esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Algorithms to Improve the Data Write Rate
### Data Write Performance Tests Using Binary Formats
#### 3.2.3 Approach 3. Parallel Writing  
Several tests were conducted to verify whether writing data in parallel into multiple files improves write performance. Accordingly, the 5 GB data pack was split into multiple equal-sized blocks and then written into separate files concurrently using parallel processing. Each data block is written into a file by a separated thread. The buffer size and the file size were set equally as 8, 16, 32, 64, 128, 256, and 512 MB, as performed in Approach  
Figure 6: Data write performance of tests using a self-created buffer. (**a**) Buffer size = 8 MB, (**b**) buffer size = 32 MB, (**c**) buffer size = 64 MB, (**d**) buffer size = 128 MB, (**e**) buffer size = 256 MB, and (**f**) buffer size = 512 MB.  
2. The results, as illustrated in Figure 7, showed that file size configurations of 128 and 256 MB resulted in average write rates of 110 and 120 MB/s, respectively. These rates were lower than the rates of 157 and 160 MB/s achieved with sequential file writes, as described in Approach 2. This indicates that writing multiple files concurrently is less efficient than writing each file sequentially. Similar results were observed when testing smaller file sizes, such as 8, 16, 32, and 64 MB.
## 3 Application Fields of Smart Manufacturing and Tactile Internet Powered by 5G
### Healthcare  
As regards healthcare, it is easier to comprehend the context by taking into account the Japanese initiative entitled \"Society 5.0\". In this initiative, it becomes apparent that the contribution of advanced HMI and the progress made so far during the last industrial revolutions (including Industry 4.0) extends beyond the limits of industry, up to the society we live in, and the companies operate in as well. Further to that, the ultimate goal of engineers is to discover new ways to address human issues. Taking into account the previous statement, healthcare is a crucial part of Industry 5.0. A distinctive example is the remote operations (also known as tele-operations), which require the cooperation of humans and robotic manipulators in real-time. Further to that, ultra-fast, highly reliable, and with near-zero latency network connections are required in order to achieve the seamless cooperation between humans and machines.  
Tele-diagnosis, tele-surgery, and tele-rehabilitation are just a few examples of the tactile internet's potential applications in healthcare. Medicinal learning could be accessible from anywhere and at any time, regardless of the doctor's location, thanks to advanced tele-demonstrative tools. The doctor will control a tele-robot at the patient's location, which will provide audio and/or visual data as well as haptic feedback. Tele-surgery applications are linked to the same specialized guideline. Tele-rehabilitation methods can be used to help patients guide and control their movements from a distance. To enable the  
Figure 13: Single and multiple user VR use cases: requirements and enablers [56].  
transmission of tele-medical innovation, high devotion and great precision are required in all tactile internet-based healthcare advances [58].
## 5 Numerical Results and Comparison
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Order**} & \multicolumn{1}{c}{**1 BW**} & \multicolumn{1}{c}{**1 FW**} & \multicolumn{1}{c}{**2 BW**} & \multicolumn{1}{c}{**2 FW**} & \multicolumn{1}{c}{**3 BW**} & \multicolumn{1}{c}{**3 FW**} & \multicolumn{1}{c}{**4 BW**} & \multicolumn{1}{c}{**4 FW**} \\ \hline
22 & 0.0392 & 0.150 & 0.293 & 0.330 & 0.556 & 0.822 & 1.097 & 1.127 \\
33 & 0.0383 & 0.148 & 0.317 & 0.326 & 0.552 & 0.820 & 1.102 & 1.138 \\
44 & 0.0380 & 0.147 & 0.324 & 0.327 & 0.550 & 0.816 & 1.096 & 1.153 \\
66 & 0.0377 & 0.146 & 0.322 & 0.335 & 0.547 & 0.812 & 1.091 & 1.170 \\
88 & 0.0376 & 0.146 & 0.321 & 0.338 & 0.546 & 0.810 & 1.088 & 1.179 \\
99 & 0.0354 & 0.130 & 0.291 & 0.347 & 0.472 & 0.712 & 1.032 & 1.204 \\
110 & 0.0365 & 0.139 & 0.308 & 0.344 & 0.507 & 0.769 & 1.062 & 1.206 \\
121 & 0.0370 & 0.144 & 0.316 & 0.344 & 0.538 & 0.797 & 1.070 & 1.194 \\
242 & 0.0373 & 0.145 & 0.319 & 0.344 & 0.544 & 0.806 & 1.082 & 1.195 \\
484 & 0.0373 & 0.145 & 0.319 & 0.345 & 0.543 & 0.805 & 1.081 & 1.199 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Natural frequencies via spatial discretization (\(h=100\) Nms). \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{1}{c}{**Order**} & \multicolumn{1}{c}{**1**} & \multicolumn{1}{c}{**2**} & \multicolumn{1}{c}{**3**} & \multicolumn{1}{c}{**4**} \\ \hline
22 & 0.117 & 0.469 & 1.061 & 1.897 \\
33 & 0.118 & 0.472 & 1.064 & 1.895 \\
44 & 0.118 & 0.474 & 1.066 & 1.897 \\
66 & 0.119 & 0.475 & 1.069 & 1.899 \\
88 & 0.119 & 0.476 & 1.070 & 1.900 \\
99 & 0.118 & 0.473 & 1.063 & 1.886 \\
110 & 0.119 & 0.475 & 1.067 & 1.894 \\
121 & 0.119 & 0.476 & 1.071 & 1.901 \\
242 & 0.119 & 0.477 & 1.072 & 1.903 \\
484 & 0.119 & 0.477 & 1.072 & 1.903 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Natural frequencies via spatial discretization (\(h=0\)). \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Order**} & \multicolumn{1}{c}{**1 BW**} & \multicolumn{1}{c}{**1 FW**} & \multicolumn{1}{c}{**2 BW**} & \multicolumn{1}{c}{**2 FW**} & \multicolumn{1}{c}{**3 BW**} & \multicolumn{1}{c}{**3 FW**} & \multicolumn{1}{c}{**4 BW**} & \multicolumn{1}{c}{**4 FW**} \\ \hline
22 & 0.0145 & 0.0548 & 0.123 & 0.202 & 0.303 & 0.396 & 0.495 & 0.506 \\
33 & 0.0140 & 0.0538 & 0.119 & 0.199 & 0.295 & 0.388 & 0.481 & 0.553 \\
44 & 0.0139 & 0.0534 & 0.118 & 0.197 & 0.292 & 0.384 & 0.476 & 0.547 \\
66 & 0.0138 & 0.0530 & 0.117 & 0.196 & 0.290 & 0.381 & 0.471 & 0.541 \\
88 & 0.0137 & 0.0529 & 0.117 & 0.195 & 0.288 & 0.380 & 0.469 & 0.539 \\
99 & 0.0128 & 0.0464 & 0.105 & 0.168 & 0.250 & 0.360 & 0.420 & 0.498 \\
110 & 0.0133 & 0.0499 & 0.112 & 0.180 & 0.272 & 0.370 & 0.440 & 0.517 \\
121 & 0.0134 & 0.0519 & 0.114 & 0.192 & 0.283 & 0.373 & 0.460 & 0.529 \\
242 & 0.0136 & 0.0526 & 0.116 & 0.194 & 0.286 & 0.377 & 0.465 & 0.535 \\
484 & 0.0136 & 0.0525 & 0.115 & 0.194 & 0.286 & 0.377 & 0.464 & 0.534 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Natural frequencies via spatial discretization (\(h\) = 300 Nms).
## 3 Chidroid Architecture
### Configuration of Endpoints  
A web API endpoint is a specific URL or location on a web server that provides access to a web API. Chidroid supports the distribution of raw data to remote HTTP or HTTPS endpoints. Alternatively, another Chidroid application can act as a gateway with a dedicated listener and receive and forward whatever data it receives to the specified final endpoint. Next, an example of configuring a remote output is presented (Listing 3).  
```
1[outputs.A14 HEALTHSEC]
2 Urls=[\"METAGOs://siem.ALHHEALTHSEC.pdmfc.com/dynamic-ports\(\leftarrow\)/10000/:80/~]
```  
Listing 3: Configuration of the outputs.  
In Listing 3 a new output named \"AI4 HEALTHSEC\" is defined and specifies that it will send data to a single endpoint using an HTTP Post over HTTPS, to a host where an SIEM is deployed. As an example, in Listing 4, the encoded JSON output is presented as an event retrieved from Logcat (see Section 3.4 for more details on Logcat).  
```
1[
2\"header\":{
3\"agentId\":\"55 bb84e8-b2 ee-4 af4-94f3-435826e14f98\",
4\"host\":\"Androids\",
5\"os\":\"Android\",
6\"architecture\":\"arm64\",7\"source\": \"script://Logcat\"
8\"sourceype\": \"logcat\"
9\"format\": \"METAGO\"
10\"timefield\": \"timestamp\",
11\"timerformat\": \"MMDDhh:mm:ss.SSS\"
12\"timezone\": \"Europe/Lisbon\"
13],
14\"events\": [
15{
16\"timestamp\": \"12-0517:48:28.215\",
17\"pid\": 26458,
18\"opid\": 26458,
19\"type\": \"V\",
20\"AppName\": \"GraphicsEnvironment\",
21\"Message\": \"ANGLE Developeroptionfor'com.google.android.apps.messaging'setto: 'default'
22}
23]
24}
```
# Intelligent Handover Decision-Making for Vehicle-to-Everything (V2X) 5G Networks
## 4 Performance Evaluation
### Result Analysis
#### 4.3.4 Performance Comparison in Terms of Energy Consumption  
Energy efficiency is a key concern in high-mobility situations, as recurrent handovers increase power use due to unnecessary signaling. ACHQ showed the highest energy consumption because all SCs participated in the evaluation of the conditional HO decision. Frequent handovers require extra signaling and power, resulting in high energy consumption in ACHQ, as illustrated in Figure 14. As the UE velocity increases, the context-aware feature can enable energy conservation, resulting in lower energy consumption in the CAP (TOPSIS-NON QL learning) and IDH-V2X algorithms. Moreover, when the network is dense, both significantly enhance energy efficiency by reducing unnecessary HOs, because it ensures that only the SCs within the UE movement can participate in the handover decision, limiting the number of scanned SCS, and therefore, saving energy. The most energy-efficient mechanism is IHD-V2X, which optimizes HO execution, lessening the overall signaling expenditure and constant energy usage at all network density levels. It enhances energy usage even more, resulting in up to 6% less energy consumption compared to CAP and ACHQ. This is mainly useful for relay applications, where UEs act as mediators and need to preserve battery life while guaranteeing connectivity. The energy consumption increases preoperatively to approximately  
Figure 12: Comparative analysis of packet losses and latency vs. various UE mobilities.  
18%, but is minimal compared to ACHO, which reaches an almost 76% peak when the UE mobility and density increase steadily.  
Based on the above simulation results, it is concluded that the proposed algorithm significantly enhances the performance of the handover process by fulfilling the user requirements in 5G V2X networks. The findings indicate that UE mobility and SC density are critical factors that affect HO effectiveness and network performance. As the velocity and density increase, handover occurrence increases, resulting in higher failure rates, higher packet loss, increased latency, and unnecessary energy consumption. A handover optimization strategy was considered.
# A Multi-Scale Graph Attention-Based Transformer for Occluded Person Re-Identification
## 4 Experiments and Results  
### Datasets  
Experiments were conducted across three occlusion scenario datasets in order to assess the performance of our proposed methodology. Our primary objective was to address the challenge of occluded person re-identification; hence, the experiments utilized the Occluded-Duke [27], OccludedREID [28], and Partial-ReID [29] datasets. The Mean Average Precision (mAP) and Rank-1 accuracy were employed as standard evaluation metrics, consistent with prevailing practices in ReID research. Details about the dataset we used can be found in Table 1.  
Figure 2: The structure of our proposed multi-scale graph attention (MSCA) module is as follows: features at different scales are extracted through three distinct branches. These features are projected to a common dimension via linear layers and subsequently fused. In our model, features are fed through the MSGA module multiple times in order to extract higher-order characteristics of occluded persons.  
The Occluded-Duke dataset was constructed based on the original DukeMTMC-reID dataset, yielding Occluded-DukeMTMC, which encompasses 15,618 training images, 17,661 gallery images, and 2210 occluded query images, making it the largest occluded ReID dataset to date. DukeMTMC-reID serves as a widely adopted benchmark for pedestrian re-identification, featuring a substantial collection of pedestrian images captured by surveillance cameras, ideal for assessing the performance of cross-camera tracking algorithms. The Occluded-Duke dataset was meticulously derived from DukeMTMC-reID by selecting instances with occlusions, aiming to escalate the difficulty of re-identification tasks, particularly when parts of pedestrians' bodies are obscured by objects such as pillars, backpacks, or other pedestrians.  
The Occluded-REID dataset is specifically designed for occluded person re-identification, consisting of 2000 images of 200 distinct pedestrians captured by a moving camera. Each identity is represented by five full-body gallery images and five query images with different viewpoints and varying degrees of severe occlusion. All images have been uniformly resized to a dimension of 128 \(\times\) 64, facilitating studies on the impact of occlusion on pedestrian recognition.  
The Partial-ReID dataset comprises 600 images of 60 pedestrians, with each pedestrian represented by 5 partial images and 5 full-body images. Using the visible portions, the images are manually cropped to create new partial images. The images in the dataset were collected across a university campus under various viewpoints, backgrounds, and degrees of occlusion.  
Occluded-Duke is a subdataset of DukeMTMC-reID, retaining the occluded images in the source dataset. We randomly divided the training dataset into training part and validation part at a ratio of 9:1. In addition, the Occluded-ReID and Partial-ReID datasets only provide the testing set, so we trained our model on Market-1501 and reused these two datasets for testing.
## 5 Our Proposal: Hybrid Architecture  
Taking into account all the characteristics, advantages, and disadvantages of each of the tools presented in the previous section, we propose a hybrid architecture in Figure 9 that aims to get the best out of each of the tools and, thus, build an ecosystem capable of supporting the processing and visualization of information in real-time.  
The first part of this architecture presents the data sources that can be of varied formats. Following our proposal, these data sources connect directly with Solace. The recommendation is to use the APIs developed by the company itself, as well as open protocols. This way, the architecture is not being restricted to using any technology.  
In 2020, Solace launched a communication that discourages the use of Kafka. Since Solace PubSub+ distinguishes itself both in event broadcasting and event management, other technologies became dispensable in Solace's perspective. However, this is only true for cases of transactional and operational use. Kafka still stands out in the event flow for storage and management of analytic use cases.  
As the goal is to have an architecture as robust as possible, encompassing as many use cases as possible, the inclusion of Kafka makes sense. However, this new version of Solace PubSub+ is still in beta and is not as stable as desired. Moreover, Kafka is used by several organizations that have made investments to employ these technologies in production. With the architecture proposed here, this technology can not only be reused, but it also creates synergy between all components. Furthermore, for those who prefer simpler architectures and are starting their investment in these areas, the usage of Kafka according to this proposal is optional.  
Moreover, the proposal is, in this case of including Kafka, to use connectors designed by Solace allowing the data connection between Solace and Kafka. Solace allows a wide variety of standard protocols to connect directly to Kafka (MQTT, AMQP, REST, WebSocket, and JMS).  
The next step to data being in Solace and/or Kafka is to pass it to Apache Spark. That way, we are giving users the choice of which technologies they want to subscribe to  
Figure 9: Proposed hybrid architecture.  
the data. With the data available in Apache Spark, data analysis will then be executed in parallel and distributed.  
Finally, there will be two data access zones on Tableau (Data Analysts) and Power BI (Common User--Employees). This way, costs can be reduced because Tableau is the most expensive software of the proposed architecture, as well as helping employees, since Power BI is very customizable and easy to understand and consult.  
The presented architecture is innovative, as it not only represents a well-researched pipeline option for information processing, but adds hybridization on Kafka. Since Solace no longer supports the connection between Kafka, with the proposed architecture, users have the option of not divesting from their investment. Furthermore, as shown in Figure 9, the usage of Kafka is optional, with all tasks intended for Kafka being also supported by Solace.
## 6 Results and Discussion
### Simulation Results and Disscusion
There is an intersection between JODRA-RDLMC and OCO. When \(F_{\rm B}\leq 45\) Gcycles/s, the delay of JODRA-PGLMC is closer to that of JODRA-RDLMC. When \(F_{\rm B}>45\) Gcycles/s, the delay of JODRA-PGLMC is closer to that of OCO. The reason is that when \(F_{\rm B}\) is few, the gain from computation offloading is relatively limited. If a UE makes a task offloading decision randomly, local computing may be selected, which can obtain more gain. When \(F_{\rm B}\) is many, computation offloading can provide more gain for UEs. It can be seen that when \(F_{\rm B}\) is abundant, the delay of OCO is very close to that of JODRA-PGLMC. This indicates that rich computing resources of the BS make more UEs tend to offload tasks. The delay of  
Figure 5: Delay vs. data size of each task. JODRA-PGLMC is much lower than that of JDORA-RDLMC, and the difference increases gradually, which proves that potential game is effective in making the task offloading decision. Comparing with JODRA-PGLMNC's delay, JODRA-PGLMC's delay is lower, and the difference increases with the increase of \(F_{\text{B}}\), which is due to the satellite-terrestrial cooperation. \(F_{\text{B}}\) affects the gain for UEs provided by satellite-terrestrial cooperation directly. The difference between JODRA-PGLMC's delay and OLC's delay is almost always the largest, which proves that the proposed scheme is quite beneficial to UEs.
## 4 Results and Discussion  
### Spectral Signature of Vegetated Structures  
Figure 4 shows the mean \(R_{\mathrm{rs}}\) spectra of different characteristic substrate types, collected at the study site on 21 July 2016. The spectral signature of the two _Spartina_ species is very clearly distinguishable from other benthic habitat types. This difference is particularly obvious for dense and green _Spartina_ meadows, without biofilm or sediment. Their \(R_{\mathrm{rs}}\) spectra display a narrow peak center at around 550 nm and a sharp increase in the signal between the red and NIR bands, which is associated with high \(R_{\mathrm{rs}}\) values over 780 nm. Further, _Spartina anglica_ and _Spartina maritima_ are also spectrally distinguishable. In green wavelengths, _Spartina anglica_ shows a more pronounced peak than _Spartina maritima_, while in the red and NIR bands, _Spartina anglica_ shows higher \(R_{\mathrm{rs}}\) values than _Spartina maritima_. For instance, the \(R_{\mathrm{rs}}\) value is 0.013 sr\({}^{-1}\) and 0.004 sr\({}^{-1}\) at 650 nm, and 0.071 sr\({}^{-1}\), and 0.042 sr\({}^{-1}\) at 840 nm, respectively (Table 2). The NDVI, obtained from in situ Rrs, also indicates a significant difference between _Spartina anglica_ and _Spartina maritima_, with a value of \(0.69\pm 0.02\) and \(0.83\pm 0.03\), respectively. Figure 3: Flow chart of the image processing steps. However, field measurements show a high variability of the spectral signatures of both _Spartina_ species, depending on phenology, vegetation cover, biofilm and sediment deposition. For instance, the algae deposition on _Spartina maritima_ causes a significant decrease in NDVI (0.54 instead of 0.83) due to an increased signal in the red and a decreased signal in the NIR. On the other hand, the sediment deposition on _Spartina__anglica_ shows a negligible impact on the radiometric signal in the red, but a strong impact in the NIR, which is responsible for a moderate decrease in NDVI (from 0.69 to 0.61). Figure 5 provides the \(R_{\text{rs}}\) values in the green, red and NIR channels for the MS Pleiades images, acquired on 3 August 2016. The images zoom in on the _Spartina__anglica_ and _maritima_ meadows, sampled during the field campaign conducted on 21 July 2016. MS images exhibit a high intra-patch variability of their spectral signatures, as previously observed using field observations. This variability is directly related to the spatial heterogeneity of the ground cover percentage in the meadows of _Spartina__species_. In order to compare field and satellite remote sensing reflectances, we computed the field SRF-corrected \(R_{\text{rs}}\) from Eq.1 (Table 2). A general smoothing effect on \(R_{\text{rs}\_SRF}\) due to the wide Pleiades spectral bands can be noted. However, small differences were recorded between \(R_{\text{rs}\_SRF}\) and \(R_{\text{rs}}\). The lowest and highest relative differences were found in the red band for _Spartina__anglica_ (7%) and _Spartina__maritima_ (22%), respectively.
# FAS-Res2 net: An Improved Res2 net-Based Script Identification Method for Natural Scenes
## 4 Experiments
### Results of the Method in This Paper
#### 4.3.7 Error Analysis  
Although this paper uses an innovative script identification method, there are still some scripts that cannot be identified correctly. As shown in Figure 8, for the first and sixth images (id:327_1 and id:188_1), the identification results are English and Chinese, respectively, while the groundtruths are Greek and Japanese, respectively. The identification error is because there are no special characters of Greek and Japanese in the image, only English letters and Chinese characters. The distinction between the two scripts cannot be made, so this task is extremely difficult. For the second image (id:139_2), due to the irregular orientation of the script symbols and the short length of the script, the feature information is biased towards Japanese, and Arabic is misidentified as a Japanese script. The third and fourth images are both due to the complex background, and the extracted feature information cannot be matched well, resulting in identification errors. The identification of the fifth image as English should be the correct identification result, but it was wrongly marked as the Russian label when making the dataset.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Method** & **16** & **32** & **64** & **128** \\ \hline Res2 Net50 + FPN + ASFF + Swin & 94.3 & 94.7 & 94.5 & 94.6 \\ Res2 Net101 + FPN + ASFF + Swin & 94.1 & 94.4 & 94.4 & 94.3 \\ \hline \end{tabular}
\end{table}
Table 5: The identification results of the improved method based on Res2 Net50 and Res2 Net101 under different batch sizes (%).  
As shown in Table 6, it shows the gradual change from the basic Res2 Net network to the added modules, from the identification error to the correct one. These two script images are from the public dataset SIW-13 test set. The first script image is Arabic, but because its background is complex and runs through the entire image, neither the pure Res2 Net or the FPN structure can extract effective features for correct identification. After adding the Adaptive Spatial Feature Fusion (ASFF) module, the effective features are weighted, and the script is identified correctly. The groundtruth of the second image is Japanese. It can be seen that the script in the image is composed of some Japanese characters and Chinese characters. Without the transformer module in the network, the identified script is Chinese. After the block extracts the global feature in-formation of the image, it is correctly identified as Japanese, thus verifying the effectiveness of each module.