esa-sceva/satcom-chunk-collection huggingface dataset:
# Agglomerative Clustering with Threshold Optimization via Extreme Value Theory
## 4 Approach
### Overall System Operation  
The overall system operation shown in Figure 4. The processing starts with training data with labels (that we will only use for evaluation), then we extract deep features from a deep learning system. We compute the nearest neighbors of extracted deep features. The nearest neighbors are used to generate initial pairs for PEACH, and we compute the pairwise distance \(d(\mathcal{S})\). Now, we can use Weibull fitting to compute our Weibull estimated threshold \(\tau_{w}\) as given in (6). In order to obtain out robust threshold \(\tau_{r}\), we estimate the median and maximum distance \(d(\mathcal{S})\) of all pairs of data points.  
By using \(\tau\in\{\tau_{w},\tau_{r}\}\), we decide if any set of clusters should merge. At the beginning of merging, as typical Agglomerative clustering, PEACH treats every sample as a single cluster, then we generate initial clusters from these single clusters. All paired clusters with link distance below the threshold \(\tau\) merge. Next, we find the nearest cluster to each cluster and generate a merging list for this level. Unpaired clusters just pass upward unmerged until they are the closest neighbor of some cluster. We repeat finding all nearest neighbors, building a merge list, and merging until the list of clusters does not change. After merging, we assign cluster labels \(C_{i}\) to the grouped data.  
Figure 3: _EVT-based Threshold Works Well._ We show multiple clustering algorithms’ NMI performance on the IJB-B 512 face dataset.
# Designing an Adaptive Underwater Visible Light Communication System +
## 5 Transmitter Design
### Receiving the Acknowledgment  
This step is represented by the \"yellow line\" in Figure 6. After receiving the preamble, the receiver calculates and compares the received power at different angles to identify the angle with the strongest signal. It then adjusts its position to align with this optimal angle and generates an acknowledgment (ACK) signal, which is modulated with a 2.4 MHz tone and sent back to the transmitter via loopback. The ACK signal is detected by the LFRx1  
Figure 6: Flow chart for transmitting station.  
of the transmitting USRP, and its power is measured. If the power exceeds a specified threshold, MATLAB prepares the transmitter to send the actual message. If the ACK signal is not received after a certain time, the transmitter starts the process again by transmitting the preamble.
## 2 Materials and Methods
### Study Area and Data Source
#### 2.1.4 Farmland Area Vectorization  
The GlobeLand30 raster data were cropped and reclassified in ArcGIS to obtain the raster of the farmland region in the study area (Figure 3a), and the conversion tools in ArcGIS were used to convert the raster to a vector in order to obtain the vector boundary of the farmland area (Figure 3b). However, due to the fact that the spatial resolution of GlobeLand30 is coarser than the resolution of GF-6 data, many of the small features, as well as the feature boundaries, are biased and need to be adjusted manually. After the conversion process, a GF-6 image was used as the background for manual adjustment in ArcGIS (Figure 3c). The process is illustrated in Figure 3.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Feature** & ** Landsat 8** & **GF-6** & **GF-2** & **Google Earth** & **ALOS** \\ \hline Data type & \multicolumn{4}{c}{Remote-sensing data} & DEM \\ Altitude (km) & 705 & 645 & 631 & 692 \\ Number of bands & 11 & PAN:8 MS:2 & PAN:4 MS:1 & \\ Temporal resolution (d) & 16 & 41 & 69 & 14 \\ Spatial resolution (m) & 30 & PAN:2 MS:8 & PAN:1 MS:4 & 0.6 & 12.5 \\ B4/Red: & B3/Red: & & & \\ Band/wavelength (\(\mu\)m) & 0.64-0.67 & 0.63-0.69 & & \\  & B5/NIR: & B4/NIR: & & \\  & 0.85–0.88 & 0.76–0.90 & & \\  & & MS: & MS: & \\  & & 0.45–0.90 & 0.45–0.90 & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of remote-sensing data.  
Figure 3: Steps for manual vectorization. (**a**) Farmland area in GlobeLand30 raster, (**b**) farmland area vector converted by GlobeLand30 raster, and (**c**) manually adjusted farmland area vector.  
The base statistical data of the manual vectorization results are shown in Table 2.
# A Mobility Management Using Follow-Me Cloud-Cloudlet in Fog-Computing-Based RANs for Smart Cities
## 4 Mobility Management Using Follow-Me Cloud-Cloudlet Approach  
In the section, mobility management using proposed follow-me cloud-cloudlet approach in F-RAN for smart cities is given. As mentioned in Section 3.3, four kinds of cooperation of cloud-cloudlet are defined, our mobility management is divided into four phases to implement these four cooperation of cloud-cloudlet, respectively.  
1. _Follow me phase_: This phase is to implement the cooperation of cloud-cloudlet \(CL_{s\to s}\). Before the handover event of UE, assumed that \(c_{s}\) and \(l_{s}\) are the current serving cloud and cloudlet of the current serving F-RAN. Packets \(p_{s}\) from the CN in data center of \(c_{s}\) are transmitted to UE through F-AP\({}_{s}\).
2. _Follow me cloudlet phase_: This phase is to implement the cooperation of cloud-cloudlet \(CL_{s\to d}\). When UE moves to a different region, and initiate the handover procedure, some packets are still transmitted from the current serving data center of \(c_{s}\) to the new serving cloudlet \(l_{d}\) if the data migration procedure is still not initiated.
3. _Follow me cloud phase_: This phase is to implement the cooperation of cloud-cloudlet \(CL_{d\to d}\). After UE is moving to a different region, a data migration operation is executed from \(c_{s}\) to \(c_{d}\), it also means that CN is migrated from \(c_{s}\) to \(c_{d}\). Packets are then transmitted from the new serving data center of \(c_{d}\) to the previous cloudlet \(l_{s}\), and then be re-forward to the new cloudlet \(l_{d}\) to UE, before the new route path from \(c_{d}\) to \(l_{d}\) is not re-calculated in FMCC.
4. _Follow me cloud-cloudlet phase_: The phase is to implement the cooperation of cloud-cloudlet \(CL_{d\to d}\). After the new route path is re-routed from the new serving data center of \(c_{d}\) to the new cloudlet \(l_{d}\) which is determined by FMCC, packets \(p_{d}\) are transmitted from \(c_{d}\) to UE through F-AP\({}_{d}\) of \(l_{d}\) by using the new re-calculated route path.  
The detailed operations of the mobility management using proposed follow-me cloud-cloudlet approach in F-RAN are described as follows. Message flow diagrams of PMIPv6-based FMC approach and our proposed mobility management using FMCL approach, are illustrated in Figure 6.  
Figure 5: Basic operations of (**a**) cooperation of cloud-cloudlet \(CL_{d\to s}\) and (**b**) cooperation of cloud-cloudlet \(CL_{d\to d}\).
## 3 Case Study
### Casting Mold Liquid Surface Segmentation Experiment and Result Analysis  
#### 3.2.1 Casting Mold Liquid Surface Segmentation Dataset Description  
A total of 150 images, which captured the liquid surfaces of casting molds during regular casting operations, were selected from the dataset outlined in Section 3.1. These images were taken between March and April 2023. The selection criteria prioritized clear images that depicted numerous scenarios, including diverse times, lighting conditions, liquid levels, casting units, and aluminum ingot parameters. After performing region extraction, picture denoising, data augmentation, and annotation using the Labelme program, a total of 1200 images were obtained. Afterward, the photographs were divided into training and validation sets in a random manner, with a ratio of 8:2. This resulted in 960 images for training and 240 images for validation.
###### Abstract  
Coherent wind doppler lidar (CWDL) is a cost-effective way to estimate wind power potential at hub height without the need to build a meteorological tower. However, fog and low stratus (FLS) can have a negative impact on the availability of lidar measurements. Information about such reductions in wind data availability for a prospective lidar deployment site in advance is beneficial in the planning process for a measurement strategy. In this paper, we show that availability reductions by FLS can be estimated by comparing time series of lidar measurements, conducted with WindCubes v1 and v2, with time series of cloud base altitude (CBA) derived from satellite data. This enables us to compute average maps (2006-2017) of estimated availability, including FLS-induced data losses for Germany which can be used for planning purposes. These maps show that the lower mountain ranges and the Alpine regions in Germany often reach the critical data availability threshold of 80% or below. Especially during the winter time special care must be taken when using lidar in southern and central regions of Germany. If only shorter lidar campaigns are planned (3-6 months) the representativeness of weather types should be considered as well, because in individual years and under persistent weather types, lowland areas might also be temporally affected by higher rates of data losses. This is shown by different examples, e.g., during radiation fog under anticyclonic weather types.  
wind; lidar; availability; fog; clouds +
Footnote †: journal: Energy Planning
## 3 Method  
### Uncropped Arable Land Ratio (UALR) Derivation  
The first step for UALR calculation is the separation of cropped fields and uncropped fields over the arable land fields. Arable land was extracted using the 2010 land-cover map. Over the arable land, the differences in the CCD NDVI between late-March and early May were calculated for both 2010 and 2011, and cumulative frequency histograms of these differences were constructed for the training samples for both years. We tried to acquire cloudless images, but there were still a few cloudlets in some of the images. In order to reduce the impact of unfavorable weather conditions, an initial NDVI threshold value of 0.4 for early May was used to identify cropped land. According to the winter crop phenology, winter crops are at the growing peak in early May, while bare soil or only sparse weeds can be observed over uncropped arable land. Otherwise, a threshold for the differences in the CCD NDVI between late-March and early May was used to separate cropped and uncropped arable land fields. The maximum difference in NDVI over the training samples was not taken as the CCD NDVI difference threshold, because of the noise in NDVI images [36,37]. In this paper, we found that when the threshold was set as 98% cumulative frequency of NDVI differences, the highest identification accuracy of cropped and uncropped arable fields can be achieved. A similar method was used to extract maximum or minimum NDVI for each biome [37, 38, 39, 40]. The corresponding decision tree used to identify cropped and uncropped arable land fields is shown in Figure 3.  
The accuracy evaluation of the cropped and uncropped arable land maps was based on the independent validation samples. To evaluate the classification results, classification accuracy measures (including the producer's accuracy, the user's accuracy for each type and the overall accuracy) and kappa statistics estimated from the confusion matrix were used [41].  
To generate the UALR map, the cropped/uncropped distribution map was resampled to a 250-m resolution using the nearest neighbor resample method. The values for each 250-m pixel were obtained by counting the number of uncropped (Nuc) and cropped (Nc) pixels at 30-m resolution located in each 250-m pixel. The uncropped arable land ratio (UALR) for each pixel was calculated using the equation:  
\[\text{UALR}=\frac{\text{N}_{\text{uc}}}{\text{N}_{\text{uc}}+\text{N}_{\text{ c}}} \tag{1}\]  
UALR was assumed to be stable during the growing season.
## 4 Discussion  
To better evaluate the effectiveness of the design modules, we performed ablation experiments on our MFACNet. In the ablation experiments, we selected the CenterTrack model as the baseline and compared it with the proposed FAW, FMME, and MFACNet models. It is worth emphasizing that we used 50% of videos from the training set for training and the remaining 50% for performance validation in the experiments. The results of the ablation experiments are presented in Table 4.  
Compared with the baseline model, the inclusion of the FMME module led to an improvement of 0.2% in the IDF1, with a significant reduction of 24.4% in the IDSWs. Furthermore, there was a 1.8% enhancement in the MOTA, 0.3% in the IDF1, and 3.7% in the MTs, along with an additional reduction of 2016 in the IDSWs when both the FMME and FAW were simultaneously incorporated. Additionally, by incorporating IoU-prior cascaded matching, the integration of the MFACNet reduced the IDSWs and improved the IDF1 in the aforementioned performance. CenterTrack utilizes two consecutive frames and contextual cues as inputs, relying solely on localization information for trajectory matching. In comparison, the MFACNet significantly improved the MT while reducing both the FNs and IDSWs, resulting in a 2.1% improvement in MOTA performance and a 0.6% improvement in the IDF1 compared to CenterTrack.
# Neuroimaging of Pediatric Cerebellum in Inherited Neurodegenerative Diseases
## 1 Introduction
### Cdg  
Congenital disorders of glycosylation (CDG), which is the addition process of complex sugar chains to lipids and proteins, comprise most of the genetic disorders due by abnormal synthesis of glycoconjugates. These disorders involve different organ systems, including the nervous system [18].  
Congenital disorder of glycosylation type 1A (CDG1A) is the most common \(N\)-linked CDG and is responsible for around 80% of all cases. This disorder is caused by mutations in _PMM2_ gene. The CNS and peripheral nervous system (PNS) are the primary targets [19].  
As a laboratory finding, the isoelectric focusing of serum transferrin showed a consistent increase of isotransferrins with higher isoelectric points than normal [20].  
CDG1A is an important cause of cerebellar ataxia. In a study of posterior fossa imaging of 158 children with ataxia, 84 presented global cerebellar atrophy; 21 of them had PMM2 mutation, while 18 presented defects of the respiratory chain [21].  
Most children with CDG1A have small pontine protuberance, shrunken appearance of the superior vermis and flattened inferior vermis, and hyperintense hemispheric cerebellar cortex at birth. In milder cases, the initial radiological findings could be normal with a subsequent atrophic evolution (Figure 5).  
The cerebellar white matter is also slightly hyperintense compared with supratentorial white matter, leading to the typical \"bright cerebellum\" appearance. 1H-MRS shows a markedly reduced NAA peak [22]. Pathological findings suggest very early-onset hypoxia followed by progressive atrophy [22,23].  
Figure 4: ARSACS. Brain MRI scan of a 15-year-old boy. The images demonstrate the superior vermis and cervical spinal cord atrophy, bulky pons, and a thin corpus callosum. T2-wi at the level of the pons show diffuse, slight hyperintensity mostly in the lateral part of the pons and in the raphe. Corticospinal tracts are visible as hypotintense parallel, thin stripes. The medial lemnisci also presented similar hypointensity. Note in the last image a rim of T2-hyperintensity around the thalami.
## 1 Introduction
### Related Works  
An RIS's unique capabilities allow for the low-cost reconfiguration of the propagation environment, capitalizing on multipath effects to optimize spatial resources and enhance wireless network performance. RIS-aided communications have attracted much attention in various applications. Current research on RIS has primarily centered on passive or joint beam-forming design [11; 12; 13]. Additionally, there have also been works focusing on the deployment of the RIS [14; 15; 16], physical layer security [17; 18; 19], RIS-assisted UAV communications [20; 21; 22], wireless power transfer [23; 24; 25], and so on. Research on RIS is currently focused on the latest advancements, including the emerging paradigm of stacked intelligent metasurface (SIM) [26] and the innovative idea of a space-time coded RIS [27; 28].  
By leveraging the programmable nature of RIS elements, the incident wavefronts can be precisely shaped to enhance the CF network. Currently, several researchers are attempting to characterize the performance of RIS-CF networks. References [29; 30] considered sum rate maximization and energy efficiency maximization in RIS-CF networks. In detail, the authors in [29] jointly designed a precoding at the BSs and RISs in a CF system. In [30], the authors proposed a hybrid beam-forming strategy that combines digital beam-forming at BSs with analog beam-forming at the RIS. RIS-CF networks provide a cost-effective way to further boost the performance of traditional communication networks. In [31], the authors  
Figure 1: The proposed concept of an RIS-CF network.  
considered the RIS-user association problem in a RIS-aided system, where it was limited to the case with a single BS serving a single user and there is no coordination between BSs. In contrast to [31], we consider CF networks in this paper where users are served by multiple selected BSs. In [32], the authors investigated the optimization of BS-user associations in RIS-CF networks, aiming to maximize the minimum SINR across all users, thus enhancing the overall network performance, and proposed a two-step fractional linearization algorithm to optimally solve this problem. However, in the solution process, the power of BSs defaults to 1, and the influence of power allocation on the network performance is not considered. In contrast, in this paper, we consider the joint optimization of power and BS selections, which can improve system performance to a greater extent. A comparison of the related works is summarized in Table 1.
## 2 Methodology
### Theory of EWT
\[W_{f}^{\tau}(0,t)=\langle f,\phi_{1}\rangle=\int f_{\tau}\overline{\phi_{1}( \tau-t)}d_{\tau}=\langle\hat{f}(\omega)\hat{\phi}_{1}(\omega)\rangle^{\vee} \tag{5}\]  
\[W_{f}^{\tau}(n,t)=\langle f,\psi_{n}\rangle=\int f_{\tau}\overline{\psi_{n}( \tau-t)}d_{\tau}=\langle\hat{f}(\omega)\hat{\psi}_{n}(\omega)\rangle^{\vee} \tag{6}\]  
Following this formalism, we can obtain the empirical mode component \(f_{k}\), which is expressed as follows:  
\[f_{0}(t)=W_{f}^{\tau}(0,t)*\phi_{1}(t)=\langle W_{f}^{\wedge}(0,\omega)\hat{ \phi}_{1}(\omega)\rangle^{\vee} \tag{7}\]  
\[f_{k}(t)=\sum_{n=1}^{N}W_{f}^{\tau}(n,t)*\psi_{n}(t)=(\sum_{n=1}^{N}W_{f}^{ \wedge}(n,\omega)\hat{\psi}_{n}(\omega))^{\vee} \tag{8}\]  
where \(*\) denotes convolution. From the above, it is evident that the AM-FM components composed of \(f_{k}\) are directly influenced by segmentation boundaries. Thus, boundaries can determine the decomposition effect of EWT.
## 3 Results  
Table 1 and Figure 6 display the means and SD values for the postoperative volumetric differences (mm\({}^{3}\)) of the internal threads at the conical internal hex implant-abutment connection among the study groups.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Study Group** & \(n\) & **Mean** & **SD** & **Minimum** & **Maximum** \\ \hline Conventional & 10 & 47.14 a  & 0.44 & 46.60 & 47.73 \\ Phibo & 10 & 47.18 a  & 0.49 & 46.37 & 47.82 \\ Rhein83 & 10 & 46.80 a  & 0.77 & 45.89 & 47.79 \\ Sanhigia & 10 & 46.74 a  & 1.04 & 45.17 & 48.07 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Descriptive statistics of the volumetric differences (mm\({}^{3}\)) of internal threads at the conical internal hex implant-abutment connection.  
Figure 6: Box plot of the postoperative volumetric differences (mm\({}^{3}\)) of the internal threads at the conical internal hex implant-abutment connection among the conventional, Phibo, Rhein83, and Sanhigia study groups. The median value is represented by the horizontal line in each box. The \(p\)-value corresponds to the comparison between the means (ANOVA).  
The ANOVA results did not show statistically significant differences (\(p\) = 0.569) in the postoperative volumetric differences of the internal threads at the conical internal hex implant-abutment connection between the conventional, Phibo, Rhein83, and Sanhigia study groups (Figure 6).  
Table 2 and Figure 7 display the means and SD values for the volumetric differences (mm\({}^{3}\)) between the pre- and postoperative volumes of the internal threads at the conical internal hex implant-abutment connection among the study groups.  
The ANOVA results did not show statistically significant differences (\(p\) = 0.808) in the pre- and postoperative volumes of the internal threads at the conical internal hex implant-abutment connection between the conventional, Phibo, Rhein83, and Sanhigia study groups (Figure 7).  
Table 3 and Figure 8 display the means and SD values for the volumetric ratio (%) between the pre- and postoperative volumes of the internal threads at the conical internal hex implant-abutment connection among the study groups.  
The ANOVA results did not show statistically significant differences (\(p\) = 0.971) in the volumetric ratio (%) between the pre- and postoperative volumes of the internal threads at the conical internal hex implant-abutment connection between the conventional, Phibo, Rhein83, and Sanhigia study groups (Figure 8).  
Additionally, the success rate for retrieval of the fractured abutment screws from the conical internal hex implant-abutment connection was analyzed. The results showed that 90% of the fractured abutment screws were successfully removed using the Phibo, Rhein83, and Sanhigia drilling removal systems, whereas 50% of the fractured abutment screws were successfully removed using the conventional technique with an exploration probe and an ultrasonic appliance.
# Resource Allocation in C-V2X Mode 3 Based on the Exchanged Preference Profiles
## 1 Introduction
### Related Work
[21] proposed two new spectrum-repartitioning and frequency-reuse techniques in roadside units for vehicular communications, namely full frequency reuse and partial frequency reuse, to reduce the number of packet collisions in broadcast transmission. [PERSON]. [22] utilized the fuzzy interference system theory and designed the corresponding matching statement table to dynamically determine the resource keep probability in mode 4 to improve the packet delivery ratio performance. In [23; 24; 25], deep reinforcement learning was introduced into the resource allocation in mode 3 to improve the latency performance, and the corresponding action space, the state space, and the reward function were designed for different communication scenarios. As pointed out above, most previous studies in the spectrum resource allocation method design generally adopt the Hungarian algorithm and the machine learning method. The Hungarian algorithm stipulates that the number of VUEs and CUEs must be equal, making it hard in practice. In the machine learning method for mode 3, the management and optimization of radio resources generally involve multiple vehicles with various QoS requirements, leading to an explosive growth in spatial dimensionality and significant increases in computation complexity. Fortunately, those deficiencies could be tackled with game theory. Matching game theory is a powerful method in game theory that can well describe the interactions and mutually beneficial relations between different participant sets. The preference profiles defined in matching game theory can handle the multiple QoS requirements of the vehicles, and there are no restrictions on the number of agents from two different participant sets. Moreover, the corresponding matching algorithm possesses a low computational complexity and thus can respond quickly to the highly dynamic vehicular network structure. Although the matching game theory has been applied in [26; 27; 28], these studies do not consider the efficiency and fairness trade-offs in the resource allocation in mode 3.