esa-sceva/satcom-chunk-collection huggingface dataset:
# Navigating a Service Robot for Indoor Complex Environments
## 2 Method
### ANFIS-Based Fuzzy System
#### 2.5.2 Combining Sensors  
One drawback using the depth map is the problem that if the obstacles are too low and too close to the robot, then they are undetectable by using the depth map alone. In this case, the sonars will be activated as aid in obstacle-avoidance. However, judging how much weight should be given to the depth map or the sonar map in order to yield the optimal map so as to find the largest available gap is a problem. It requires solving the weights in Equation (1) at each instance of decision making:  
\[\text{(Result Map)}=\text{W}_{\text{d}}\ ^{*}\text{(Depth Map)}+\text{W}_{\text{s}}\ ^{*}\text{(Normalized Sonar Map)} \tag{1}\]  
The field of research of combining sonar map with other sensors, such as depth map, is still an area that requires exploration [21,22]. The sonar map can only be used for obstacles that are close, so using the sonar map alone may cause the robot to spin continuously in order to acquire more information. Because of this, our research uses the depth map to move the robot until it moves too close to the gap or obstacles and determines that additional information would be required, then it activates the sonar system in order to acquire the sonar map. There are eight sonar sensors installed in a semi-circular fashion, as illustrated in Figure 10.  
However, the leftmost and rightmost sensors are not used because they seem to cause erroneous decisions. The sonar signals are first normalized between 0 and 1.0, and it is determined that signal strength less than 0.4 indicates that obstacles are close, while greater than 0.6 indicates that obstacles are far away. If each of the six sonar signals are used as individual fuzzy inputs, then the system  
Figure 10: Placement of sonar sensors near our robot’s base. Figure 9: An example of fuzzy-rules-based decision. would become overly complicated. Therefore, in order to reduce the number of fuzzy rules, the signals for the left three sensors are averaged into a single input, and the signals for the right three sensors are averaged into another single input. This would effectively reduce the number of fuzzy rules. We designed a total of 8 fuzzy rules for combining the sensors. They are listed in Table 3.  
An example of using these rules as shown in Figure 11, where gap width is around the robot's width, the normalized and averaged value for left sonar is 0.259, and the normalized and averaged value for right sonar is 0.729. The eight rows in Figure 11 represents the inputs and outputs for the 8 rules. The red lines are symbolic representations of the input values. Figure 12 shows the paths the robot takes when decisions are made based on using fuzzy rules to combine sonar map and depth map versus using only the sonar map alone. In situations like this, we can see the advantage of combining the inputs of sensors. Figure 11: An example of using fuzzy rules to combine sensors input.
# Security Risk Analysis of LoRaWAN and Future Directions
## 5 Security Threat and Risk Assessment of LoRaWAN v1.1
### Risk Assessment Methodology
#### 5.1.1 Likelihood Assessment  
Three discrete levels of categorization are provided for this assessment method depending on the likelihood (probability) of a threat: _unlikely_, _possible_, _likely_. Following two factors are used to evaluate the likelihood assessment of threats:(a) Motivation level  
This represents the motivation level of an attacker to perform an attack. Depends on the stakes of the attack result (opportunity) and also related to the desperation degree of the attacker (greed). For example, if the result of an attack would reveal more benefits than the cost of the attack itself, then this would create motivation for the attacker. Motivation level is described by three levels: _low_, _medium_, _high_.  
(b) Technical difficulty  
This represents the bounds and challenges related to performing an attack. For example, replay attacks are quite easy to perform, requires recording and replaying the same message transmitted by others. Therefore, its technical difficulty is categorized as \"solvable\". However, introducing an authentic end-device (called \"rogue end-device\") to the network requires more advanced skills and categorized as \"strong\". Technical difficulty is described by three levels: _none_, _solvable_, _strong_.  
(c) Likelihood of an attack  
Based on these risk factors (motivation level and technical difficulty), likelihood assessment chart is created as shown in Figure 3. For example, if an attack is easy to perform (difficulty of none) and if the stakes are good (motivation of high), then the likelihood of the attack is \"likely\".
# Design and Application Space Exploration of a Domain-Specific Accelerator System
## 5 Prototype Verification Environment  
The validation of the proposed accelerator is split into two parts: FPGA prototype verification, and chip testing.  
Before tape-out, we build an SoC prototype system based on FPGA not just for algorithm verification but to verify the functional correctness with real peripherals and demonstrate that the software runs end to end, and to create an emulation platform for running long workloads.  
Figure 8 shows the system block diagram of the SoC. It is implemented using Xilinx ISE 14.5 targeting a Xilinx Virtex6-760, and the working frequency is 25 MHz. The RASP engine connects through a 256 bit AXI bus with other components: a Gigabit Media Access Controller (GMAC) for data package switching, a Nor Flash chip to store compiled program bit file, and a universal asynchronous receiver-transmitter (UART) for asynchronous serial communication. The entire SoC consumes 346,192 (73%) LUTs and 560 (78%) RAMB36E1, respectively.  
From the system block diagram, we can learn that the proposed accelerator offers good design-reuse opportunities because it is mostly independent of the design of the CPU and the rest of the system. To couple the RASP with different systems, it is sufficient to have a thin hardware wrapper that interfaces its configuration registers and DMA controller with the on-chip interconnect.  
We have also implemented the prototype chip with 40 nm CMOS technology, which has an area of 19.2 mm\({}^{2}\) and a maximum clock frequency of 1 GHz. The chip layout is shown in Figure 9; it is integrated with two DSP cores, and the area outlined with a red line is the accelerator subsystem. Table 2 lists a summary of the RASP chip specifications. Notice that, for the same workload strength, the TI TMS320C6672 consumes 5453 mW with an operating frequency of 1 GHz.  
Figure 8: System block diagram of SoC with single RASP.  
Figure 9: Chip layout of the SoC.
# Hybrid Path Planning for Efficient Data Collection in UAV-Aided WSNs for Emergency Applications
## 5 Performance Evaluation
### Simulation Environment  
In our simulation, SNs are randomly distributed in an area measuring 500 m \(\times\) 500 m. Among the total SNs deployed, 10% of them are selected as CHs responsible for transmitting data to the UAV. We investigated an efficient MAC protocol for UAV-to-CH communication in our previous study [35], and this is used in the current study. The UAV uses a hybrid MAC protocol for UAV-to-CH communication. The UAV has a roadmap of its flight path, provided by the PRM algorithm before flying. The map provided by the PRM is the shortest and free of static and dynamic obstacles, and threats from prohibited areas. It follows the shortest path with a predefined height and speed until it encounters the complex environment's obstacle and threat. In this case, the UAV uses the ABC to avoid threats and seeks the shortest, safest, and the smoothest path to accomplish its mission. For an efficient operation of the protocol, we optimized the UAV's height and velocity such that every CH has access to the UAV at least once during the flight time.  
We have considered some key metrics to evaluate the proposed MAC's performance and compared it with existing protocols. The performance metrics used for the evaluation are as follows.
# Multi-Grained Similarity Preserving and Updating for Unsupervised Cross-Modal Hashing
## 3 Methodology
### MGSP for Hash Code Learning
#### 3.5.3 Similarity Consistency Reconstruction  
The similarity consistency preserving should not only be reflected in cross-modal features, but more importantly, in hash codes. Deep feature reconstruction, namely reducing the heterogeneity between original features and reconstructed features from continuous hash codes, has been verified to be an effective technique. Current literatures indicate that, two different reconstruction strategies, i.e., intra-modal [28] and inter-modal reconstruction [37], are developed for similarity consistency preserving. In this work, we integrate inter-modal reconstruction into our method to narrow cross-modal gap. Specifically, the continuous hash codes \(\mathbf{H}^{v}\) and \(\mathbf{H}^{t}\) are generated by a couple of two-layer fully-connected networks from \(\mathbf{P}^{v}\) and \(\mathbf{P}^{t}\), which then are fed into two re-constructors to output reconstructed features. Accordingly, inspired by [37], a similarity consistency reconstruction loss function \(\mathcal{L}_{rec}\) is introduced to preserve the similarity structure information into hash codes:  
\[\mathcal{L}_{rec}=\left\|\mathbf{F}^{v}-\mathbf{F}^{v}\right\|_{F}^{2}+\left\|\hat\mathbf{{F}}-\mathbf{F}^{t}\right\|_{F}^{2}, \tag{14}\]  
where \(\hat{\mathbf{F}}^{v}\) and \(\hat{\mathbf{F}}^{t}\) denote the reconstructed feature representations.
# Digestate as Sustainable Nutrient Source for Microalgae--Challenges and Prospects
## 7 Use of Microalgae Biomass
### Applications in the Food and Feed Industry  
Microalgae such as _Chlorella, Arthospira, Haematococcus_, and _Dunaliella_ are commonly used in foods, e.g., as human nutritional supplements, due to their beneficial properties (e.g., antiviral, antitumor, anti-inflammatory) [86]. They are a rich source of proteins, carbohydrates, healthy lipids, and antioxidants such as pigments [86].  
Microalgal biomass produced on digestate has high potential to be used in aquaculture. Live microalgae can be fed directly to fish larvae, mollusks, or crustaceans, or to rotifers that can be a live feed for fish or crustacean larvae--as aimed for in the Algae4 Fish project (Interreg ATC221). Another possibility is to use refrigerated or frozen pastes of microalgae (_Chlorella, Nannochloropsis, Phaeodactylum_), which are fed to herbivous fish as well as larvae of shrimps, oysters, sea bream, bivalves, abalone, crab, or zooplankton. Spray-dried, disintegrated, or freeze-dried products are available as intact cells or with broken cell walls, fed to zooplankton, algae grazers, or mollusks, or mixed (1-5%) in expensive commercial feed preparations.  
However, new products entering the food chain must meet requirements concerning public health, hygiene, and safety [78]. In the European Union, the General Food Law (EC 178/2002 [87]) sets the basic legislative framework for food and feed production [86]. Regulations such as Hygiene Regulations (EC 852/2004 [88], EC 853/2004 [89]) and the Legislation about Novel Food (EC 258/97 [90]) must be followed when producing microalgae for nutritional applications [78; 86]. EC 767/2009 [91] comprises specific regulations for new ingredients for feed purposes [78; 86]. The level of contaminants in microalgal biomass must be tested before it is used as animal feed (2002/32/EC [92]) [78; 86].
# Particle Swarm Optimisation in Practice: Multiple Applications in a Digital Microscope System
## 6 Image Distortion Correction  
In the digital microscope used a simplified image distortion correction pipeline is shown in Figure 14.  
Digital microscopes are commonly used to measure objects being inspected. Typically, the system is calibrated by using a reference ruler, which is placed under the microscope and a reference measurement is made. From that reference measurement, a pixel to mm ratio is determined and this is used to make further measurements.  
This method relies on accurate spatial reproduction of the image; however, optics can introduce lens distortion errors due to non-linear magnification of lenses and the alignment of the sensor with the optics. In addition, the optics with the object being inspected can introduce linear affine and non-linear perspective errors. Figure 15 shows an example of a rectangular grid image that has perspective and lens distortions applied. As the two blue lines are the exact same length, it is evident that significant measurement errors occur if a fixed pixel to mm ratio is used to make measurements.  
Figure 14: Microscope image distortion processing pipeline.  
Figure 15: Image distortion errors.  
In theory, a pinhole camera produces a geometric perfect image of an object; however, in real optical systems lens manufactures have to balance many aberrations resulting in radial image distortion [46]. Radial distortions appear as barrel (Figure 16) or pincushion (Figure 17) distortions.  
Radial distortion can be modelled using a polynomial equation. For each pixel (x,y) in the distorted image, the correct position of where that pixel should be is given in Equations (17) and (18). \(R\) is the radius and \(r\) is the normalised distance of a pixel from the optical centre for a 1920 \(\times\) 1080 image and \(k_{1}\) to \(k_{n}\) are the polynomial coefficients.  
The optical centre of the camera may not be the centre of the image due to sensor and lens misalignment. In Figure 18 the red grid is an undistorted grid. The blue grid has barrel distortion and decentring in the x-plane. This is because decentring P1 and P2 are not equidistant from the image centre. If the image centre is used to calculate \(r\) in Equations (11)-(14), significant errors would be introduced; therefore, the optical centre position needs to be found.  
\[dx=x_{point}-x_{optical-centre} \tag{11}\]  
\[dy=y_{point}-y_{optical-centre} \tag{12}\]  
\[R=\sqrt{dx^{2}+dy^{2}} \tag{13}\]  
\[r=R/1100.1 \tag{14}\]  
\[x_{scale}=1+k_{1}r^{2}+k_{2}r^{4}+k_{3}r^{6}... \tag{15}\]  
\[y_{scale}=1+k_{1}r^{2}+k_{2}r^{4}+k_{3}r^{6}... \tag{16}\]  
\[x_{\text{corrected}}=x_{\text{optical-centre}}+(x-x_{\text{optical-centre}}) \cdot\text{scale} \tag{17}\]  
\[y_{\text{corrected}}=y_{\text{optical-centre}}+(y-y_{\text{optical-centre}}) \cdot\text{scale} \tag{18}\]  
Figure 16: Barrel distortion.  
Figure 17: Pincushion distortion.
# A Multilayer Data Processing and Aggregating Fog-Based Framework for Latency-Sensitive IoT Services
## 5 Experiment and Evaluation
### Results
#### 5.4.4 Additional Results  
Figure 17 shows the average response time of a query using both the newest and the historical data when simulating 2400 clients. The vertical axis represents the time in milliseconds and the horizontal axis represents the round. The average response time of newest data query ranges from 29 to 187 ms, while the average response time of historical data query ranges from 134 to 345 ms.  
Figure 18 shows the average time delay of a query for the newest and the historical data (2400 clients were simulated). The vertical axis represents time in second and the horizontal axis represents the round. The time delay indicates the time between sending/receiving the request/response. The average delay time of newest data query ranges from 2 to 8 s, while the average delay time of historical data query ranges from 4 to 18 s.  
Figure 17: The response time of newest and historical data query (2400 clients’ connections).  
Figure 18: The delay time of newest and historical data query (2400 clients’ connections).  
Results show that the average response time and delay time of the experiment when using 2400 clients oscillate and are not stable. Findings, as detailed in the setup experiment in Section 5.2, indicate that the results remain stable with 400 to 2000 clients' connections and start to oscillate with 2400 clients' connections. To handle the increment in the clients' connections, additional fog nodes and gateways should be set and configured to simulate greater numbers of clients.
# Applications of Probabilistic Forecasting in Smart Grids: A Review
## 7 Further Probabilistic Forecasting and Applications
### Probabilistic Forecast of BESS SOC  
Authors of [133] developed a probabilistic forecasting model that analyzes the uncertainties of the battery energy storage system's state of the charge (BESS SOC) in providing the primary frequency control. The results were then used as inputs of the predictive optimization of BESS which schedules BESS for providing multiple flexibility services. In order to develop the probabilistic forecasts, the authors applied a multi-attention recurrent neural network (MARNN) to extract the most important contextual information in time-series forecasting. Afterward, they proposed a robust forecast, utilizing the combination of mixture density networks (MDNs) and Monte Carlo dropout (MCD). Finally, the proposed model was tested for different regulatory frameworks of primary frequency control services, using the frequency datasets of real-world power grids.
## 2 Related Work
### Run-Length Encoding  
Run-length encoding (RLE) [14] is a well-known, simple and quick form of data compression in which sequences of the same data value are found in many consecutive data elements. The RLE applications of this encoding are when the source information comprises long substrings of the same character or binary digit. For this reason, using RLE to compress the binary secret message is very applicable. For example, secret message \(00001011101\) will encoded into \(0(4)1(1)0(1)1(3)0(1)1(1)\).  
In 2006, [PERSON] _et al_. [15] proposed two new image steganographic methods using the run-length approach. There are two methods, one is BRL (hiding bitmap files by run-length), which focuses on binary images, and the other is GRL (hiding general data files by run-length). The major idea of these methods is to use RLE to increase the SMVQ method embedding capacity [16]. For binary images, [PERSON] and [PERSON] [17] also proposed run-length based steganography. Their proposed algorithm is dependent on their run length characteristics and characteristics values of the block and alters pixels of the cover image's embeddable blocks. Simultaneously, this scheme also enhances the security of the embedded data and the capacity of the embedding method. In addition, steganographic access control in data hiding using run-length encoding and modulo operations was proposed by [PERSON] _et al._[18] in 2011. In their scheme, a high capacity steganographic with access control modifies sharp bitstreams into smooth bit streams and embedded into the cover image. The modulo value is fixed in this scheme, meaning the embedding capacity is limited.  
Accordingly, RLE to increase embedding capacity for data hiding is important. In particular, it will increase the compression ratio when there are many continuities of ones and zeros in these binary images (black and white picture). We use binary images and also gray-scale images for the experiments in this paper. The results reveal a good compression ratio and improved embedding capacity.
## 5 Materials and Methods
### Performance Comparison Methodology
#### 5.6.2 Evaluation Metrics  
The performance of each variant is assessed using the following key metrics:  
* Total Distance Traveled: Measures the total route distance (km) to evaluate optimization effectiveness.
* Total Delivery Time: Assesses the overall time required to complete all deliveries.
* Computational Time: Records execution time (seconds) to compare algorithm efficiency.
* Population Diversity: Tracks genetic diversity over generations to analyze convergence behavior.
## 3 Methodology
### Dynamic Region Depth Estimation
#### 3.1.2 Depth Consistency and Scale Ambiguity  
A key challenge in dynamic region depth estimation is ensuring scale consistency between the predicted depths of moving objects and the surrounding static background. In monocular depth estimation, scale ambiguity arises because depth predictions are only correct up to an unknown scale factor. This problem is exacerbated in dynamic regions, where the relative motion of objects introduces further uncertainty. To address this issue, we introduce a DSA module (detailed in Section 3.4) that aligns the depth scales between the dynamic and static regions. The DSA module ensures that the depth predictions for moving objects are consistent with the depth scale of the static background, resolving the scale ambiguity between the two regions. This is critical for achieving accurate and seamless depth predictions in scenes with both static and dynamic elements. To train the dynamic depth estimation network, we use a combination of photometric reconstruction loss and depth smoothness loss. The photometric reconstruction loss is defined similarly to the loss used for static regions but is applied only to the dynamic object regions as indicated by the motion mask \(M_{dyn}\). The photometric loss for dynamic objects \(L_{photo}^{dyn}\) is computed by comparing the reference image \(I_{r}\) with the reconstructed image \(I_{r+s}^{dyn}(p)\) which is synthesized by warping the source image \(I_{s}\) based on the predicted depth map \(D_{dyn}\) for the dynamic regions:  
\[L_{photo}^{dyn}=\sum_{p\in M_{dyn}}\left|I_{r}(p)-I_{r-s}^{dyn}(p)\right| \tag{4}\]  
This equation uses \(\left|I_{r}(p)-I_{r-s}^{dyn}(p)\right|\) to denote the absolute difference between the intensity values of the reference image. This loss encourages the network to predict dynamic object depths that are consistent with the appearance of the object in the source and reference frames. To prevent overly noisy depth predictions in textureless areas, we also apply an edge-aware depth smoothness loss \(L_{smooth}^{dyn}\) to the dynamic depth map:  
\[L_{smooth}^{dyn}=\sum_{p\in M_{dyn}}\left|\
abla D_{dyn}(p)\right|e^{-\
abla I _{r}(p)} \tag{5}\]  
where \(\left|\
abla D_{dyn}(p)\right|\) is the magnitude of the gradient of the depth map D and \(\
abla I_{r}(p)\) is the magnitude of the gradient of the reference image \(I_{r}\), which is used as a weight to preserve the edges. The gradient \(\
abla I_{r}(p)\) is explicitly defined as a vector containing the partial derivatives of the intensity \(Ir\) with respect to \(x\) and \(y\). This term encourages smooth depth predictions in regions with low image gradients while preserving depth discontinuities at object boundaries.
## 3 System Model  
### Delay Model  
Each hop delay consists of four parts, 1 Queuing delay, the queuing delay can be neglected if unsaturated traffic is used; 2 Processing delay, Assuming that each node processes a constant length packet with equal delay; 3 propagation delay, it can be neglected compared with other parameters; 4 transmission delay, generally, with constant unsaturated traffic, the size of the packet remains unchanged, and the node does not need to wait. Therefore, the delay per hop can be simply estimated as a constant, and the end-to-end delay is proportional to the number of hops. Based on the data in reference [13], the delay of 802.11b DCF per hop is estimated. The delay \(T_{hop}\) of successful data transmission includes transmission delay \(T_{DATA}\) and response delay \(T_{ACK}\):  
\[T_{DATA}=T_{PRE}+T_{PHY}+\frac{8L_{MAC}+8L_{DATA}}{R_{DATA}} \tag{1}\]  
\[T_{RTS}=T_{PRE}+T_{PHY}+\frac{8L_{RTS}}{R_{CTRL}} \tag{2}\]  
RTS/CTS delay can be calculated by the following formula:  
\[T_{RTS}=T_{PRE}+T_{PHY}+\frac{8L_{RTS}}{R_{CTRL}} \tag{3}\]  
\[T_{CTS}=T_{PRE}+T_{PHY}+\frac{8L_{CTS}}{R_{CTRL}} \tag{4}\]\[T_{hop}=T_{RTS}+\tau+T_{SIFS}+T_{CTS}+\tau+T_{SIFS}+T_{DATA}+\tau+T_{SIFS}+T_{ ACK}+\tau+T_{DIFS}+\frac{CW_{min}\sigma}{3H} \tag{5}\]  
The symbols and values used are shown in Table 1. According to Table 1, the value of \(T_{hop}\) with RTS/CTS is calculated to be 2.68 Ms. If the end-to-end delay \(T_{QOS}\) is specified, the delay \(H_{QOS}\) per hop can be calculated by the following formula:  
\[H_{QOS}=\left\lfloor\frac{T_{QOS}}{T_{hop}}\right\rfloor \tag{6}\]