\) & 84.78\(\uparrow\) & 84.59\(\uparrow\) \\ Charges\({}_{small}\) & 85.12\(\uparrow\) & 84.61\(\uparrow\) & 84.27\(\uparrow\) & 83.36\(\uparrow\) & 82.48\(\uparrow\) & 82.15\(\uparrow\) & 81.92\(\uparrow\) & 81.35\(\uparrow\) \\ Terms of Penalty\({}_{big}\) & 59.57\(\uparrow\) & 59.52- & 51.87\(\uparrow\) & 51.83- & 45.23\(\uparrow\) & 45.19\(\downarrow\) & 47.03\(\uparrow\) & 46.96- \\ Terms of Penalty\({}_{small}\) & 38.35\(\uparrow\) & 38.19\(\uparrow\) & 36.12\(\uparrow\) & 35.98\(\uparrow\) & 32.37\(\uparrow\) & 32.21\(\uparrow\) & 32.68\(\uparrow\) & 32.46\(\uparrow\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Maximum and minimum values of LJP experimental results.  
Figure 5: AUC scores and ROC curves of several models on CAIL-big.  
which indicates that we could pursue the model's efficiency using only one of the similarity calculation methods for constructing the graph.  
We also conducted experiments in the graph fusion section, where we used simple concatenation and took an average. As we can see in Table 8, the experimental results indicate that our approach achieved the best results. The simple concatenation and averaging approaches destroyed the feature information and degraded the model's performance.
# USD-YOLO: An Enhanced YOLO Algorithm for Small Object Detection in Unmanned Systems Perception
## 3 Methodology
### Architecture of USD-YOLO  
The architecture of the USD-YOLO algorithm is shown in Figure 6. Compared with the baseline model YOLOv8, USD-YOLO integrates the three designed modules SCConv, CGAM, and APE into the backbone, neck, and post-processing parts of the network, respectively.  
The backbone is responsible for extracting multi-scale features from the input image; USD-YOLO introduces the SCConv module into the backbone. The image first passes through a series of Conv and C2f modules for initial feature extraction. The SCConv module is inserted between the C2f module and the Spatial Pyramid Pooling-Fast (SPPF) module. SCConv reduces redundancy in channel and spatial features, filtering out key features of small objects in the image and passing them to the SPPF module. After the SPPF module, the backbone outputs multi-scale feature maps (P3, P4, P5) for further processing in the neck part.  
Figure 4: The schematic diagram of the GAM module.  
Figure 5: The schematic diagram of the CGAM module.  
Figure 6: The structure of the USD-YOLO algorithm.  
The backbone is responsible for extracting multi-scale features from the input image. USD-YOLO replaces the original C2f module with the CGAM module in the neck. The neck upsamples and concatenates the feature maps output by the backbone to fuse features at different scales. After upsampling and concatenation, the CGAM module performs convolution operations on the features and employs a global attention mechanism to expand the receptive field, providing more contextual information. After processing by the CGAM module, the neck outputs enhanced feature maps for detection in the head part.  
The post-processing step filters the original predictions from the head and removes redundant detections. USD-YOLO introduces the APE module in the post-processing part behind the head. The head receives the feature maps (P3, P4, P5) output by the neck and generates candidate detection boxes along with their confidence scores. After generating candidate boxes, the APE module improves traditional IoU and NMS. APE provides more accurate candidate box overlap measurements and a smarter processing mechanism, enhancing the precision of candidate boxes. After processing by the APE module, the final detection results significantly improve the precision of densely distributed and occluded small objects.  
By integrating the SCConv, CGAM, and APE modules into the backbone, neck, and post-processing parts, USD-YOLO significantly improves the detection precision of small objects.
# Variable Neighborhood Search for the Two-Echelon Electric Vehicle Routing Problem with Time Windows
## 3 Problem Description and Mathematical Model
Constraint (15) ensures that each customer is served by only one satellite. Constraints (16), (17), and (19) ensure that each electric vehicle completes its tour at the same satellite from which it started the tour. Constraint (18) guarantees that each electric vehicle can provide service through only one satellite. Constraints (23) and (24) prevent returning to the node from which a vehicle just departed. Constraints (25)-(32) are battery state constraints. Constraint (33) states that the load of a vehicle is the same when arriving and departing from a charging station. Constraints (34)-(39) calculate arrival and departure times considering service and battery charging times. Moreover, constraints (40)-(43) restrict the visiting time of each customer with respect to the time windows. Finally, constraint (44) defines variable domains. The classical VRP is NP-Hard [36]. The multi-tier distribution structure (two echelons) and additional limitations such as the driving range of electric vehicles and customers' time windows further increase the complexity. As the computation time required to solve such complex problems to optimality increases dramatically with a growing instance size, most approaches from the related literature for similar problems are approximate techniques, especially in the context of large-sized problem instances. In this study, we propose an approach based on VNS to solve the 2E-EVRP-TW. Algorithm 2 presents the general structure of the proposed algorithm. It starts with the application of a modified version of the Clarke and Wright Savings Algorithm to obtain an initial solution quickly and efficiently. Subsequently, shaking and local search procedures are applied to improve the initial solution. However, before describing the proposed algorithm, we first explain how a solution \(S\) is represented, and subsequently we outline an extended objective function used to handle infeasible solutions.